diff --git a/.github/workflows/build-pr-review-both-browsers.yml b/.github/workflows/build-pr-review-both-browsers.yml index 63ba0544..5ce47970 100644 --- a/.github/workflows/build-pr-review-both-browsers.yml +++ b/.github/workflows/build-pr-review-both-browsers.yml @@ -26,7 +26,7 @@ jobs: - name: "Setup Node version" uses: actions/setup-node@v3 with: - node-version: 20.8.1 + node-version: 20.9.0 - name: Get commit SHA shell: bash diff --git a/.github/workflows/deploy-prod.yml b/.github/workflows/deploy-prod.yml index 50dea8b7..dd76d6c7 100644 --- a/.github/workflows/deploy-prod.yml +++ b/.github/workflows/deploy-prod.yml @@ -8,9 +8,7 @@ jobs: changes: runs-on: ubuntu-latest outputs: - annotation-ui: ${{ steps.filter.outputs.annotation-ui }} uli-website: ${{ steps.filter.outputs.uli-website}} - ogbv-ml-rest: ${{ steps.filter.outputs.ogbv-ml-rest }} uli-server: ${{ steps.filter.outputs.uli-server }} steps: - uses: actions/checkout@v2 @@ -20,55 +18,11 @@ jobs: id: filter with: filters: | - annotation-ui: - - 'annotators/backend/**/*' - - 'annotators/annotation-ui/**/*' uli-website: - 'uli-website/**/*' - ogbv-ml-rest: - - 'ogbv-ml-rest/**/*' uli-server: - 'browser-extension/api-server/**/*' - annotation-ui: - runs-on: ubuntu-latest - needs: changes - if: ${{ needs.changes.outputs.annotation-ui == 'true' }} - steps: - - uses: actions/checkout@v2 - with: - ref: main - - name: Declare some variables - id: vars - shell: bash - run: | - echo "setting variables" - echo "::set-output name=sha_short::$(git rev-parse --short HEAD)" - cd annotators/annotation-ui - npm install --force - npm run build - - - name: Publish to Docker - uses: elgohr/Publish-Docker-Github-Action@master - with: - username: tattletech - password: ${{ secrets.DOCKER_PASSWORD }} - name: tattletech/ogbv_annotator - tags: ${{ steps.vars.outputs.sha_short }} - workdir: annotators/backend/ - - - name: deploy to cluster - uses: steebchen/kubectl@v2.0.0 - with: # defaults to latest kubectl binary version - config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} - command: set image --record deployment/annotation-ui annotation-ui=tattletech/ogbv_annotator:${{ steps.vars.outputs.sha_short }} - - name: verify deployment - uses: steebchen/kubectl@v2.0.0 - with: - config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} - version: v1.21.0 # specify kubectl binary version explicitly - command: rollout status deployment/annotation-ui - uli-website: runs-on: ubuntu-latest needs: changes @@ -102,44 +56,6 @@ jobs: SLACK_COLOR: "#020637" SLACK_ICON: https://tattle-media.s3.amazonaws.com/monogram-dark.svg - ogbv-ml-rest: - runs-on: ubuntu-latest - needs: changes - if: ${{ needs.changes.outputs.ogbv-ml-rest == 'true' }} - steps: - - name: Checkout - uses: actions/checkout@v2 - with: - ref: main - - - name: Declare some variables - id: vars - shell: bash - run: | - echo "setting variables" - echo "::set-output name=sha_short::$(git rev-parse --short HEAD)" - - - name: Build and Push to Docker - uses: elgohr/Publish-Docker-Github-Action@master - with: - username: tattletech - password: ${{ secrets.DOCKER_PASSWORD }} - name: tattletech/ogbv-ml-rest - tags: ${{ steps.vars.outputs.sha_short }} - workdir: ogbv-ml-rest - - # - name: deploy to cluster - # uses: steebchen/kubectl@v2.0.0 - # with: # defaults to latest kubectl binary version - # config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} - # command: set image --record deployment/ogbv-ml-rest ogbv-ml-rest=tattletech/ogbv-ml-rest:${{ steps.vars.outputs.sha_short }} - # - name: verify deployment - # uses: steebchen/kubectl@v2.0.0 - # with: - # config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} - # version: v1.21.0 # specify kubectl binary version explicitly - # command: rollout status deployment/ogbv-ml-rest - uli-server: runs-on: ubuntu-latest needs: changes @@ -174,4 +90,4 @@ jobs: with: config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} version: v1.21.0 # specify kubectl binary version explicitly - command: rollout status deployment/ogbv-plugin + command: rollout status deployment/ogbv-plugin \ No newline at end of file diff --git a/.github/workflows/deploy-staging.yml b/.github/workflows/deploy-staging.yml index f742ca44..1bbc3fbe 100644 --- a/.github/workflows/deploy-staging.yml +++ b/.github/workflows/deploy-staging.yml @@ -8,8 +8,6 @@ jobs: changes: runs-on: ubuntu-latest outputs: - annotation-ui: ${{ steps.filter.outputs.annotation-ui }} - ogbv-ml-rest: ${{ steps.filter.outputs.ogbv-ml-rest }} browser-extension-server: ${{ steps.filter.outputs.browser-extension-server }} steps: - uses: actions/checkout@v2 @@ -19,91 +17,9 @@ jobs: id: filter with: filters: | - annotation-ui: - - 'annotators/backend/**/*' - - 'annotators/annotation-ui/**/*' - ogbv-ml-rest: - - 'ogbv-ml-rest/**/*' browser-extension-server: - 'browser-extension/api-server/**/*' - annotation-ui: - runs-on: ubuntu-latest - needs: changes - if: ${{ needs.changes.outputs.annotation-ui == 'true' }} - steps: - - uses: actions/checkout@v2 - with: - ref: development - - name: Declare some variables - id: vars - shell: bash - run: | - echo "setting variables" - echo "::set-output name=sha_short::$(git rev-parse --short HEAD)" - cd annotators/annotation-ui - npm install - npm run build - - - name: Publish to Docker - uses: elgohr/Publish-Docker-Github-Action@master - with: - username: tattletech - password: ${{ secrets.DOCKER_PASSWORD }} - name: tattletech/ogbv_annotator - tags: ${{ steps.vars.outputs.sha_short }} - workdir: annotators/backend/ - - - name: deploy to cluster - uses: steebchen/kubectl@v2.0.0 - with: # defaults to latest kubectl binary version - config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} - command: set image --record deployment/annotation-ui-staging annotation-ui-staging=tattletech/ogbv_annotator:${{ steps.vars.outputs.sha_short }} - - name: verify deployment - uses: steebchen/kubectl@v2.0.0 - with: - config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} - version: v1.21.0 # specify kubectl binary version explicitly - command: rollout status deployment/annotation-ui-staging - - ogbv-ml-rest: - runs-on: ubuntu-latest - needs: changes - if: ${{ needs.changes.outputs.ogbv-ml-rest == 'true' }} - steps: - - name: Checkout - uses: actions/checkout@v2 - with: - ref: development - - - name: Declare some variables - id: vars - shell: bash - run: | - echo "setting variables" - echo "::set-output name=sha_short::$(git rev-parse --short HEAD)" - - - name: Build and Push to Docker - uses: elgohr/Publish-Docker-Github-Action@master - with: - username: tattletech - password: ${{ secrets.DOCKER_PASSWORD }} - name: tattletech/ogbv-ml-rest - tags: ${{ steps.vars.outputs.sha_short }} - workdir: ogbv-ml-rest - - # - name: deploy to cluster - # uses: steebchen/kubectl@v2.0.0 - # with: # defaults to latest kubectl binary version - # config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} - # command: set image --record deployment/ogbv-ml-rest ogbv-ml-rest=tattletech/ogbv-ml-rest:${{ steps.vars.outputs.sha_short }} - # - name: verify deployment - # uses: steebchen/kubectl@v2.0.0 - # with: - # config: ${{ secrets.PROD_KUBE_CONFIG_DATA }} - # version: v1.21.0 # specify kubectl binary version explicitly - # command: rollout status deployment/ogbv-ml-rest - browser-extension-server: runs-on: "ubuntu-latest" needs: "changes" diff --git a/annotators/.gitignore b/annotators/.gitignore deleted file mode 100644 index 5e446150..00000000 --- a/annotators/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -node_modules/ -temp/ \ No newline at end of file diff --git a/annotators/README.md b/annotators/README.md deleted file mode 100644 index 4a144a23..00000000 --- a/annotators/README.md +++ /dev/null @@ -1,38 +0,0 @@ -# Annotation UI -This tool was created to crowdsource annotations. -![Cover Image](docs/annotators-cover.png) - -## Features : -- User Management -- Supports Localization for English, Hindi and Tamil - -## Annotators Database Deployment Guide -- There is a `docker-compose.yml` file in here. - -#### First We Troubleshoot ports -- This command will list the ports next to their pid if they are running. -```bash -sudo lsof -i -P -n | grep -``` -- Check for this port 3306 (also 8000 & 3000) -- If any of these are already running, kill them with -```bash -sudo kill < pid > -``` - -### Now Bring up the `Adminer` MySQL DB: - -```bash -docker-compose up -``` -- Ctrl+C to stop it -- Dont forget to -```bash -docker-compose down -``` - -#### Open Adminer ( http://localhost:8080 ) -- login using username: tattle , password: tattle_pw, DB: ogbv_annotator_local - -#### Check out the README.md in `backend` to create tables in the database using `sequilize` and setup the backend for development. -#### Next check out the README.md in `annotator-ui` to setup the frontend for development . diff --git a/annotators/allocation_automation/allocation_spreadsheet_automate.py b/annotators/allocation_automation/allocation_spreadsheet_automate.py deleted file mode 100644 index 26b708a0..00000000 --- a/annotators/allocation_automation/allocation_spreadsheet_automate.py +++ /dev/null @@ -1,295 +0,0 @@ -from datetime import date -from gspread_formatting import * - -def upload_to_spreadsheet(data,EMAIL,annotator_id): - - service = gspread.service_account() - sheet_name = str(annotator_id) + str(date.today()) - sh = service.create(sheet_name) - - worksheets_count = len(list(sh.worksheets())) - - - sh.share(EMAIL, perm_type='user', role='writer') - worksheet_name = "worksheet" + str(worksheets_count) - - rows = data.shape[0] - #cols = data.shape[1] - - worksheet = sh.add_worksheet(title=worksheet_name,rows=rows,cols=20) - - spread = Spread(sheet_name) - - spread.df_to_sheet(df=data,sheet=worksheet_name,index=False) - - validation_rule = DataValidationRule( - BooleanCondition('ONE_OF_LIST', ['0', '1']),showCustomUi=True) - - validation_range = 'C2:E' + str(rows) - set_data_validation_for_cell_range(worksheet,validation_range, validation_rule) - -import warnings -warnings.filterwarnings('ignore') -import pandas as pd -import csv -import random -import numpy as np -import gspread -from gspread_pandas import Spread - -def filter_by_annotator(x,annotator_id): - - """ - filter posts by the given input annotator id - """ - return False if annotator_id not in x else True - - -def assign_availability(annotators,annotators_dict): - - """ - Assign initial availabity of annotators - """ - - for annotator in annotators: - - # is available - if annotator['current_posts'] <= annotator['max_post']: - annotators_dict[annotator['id']] = 1 - - # not available - else: - annotators_dict[annotator['id']] = 0 - - return annotators_dict - -def pick_annotators(annotators_dict,count,lang,annotators,Config): - - """ - Arg: - annotators_dict : dict with annotator id as key and availability (bool) as value,can expand with new - incoming annotators - - availability - - 1 -> available - 0 -> not available - - count : no. of annotator to allocate - - lang : language of the post - - Return: - list of annotators ids - """ - - assign_dict = annotators_dict.copy() - - #going for option 2: assign zero avail value to un-available annotators - for annotator,avail in annotators_dict.items(): - - # depends on the annotator id (sequence) - if (not avail) or (annotators[Config['ordering'][annotator]]['language']!= lang): - - # remove from annotators dict if not available or not same language as the post - assign_dict.pop(annotator) - - # error handling for sample larger than population - - # send - try: - list_annotators = random.sample(assign_dict.keys(),count) - except: - raise "Error allocating posts to annotators" - - return list_annotators - -def fetch_tweets(num_posts,lang='hi'): - - data = pd.read_csv('twitter_data_jan6.csv') - - lang_groups = data.groupby(['Language']) - - lang_data = lang_groups.get_group(lang) - - lang_data = lang_data.sample(frac=1) - - return lang_data.head(num_posts) - -def allocate(posts,annotators,Config): - - """ - Allocate posts to annotators following distribution criteria - - Args: - - posts : [ - { - tweet, - hashtags, - timestamp, - language - annotations: [ - { - id, - status : pending | completed - - # pending -> assigned to the annotator,yet to be completed -> 0 - # completed -> assigned to the annator, annotation completed -> 1 - } - ] - } - ] - - annotators: [ - { - id, - language, - max_post, - current_posts,# count of posts assigned - overlap : # yet to be defined - } - ] - - config : { - - net_overlap #k value in the distribution criteria - } - - """ - - # Iterate through the posts and assign posts following the distribution criteria. - # k% of the posts need to be annotated by 3 annotators. The rest of the posts (100-k)% will be annotated by (N-3) annotator. - - # initialize annotators availability - - annotators_dict = {} - annotators_dict = assign_availability(annotators,annotators_dict) - - #print(annotators_dict) - - # create a csv with post id,tweet and annotator id - csv_dict = [] - - # iterate through posts and assign them if already not assigned - print(f"No. of posts : {len(posts)}") - - for post in posts: - - temp_dict = {} - - if not post['annotations'][0]['id']: - - p = random.uniform(0,1) - - # paramter 'K' - if p < Config['net_overlap']/100: - - list_annotators = pick_annotators(annotators_dict,3,post['language'],annotators,Config) - - temp_id_list = [] - temp_status_list = [] - - for annotator in list_annotators: - - #check the language avail before and [] - #check availablity can be done here [+] - - #allocate_helper(post,annotator) - temp_id_list.append(annotator) - temp_status_list.append(0) #status - - post['annotations'][0]['id'] = annotator - post['annotations'][0]['status'] = 0 # pending annotation status - - ann_index = Config['ordering'][annotator] - - annotators[ann_index]['current_posts'] += 1 - - # update annotators_dict by changing avail value of un-unavailable annotators to 0 - if annotators[ann_index]['current_posts'] > annotators[ann_index]['max_post']: - annotators_dict[ann_index] = 0 - - temp_dict['tweet_id'] = post['post_id'] - temp_dict['tweet'] = post['tweet'] - temp_dict['annotator'] = temp_id_list - temp_dict['count'] = len(temp_id_list) - - #temp_dict['status'] = temp_status_list - - else: - list_annotators = pick_annotators(annotators_dict,1,post['language'],annotators,Config) - - for annotator in list_annotators: - - post['annotations'][0]['id'] = annotator - post['annotations'][0]['status'] = 0 # pending annotation status - ann_index = Config['ordering'][annotator] - - annotators[ann_index]['current_posts'] += 1 - if annotators[ann_index]['current_posts'] > annotators[ann_index]['max_post']: - annotators_dict[ann_index] = 0 - - - - temp_dict['tweet_id'] = post['post_id'] - temp_dict['tweet'] = post['tweet'] - temp_dict['annotator'] = post['annotations'][0]['id'] - temp_dict['count'] = 1 - - csv_dict.append(temp_dict) - - keys = csv_dict[0].keys() - - with open('allocation_hindi.csv', 'w', newline='') as output_file: - dict_writer = csv.DictWriter(output_file, keys) - dict_writer.writeheader() - dict_writer.writerows(csv_dict) - - -if __name__ == "__main__": - - config = {'net_overlap' : 20,'ordering' : {'denny_hi':0, 'kirti_hi':1,'div_hi':2,'seema_hi':3,'kherun_hi':4, - 'haseena_hi':5,'srishti_hi':6}} - """ - Fetch tweets from csv - """ - posts = fetch_tweets(num_posts=8000) - hin_posts = [{'tweet': row[0],'post_id':row[1],'language':row[2],'annotations':[{'id' : [],'status':-1}]} for row in zip(posts['Tweet'],posts['Tweet ID'],posts['Language'])] - - hin_annotators = [{'id':'denny_hi','language':'hi','max_post':10000,'current_posts':0}, - {'id':'kirti_hi','language':'hi','max_post':10000,'current_posts':0}, - {'id':'div_hi','language':'hi','max_post':9000,'current_posts':0}, - {'id':'seema_hi','language':'hi','max_post':8000,'current_posts':0}, - {'id':'kherun_hi','language':'hi','max_post':7000,'current_posts':0}, - {'id':'haseena_hi','language':'hi','max_post':7500,'current_posts':0}, - {'id':'srishti_hi','language':'hi','max_post':7500,'current_posts':0}] - - allocate(hin_posts,hin_annotators,config) - - df_hin = pd.read_csv('allocation_hindi.csv') - - df_hin_k_posts = df_hin[df_hin['count']==3] - - df_hin_k_posts['annotator'] = df_hin_k_posts['annotator'].apply(eval) - - hin_annotator_ids = list(config['ordering'].keys()) - - for _id in hin_annotator_ids: - - annotator_posts = df_hin_k_posts[df_hin_k_posts['annotator'].apply(filter_by_annotator,args=(_id,))] - - annotator_posts = annotator_posts.iloc[:,:-2] - - annotator_posts.columns = ['tweet_id','tweet_edited_text'] - annotator_posts.reset_index(inplace=True,drop=True) - - additional_fields = ['Is this post gendered abuse when not directed at a person of marginalized gender?' - ,'Is the post gendered abuse even when directed at a person of marginalized gender?','Does this post contain explicit/ aggressive language?'] - - print(annotator_posts.head()) - - for field in additional_fields: - - annotator_posts[field] = 'NaN' - - upload_to_spreadsheet(annotator_posts,'techtattle6@gmail.com',_id) \ No newline at end of file diff --git a/annotators/allotment/README.md b/annotators/allotment/README.md deleted file mode 100644 index 9d556d25..00000000 --- a/annotators/allotment/README.md +++ /dev/null @@ -1,56 +0,0 @@ -### Posts Allocation to Annotators - -#### Following is the sample dict of posts and annotators with corresponding attributes - - - -``` -posts : [ - { - tweet, - hashtags, - timestamp, - language - annotations: [ - { - id, - status : pending | completed - # pending -> assigned to the annotator,yet to be completed -> 0 - # completed -> assigned to the annator, annotation completed -> 1 - } - ] - } - ] - -annotators: [ - { - id, - language, - max_post, - current_posts,# count of posts assigned - overlap : # yet to be defined - } - -config : { - net_overlap -> k value -} - -``` - -#### Distribution Criteria - - - k% of the posts need to be annotated by 3 random annotators. The rest of the posts (100-k)% will be annotated by 1 random annotator. - - - - -#### WorkFlow of the Script - --> Assign the k value (net_overlap) - --> Fetch the posts from db - --> Update the posts acc. to the above dict format - --> Create an annotators dict to save the availability of annotators - diff --git a/annotators/allotment/allotment.py b/annotators/allotment/allotment.py deleted file mode 100644 index 27d74f77..00000000 --- a/annotators/allotment/allotment.py +++ /dev/null @@ -1,209 +0,0 @@ -import warnings -warnings.filterwarnings('ignore') -import pandas as pd -import csv -import random - -def assign_availability(annotators,annotators_dict): - - """ - Assign initial availabity of annotators - """ - - for annotator in annotators: - - # is available - if annotator['current_posts'] <= annotator['max_post']: - annotators_dict[annotator['id']] = 1 - - # not available - else: - annotators_dict[annotator['id']] = 0 - - return annotators_dict - -def pick_annotators(annotators_dict,count,lang,annotators): - - """ - Arg: - annotators_dict : dict with annotator id as key and availability (bool) as value,can expand with new - incoming annotators - availability - - 1 -> available - 0 -> not available - count : no. of annotator to allocate - lang : language of the post - Return: - list of annotators ids - """ - - assign_dict = annotators_dict.copy() - - #going for option 2: assign zero avail value to un-available annotators - for annotator,avail in annotators_dict.items(): - - # depends on the annotator id (sequence) - if (not avail) or (annotators[annotator-1]['language']!= lang): - - # remove from annotators dict if not available or not same language as the post - assign_dict.pop(annotator) - - # error handling for sample larger than population - - # send - try: - list_annotators = random.sample(assign_dict.keys(),count) - except: - raise "Error allocating posts to annotators" - - return list_annotators - -def fetch_tweets(num_posts,lang='hi'): - - data = pd.read_csv('twitter_data_jan6.csv') - - lang_groups = data.groupby(['Language']) - - lang_data = lang_groups.get_group(lang) - - lang_data = lang_data.sample(frac=1) - - return lang_data.head(num_posts) - -def allocate(posts,annotators,Config): - - """ - Allocate posts to annotators following distribution criteria - - Args: - - posts : [ - { - tweet, - hashtags, - timestamp, - language - annotations: [ - { - id, - status : pending | completed - # pending -> assigned to the annotator,yet to be completed -> 0 - # completed -> assigned to the annator, annotation completed -> 1 - } - ] - } - ] - annotators: [ - { - id, - language, - max_post, - current_posts,# count of posts assigned - overlap : # yet to be defined - } - ] - - config : { - - net_overlap #k value in the distribution criteria - } - """ - - # Iterate through the posts and assign posts following the distribution criteria. - # k% of the posts need to be annotated by 3 annotators. The rest of the posts (100-k)% will be annotated by (N-3) annotator. - - # initialize annotators availability - - annotators_dict = {} - annotators_dict = assign_availability(annotators,annotators_dict) - - - # create a csv with post id,tweet and annotator id - csv_dict = [] - - # iterate through posts and assign them if already not assigned - print(f"No. of posts : {len(posts)}") - - for post in posts: - - temp_dict = {} - - if not post['annotations'][0]['id']: - - p = random.uniform(0,1) - - # paramter 'K' - if p < Config['net_overlap']/100: - - list_annotators = pick_annotators(annotators_dict,3,post['language'],annotators) - - temp_id_list = [] - temp_status_list = [] - - for annotator in list_annotators: - - #check the language avail before and [] - #check availablity can be done here [+] - - #allocate_helper(post,annotator) - temp_id_list.append(annotator) - temp_status_list.append(0) #status - - - post['annotations'][0]['id'] = annotator - post['annotations'][0]['status'] = 0 # pending annotation status - annotators[annotator-1]['current_posts'] += 1 - - # update annotators_dict by changing avail value of un-unavailable annotators to 0 - if annotators[annotator-1]['current_posts'] > annotators[annotator-1]['max_post']: - annotators_dict[annotator] = 0 - - temp_dict['post_id'] = post['post_id'] - temp_dict['annotator'] = temp_id_list - #temp_dict['status'] = temp_status_list - - else: - list_annotators = pick_annotators(annotators_dict,1,post['language'],annotators) - - for annotator in list_annotators: - - post['annotations'][0]['id'] = annotator - post['annotations'][0]['status'] = 0 # pending annotation status - annotators[annotator-1]['current_posts'] += 1 - - if annotators[annotator-1]['current_posts'] > annotators[annotator-1]['max_post']: - annotators_dict[annotator] = 0 - - - - temp_dict['post_id'] = post['post_id'] - temp_dict['annotator'] = post['annotations'][0]['id'] - - - - csv_dict.append(temp_dict) - - keys = csv_dict[0].keys() - - with open('allocation_hindi.csv', 'w', newline='') as output_file: - dict_writer = csv.DictWriter(output_file, keys) - dict_writer.writeheader() - dict_writer.writerows(csv_dict) - - -if __name__ == "__main__": - - config = {'net_overlap' : 20} - - #hindi posts - posts = fetch_tweets(num_posts=8000) - hin_posts = [{'tweet': row[0],'post_id':row[1],'language':row[2],'annotations':[{'id' : [],'status':-1}]} for row in zip(posts['Tweet'],posts['Tweet ID'],posts['Language'])] - - hin_annotators = [{'id':1,'language':'hi','max_post':10000,'current_posts':0}, - {'id':2,'language':'hi','max_post':10000,'current_posts':0}, - {'id':3,'language':'hi','max_post':9000,'current_posts':0}, - {'id':4,'language':'hi','max_post':8000,'current_posts':0}, - {'id':5,'language':'hi','max_post':7000,'current_posts':0}, - {'id':6,'language':'hi','max_post':7500,'current_posts':0}] - - allocate(hin_posts,hin_annotators,config) diff --git a/annotators/annotation-ui/.gitignore b/annotators/annotation-ui/.gitignore deleted file mode 100644 index f9b891a9..00000000 --- a/annotators/annotation-ui/.gitignore +++ /dev/null @@ -1,7 +0,0 @@ -node_modules/ -.cache/ -public - -# e2e artefacts -cypress/videos -cypress/screenshots \ No newline at end of file diff --git a/annotators/annotation-ui/README.md b/annotators/annotation-ui/README.md deleted file mode 100644 index c5477224..00000000 --- a/annotators/annotation-ui/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# Annotators Frontend - -> Install packages, you only need to do this once after cloning the repo -```bash -npm i -``` - -> Start annotation-ui -```bash -npm run start -``` -> You will be able to access the frontend at `localhost:8000` - -> Any changes made in annotation-ui code will be directly reflected - -> Use Ctrl+C to stop - -### For API development with backend - -> Go to `/src/components/config.js` and Edit the file like below -``` -api_endpoint: "http://localhost:3000/api" -``` diff --git a/annotators/annotation-ui/cypress.json b/annotators/annotation-ui/cypress.json deleted file mode 100644 index c3b257ba..00000000 --- a/annotators/annotation-ui/cypress.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "projectId": "f6w72z" -} diff --git a/annotators/annotation-ui/cypress/fixtures/example.json b/annotators/annotation-ui/cypress/fixtures/example.json deleted file mode 100644 index 02e42543..00000000 --- a/annotators/annotation-ui/cypress/fixtures/example.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "name": "Using fixtures to represent data", - "email": "hello@cypress.io", - "body": "Fixtures are a great way to mock data for responses to routes" -} diff --git a/annotators/annotation-ui/cypress/fixtures/profile.json b/annotators/annotation-ui/cypress/fixtures/profile.json deleted file mode 100644 index b6c355ca..00000000 --- a/annotators/annotation-ui/cypress/fixtures/profile.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "id": 8739, - "name": "Jane", - "email": "jane@example.com" -} \ No newline at end of file diff --git a/annotators/annotation-ui/cypress/fixtures/users.json b/annotators/annotation-ui/cypress/fixtures/users.json deleted file mode 100644 index 79b699aa..00000000 --- a/annotators/annotation-ui/cypress/fixtures/users.json +++ /dev/null @@ -1,232 +0,0 @@ -[ - { - "id": 1, - "name": "Leanne Graham", - "username": "Bret", - "email": "Sincere@april.biz", - "address": { - "street": "Kulas Light", - "suite": "Apt. 556", - "city": "Gwenborough", - "zipcode": "92998-3874", - "geo": { - "lat": "-37.3159", - "lng": "81.1496" - } - }, - "phone": "1-770-736-8031 x56442", - "website": "hildegard.org", - "company": { - "name": "Romaguera-Crona", - "catchPhrase": "Multi-layered client-server neural-net", - "bs": "harness real-time e-markets" - } - }, - { - "id": 2, - "name": "Ervin Howell", - "username": "Antonette", - "email": "Shanna@melissa.tv", - "address": { - "street": "Victor Plains", - "suite": "Suite 879", - "city": "Wisokyburgh", - "zipcode": "90566-7771", - "geo": { - "lat": "-43.9509", - "lng": "-34.4618" - } - }, - "phone": "010-692-6593 x09125", - "website": "anastasia.net", - "company": { - "name": "Deckow-Crist", - "catchPhrase": "Proactive didactic contingency", - "bs": "synergize scalable supply-chains" - } - }, - { - "id": 3, - "name": "Clementine Bauch", - "username": "Samantha", - "email": "Nathan@yesenia.net", - "address": { - "street": "Douglas Extension", - "suite": "Suite 847", - "city": "McKenziehaven", - "zipcode": "59590-4157", - "geo": { - "lat": "-68.6102", - "lng": "-47.0653" - } - }, - "phone": "1-463-123-4447", - "website": "ramiro.info", - "company": { - "name": "Romaguera-Jacobson", - "catchPhrase": "Face to face bifurcated interface", - "bs": "e-enable strategic applications" - } - }, - { - "id": 4, - "name": "Patricia Lebsack", - "username": "Karianne", - "email": "Julianne.OConner@kory.org", - "address": { - "street": "Hoeger Mall", - "suite": "Apt. 692", - "city": "South Elvis", - "zipcode": "53919-4257", - "geo": { - "lat": "29.4572", - "lng": "-164.2990" - } - }, - "phone": "493-170-9623 x156", - "website": "kale.biz", - "company": { - "name": "Robel-Corkery", - "catchPhrase": "Multi-tiered zero tolerance productivity", - "bs": "transition cutting-edge web services" - } - }, - { - "id": 5, - "name": "Chelsey Dietrich", - "username": "Kamren", - "email": "Lucio_Hettinger@annie.ca", - "address": { - "street": "Skiles Walks", - "suite": "Suite 351", - "city": "Roscoeview", - "zipcode": "33263", - "geo": { - "lat": "-31.8129", - "lng": "62.5342" - } - }, - "phone": "(254)954-1289", - "website": "demarco.info", - "company": { - "name": "Keebler LLC", - "catchPhrase": "User-centric fault-tolerant solution", - "bs": "revolutionize end-to-end systems" - } - }, - { - "id": 6, - "name": "Mrs. Dennis Schulist", - "username": "Leopoldo_Corkery", - "email": "Karley_Dach@jasper.info", - "address": { - "street": "Norberto Crossing", - "suite": "Apt. 950", - "city": "South Christy", - "zipcode": "23505-1337", - "geo": { - "lat": "-71.4197", - "lng": "71.7478" - } - }, - "phone": "1-477-935-8478 x6430", - "website": "ola.org", - "company": { - "name": "Considine-Lockman", - "catchPhrase": "Synchronised bottom-line interface", - "bs": "e-enable innovative applications" - } - }, - { - "id": 7, - "name": "Kurtis Weissnat", - "username": "Elwyn.Skiles", - "email": "Telly.Hoeger@billy.biz", - "address": { - "street": "Rex Trail", - "suite": "Suite 280", - "city": "Howemouth", - "zipcode": "58804-1099", - "geo": { - "lat": "24.8918", - "lng": "21.8984" - } - }, - "phone": "210.067.6132", - "website": "elvis.io", - "company": { - "name": "Johns Group", - "catchPhrase": "Configurable multimedia task-force", - "bs": "generate enterprise e-tailers" - } - }, - { - "id": 8, - "name": "Nicholas Runolfsdottir V", - "username": "Maxime_Nienow", - "email": "Sherwood@rosamond.me", - "address": { - "street": "Ellsworth Summit", - "suite": "Suite 729", - "city": "Aliyaview", - "zipcode": "45169", - "geo": { - "lat": "-14.3990", - "lng": "-120.7677" - } - }, - "phone": "586.493.6943 x140", - "website": "jacynthe.com", - "company": { - "name": "Abernathy Group", - "catchPhrase": "Implemented secondary concept", - "bs": "e-enable extensible e-tailers" - } - }, - { - "id": 9, - "name": "Glenna Reichert", - "username": "Delphine", - "email": "Chaim_McDermott@dana.io", - "address": { - "street": "Dayna Park", - "suite": "Suite 449", - "city": "Bartholomebury", - "zipcode": "76495-3109", - "geo": { - "lat": "24.6463", - "lng": "-168.8889" - } - }, - "phone": "(775)976-6794 x41206", - "website": "conrad.com", - "company": { - "name": "Yost and Sons", - "catchPhrase": "Switchable contextually-based project", - "bs": "aggregate real-time technologies" - } - }, - { - "id": 10, - "name": "Clementina DuBuque", - "username": "Moriah.Stanton", - "email": "Rey.Padberg@karina.biz", - "address": { - "street": "Kattie Turnpike", - "suite": "Suite 198", - "city": "Lebsackbury", - "zipcode": "31428-2261", - "geo": { - "lat": "-38.2386", - "lng": "57.2232" - } - }, - "phone": "024-648-3804", - "website": "ambrose.net", - "company": { - "name": "Hoeger LLC", - "catchPhrase": "Centralized empowering task-force", - "bs": "target end-to-end models" - } - } -] \ No newline at end of file diff --git a/annotators/annotation-ui/cypress/integration/annotation_spec.js b/annotators/annotation-ui/cypress/integration/annotation_spec.js deleted file mode 100644 index be96bd71..00000000 --- a/annotators/annotation-ui/cypress/integration/annotation_spec.js +++ /dev/null @@ -1,95 +0,0 @@ -/// - -describe("Post Annotation", () => { - before(() => { - cy.exec( - "cd ../backend && pwd && npx sequelize-cli db:seed:undo:all && npx sequelize-cli db:seed --seed 20220219083157-e2e-test-setup.js" - ).then((res) => { - cy.log(res); - }); - }); - - it("A User can annotate posts", () => { - cy.visit("http://localhost:8000/login"); - cy.get('input[name="username"]').type("tattle_annotator_en"); - cy.get('input[name="password"]').type("r4&BTq"); - cy.contains("Login").click(); - - cy.url().should("include", "/posts"); - - cy.wait(250); - cy.get('input[name="question_1"]').eq(1).click(); - cy.get('input[name="question_2"]').eq(0).click(); - cy.get('input[name="question_3"]').eq(0).click(); - - cy.get('[aria-label="LinkNext"]').click(); - cy.wait(2000); - - cy.contains("pending : 53"); - - cy.get('[aria-label="LinkPrevious"]').click(); - cy.wait(2000); - - cy.get('input[name="question_1"]').eq(1).should("be.checked"); - cy.get('input[name="question_2"]').eq(0).should("be.checked"); - cy.get('input[name="question_3"]').eq(0).should("be.checked"); - - cy.get('[aria-label="LinkNext"]').click(); - cy.get('[aria-label="LinkNext"]').click(); - cy.get('[aria-label="LinkNext"]').click(); - cy.get('[aria-label="LinkNext"]').click(); - cy.get('[aria-label="LinkNext"]').click(); - cy.wait(2000); - }); - - it("User session is maintained", () => { - cy.contains("Logout").click(); - cy.url().should("include", "/login"); - - cy.get('input[name="username"]').type("tattle_annotator_en"); - cy.get('input[name="password"]').type("r4&BTq"); - cy.contains("Login").click(); - - cy.url().should("include", "/posts"); - - cy.contains("6/54"); - - cy.reload(); - cy.wait(2000); - cy.contains("6/54"); - cy.get('[aria-label="LinkNext"]').click(); - cy.get('[aria-label="LinkNext"]').click(); - cy.get('[aria-label="LinkNext"]').click(); - cy.contains("9/54"); - - cy.get('[aria-label="LinkPrevious"]').click(); - cy.get('[aria-label="LinkPrevious"]').click(); - cy.get('[aria-label="LinkPrevious"]').click(); - cy.get('[aria-label="LinkPrevious"]').click(); - cy.get('[aria-label="LinkPrevious"]').click(); - cy.get('[aria-label="LinkPrevious"]').click(); - cy.contains("3/54"); - }); - - it("Annotations are stored correctly in db", () => { - cy.request("http://localhost:3000/api/annotations/type/csv").then( - (response) => { - if (response.status === 200) { - let annotation_1 = response.body.tweets.filter( - (tweet) => tweet.userId === "bd33353d-7ac2-418a-8417-4e82004fd398" - ); - cy.log(annotation_1); - // expect(response.body.tweets[0]).to.have.property('tweets') - } - } - ); - }); - - after(() => { - cy.exec("cd ../backend && npx sequelize-cli db:seed:undo:all").then( - (res) => { - cy.log(res); - } - ); - }); -}); diff --git a/annotators/annotation-ui/cypress/integration/localization.js b/annotators/annotation-ui/cypress/integration/localization.js deleted file mode 100644 index e2674adb..00000000 --- a/annotators/annotation-ui/cypress/integration/localization.js +++ /dev/null @@ -1,48 +0,0 @@ -describe.only("App is multilingua", () => { - before(() => { - cy.exec( - "cd ../backend && pwd && npx sequelize-cli db:seed:undo:all && npx sequelize-cli db:seed --seed 20220219083157-e2e-test-setup.js" - ).then((res) => { - cy.log(res); - }); - }); - it("App opens in english for english users", () => { - cy.visit("http://localhost:8000/login"); - cy.get('input[name="username"]').type("tattle_annotator_en"); - cy.get('input[name="password"]').type("r4&BTq"); - cy.contains("Login").click(); - - cy.url().should("include", "/posts"); - cy.contains("OGBV ANNOTATOR"); - cy.contains("Post Annotation"); - cy.contains( - "Is this post gendered abuse when not directed at a person of marginalized gender and sexuality?" - ); - }); - it("App opens in hindi for hindi users", () => { - cy.visit("http://localhost:8000/login"); - cy.get('input[name="username"]').type("tattle_annotator_hi"); - cy.get('input[name="password"]').type("Vk5$%r"); - cy.contains("Login").click(); - - cy.url().should("include", "/hi/posts"); - cy.contains("एनोटेटर"); - cy.contains("पोस्ट व्याख्या"); - cy.contains( - "क्या यह पोस्ट ओजीबीवी है अगर हाशिए के लिंग के व्यक्ति पर निर्देशित नहीं किया जाता है?" - ); - }); - it("App opens in tamil for tamil users", () => { - cy.visit("http://localhost:8000/login"); - cy.get('input[name="username"]').type("tattle_annotator_ta"); - cy.get('input[name="password"]').type("+B2CuN"); - cy.contains("Login").click(); - - cy.url().should("include", "/ta/posts"); - cy.contains("குறிப்பாளர்"); - cy.contains("குறிப்பிற்க்குப் பின்"); - cy.contains( - "இந்த பதிவு விளிம்புநிலை பாலினத்தவர்களைத் கொண்ட ஒரு நபரை நோக்கி இயக்கப்படாதபோது பாலின துஷ்பிரயோகம் ஆகிறது ?" - ); - }); -}); diff --git a/annotators/annotation-ui/cypress/integration/login_spec.js b/annotators/annotation-ui/cypress/integration/login_spec.js deleted file mode 100644 index d3ed7984..00000000 --- a/annotators/annotation-ui/cypress/integration/login_spec.js +++ /dev/null @@ -1,24 +0,0 @@ -/// - -describe("User Authentication", () => { - it("A User can login and logout", () => { - cy.visit("http://localhost:8000/login"); - cy.get('input[name="username"]').type("tattle_annotator_en"); - cy.get('input[name="password"]').type("r4&BTq"); - cy.contains("Login").click(); - - cy.url().should("include", "/hi/posts"); - - cy.contains("Logout").click(); - cy.url().should("include", "/login"); - }); - - it("An Unauthorized user can't login", () => { - cy.visit("http://localhost:8000/login"); - cy.get('input[name="username"]').type("tattle_annotator_en"); - cy.get('input[name="password"]').type("asdf"); - cy.contains("Login").click(); - - cy.url().should("include", "/login"); - }); -}); diff --git a/annotators/annotation-ui/cypress/plugins/index.js b/annotators/annotation-ui/cypress/plugins/index.js deleted file mode 100644 index 59b2bab6..00000000 --- a/annotators/annotation-ui/cypress/plugins/index.js +++ /dev/null @@ -1,22 +0,0 @@ -/// -// *********************************************************** -// This example plugins/index.js can be used to load plugins -// -// You can change the location of this file or turn off loading -// the plugins file with the 'pluginsFile' configuration option. -// -// You can read more here: -// https://on.cypress.io/plugins-guide -// *********************************************************** - -// This function is called when a project is opened or re-opened (e.g. due to -// the project's config changing) - -/** - * @type {Cypress.PluginConfig} - */ -// eslint-disable-next-line no-unused-vars -module.exports = (on, config) => { - // `on` is used to hook into various events Cypress emits - // `config` is the resolved Cypress config -} diff --git a/annotators/annotation-ui/cypress/support/commands.js b/annotators/annotation-ui/cypress/support/commands.js deleted file mode 100644 index 119ab03f..00000000 --- a/annotators/annotation-ui/cypress/support/commands.js +++ /dev/null @@ -1,25 +0,0 @@ -// *********************************************** -// This example commands.js shows you how to -// create various custom commands and overwrite -// existing commands. -// -// For more comprehensive examples of custom -// commands please read more here: -// https://on.cypress.io/custom-commands -// *********************************************** -// -// -// -- This is a parent command -- -// Cypress.Commands.add('login', (email, password) => { ... }) -// -// -// -- This is a child command -- -// Cypress.Commands.add('drag', { prevSubject: 'element'}, (subject, options) => { ... }) -// -// -// -- This is a dual command -- -// Cypress.Commands.add('dismiss', { prevSubject: 'optional'}, (subject, options) => { ... }) -// -// -// -- This will overwrite an existing command -- -// Cypress.Commands.overwrite('visit', (originalFn, url, options) => { ... }) diff --git a/annotators/annotation-ui/cypress/support/index.js b/annotators/annotation-ui/cypress/support/index.js deleted file mode 100644 index d68db96d..00000000 --- a/annotators/annotation-ui/cypress/support/index.js +++ /dev/null @@ -1,20 +0,0 @@ -// *********************************************************** -// This example support/index.js is processed and -// loaded automatically before your test files. -// -// This is a great place to put global configuration and -// behavior that modifies Cypress. -// -// You can change the location of this file or turn off -// automatically serving support files with the -// 'supportFile' configuration option. -// -// You can read more here: -// https://on.cypress.io/configuration -// *********************************************************** - -// Import commands.js using ES2015 syntax: -import './commands' - -// Alternatively you can use CommonJS syntax: -// require('./commands') diff --git a/annotators/annotation-ui/gatsby-config.js b/annotators/annotation-ui/gatsby-config.js deleted file mode 100644 index bcc322ee..00000000 --- a/annotators/annotation-ui/gatsby-config.js +++ /dev/null @@ -1,81 +0,0 @@ -module.exports = { - siteMetadata: { - siteUrl: "https://ogbv-annotator.tattle.co.in", - title: "docs", - description: "A search engine framework", - author: "@tattle-made", - }, - plugins: [ - "gatsby-plugin-styled-components", - "gatsby-plugin-image", - "gatsby-plugin-react-helmet", - `gatsby-source-local-git-modern`, - { - resolve: `gatsby-plugin-mdx`, - options: { - extensions: [".mdx", ".md"], - defaultLayouts: { - default: require.resolve("./src/components/default-layout.js"), - }, - gatsbyRemarkPlugins: [ - { - resolve: `gatsby-remark-highlight-code`, - options: { - terminal: "carbon", - theme: "synthwave", - }, - }, - ], - }, - }, - "gatsby-plugin-sharp", - "gatsby-transformer-sharp", - { - resolve: "gatsby-plugin-web-font-loader", - options: { - google: { - families: ["Raleway"], - }, - }, - }, - { - resolve: "gatsby-source-filesystem", - options: { - name: "images", - path: "./src/images/", - }, - __key: "images", - }, - { - resolve: "gatsby-source-filesystem", - options: { - name: "pages", - path: "./src/pages/", - }, - __key: "pages", - }, - { - resolve: "gatsby-source-filesystem", - options: { - path: "./src/locales", - name: "locale", - }, - }, - { - resolve: "gatsby-plugin-react-i18next", - options: { - localeJsonSourceName: "locale", - languages: ["en", "hi", "ta"], - defaultLanguage: "en", - siteUrl: "http://localhost:8000/", - i18nextOptions: { - interpolation: { - escapeValue: false, - }, - keySeparator: false, - nsSeparator: false, - }, - }, - }, - ], -}; diff --git a/annotators/annotation-ui/gatsby-node.js b/annotators/annotation-ui/gatsby-node.js deleted file mode 100644 index 3bf96943..00000000 --- a/annotators/annotation-ui/gatsby-node.js +++ /dev/null @@ -1,12 +0,0 @@ -exports.onCreatePage = async ({ page, actions }) => { - const { createPage } = actions; - - // page.matchPath is a special key that's used for matching pages - // only on the client. - if (page.path.match(/^\/app/)) { - page.matchPath = `/app/*`; - - // Update the page. - createPage(page); - } -}; diff --git a/annotators/annotation-ui/package-lock.json b/annotators/annotation-ui/package-lock.json deleted file mode 100644 index 6e2725db..00000000 --- a/annotators/annotation-ui/package-lock.json +++ /dev/null @@ -1,38027 +0,0 @@ -{ - "name": "docs", - "version": "1.0.0", - "lockfileVersion": 2, - "requires": true, - "packages": { - "": { - "name": "docs", - "version": "1.0.0", - "dependencies": { - "@deckdeckgo/highlight-code": "^3.4.1", - "@mdx-js/mdx": "^1.6.22", - "@mdx-js/react": "^1.6.22", - "axios": "^0.21.4", - "babel-plugin-styled-components": "^1.13.3", - "gatsby": "^4.0.2", - "gatsby-plugin-image": "^2.0.0", - "gatsby-plugin-mdx": "^3.0.0", - "gatsby-plugin-react-helmet": "^5.0.0", - "gatsby-plugin-react-i18next": "^1.2.2", - "gatsby-plugin-sharp": "^4.0.1", - 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"name": "docs", - "version": "1.0.0", - "private": true, - "description": "docs", - "author": "Denny", - "keywords": [ - "gatsby" - ], - "scripts": { - "develop": "gatsby develop", - "start": "gatsby develop", - "build": "rm -rf public/ && gatsby build && cp -r public/ ../backend/public/ && rm -rf public/", - "serve": "gatsby serve", - "clean": "gatsby clean", - "e2e": "cypress open" - }, - "dependencies": { - "@deckdeckgo/highlight-code": "^3.4.1", - "@mdx-js/mdx": "^1.6.22", - "@mdx-js/react": "^1.6.22", - "axios": "^0.21.4", - "babel-plugin-styled-components": "^1.13.3", - "gatsby": "^4.0.2", - "gatsby-plugin-image": "^2.0.0", - "gatsby-plugin-mdx": "^3.0.0", - "gatsby-plugin-react-helmet": "^5.0.0", - "gatsby-plugin-react-i18next": "^1.2.2", - "gatsby-plugin-sharp": "^4.0.1", - "gatsby-plugin-styled-components": "^5.0.0", - "gatsby-plugin-web-font-loader": "^1.0.4", - "gatsby-remark-highlight-code": "^3.0.1", - "gatsby-source-filesystem": "^4.0.0", - "gatsby-source-local-git-modern": "^2.0.1", - "gatsby-transformer-sharp": "^4.0.0", - "grommet": "^2.18.0", - "grommet-icons": "^4.6.2", - "i18next": "^21.4.0", - "js-file-download": "^0.4.12", - "local-storage": "^2.0.0", - "lodash": "^4.17.21", - "lorem-ipsum": "^2.0.4", - "react": "^17.0.1", - "react-copy-to-clipboard": "^5.0.4", - "react-dom": "^17.0.1", - "react-helmet": "^6.1.0", - "react-i18next": "^11.13.0", - "react-json-view": "^1.21.3", - "react-radio-button-group": "^1.2.8", - "styled-components": "^5.3.3" - } -} diff --git a/annotators/annotation-ui/src/components/AllotationList.jsx b/annotators/annotation-ui/src/components/AllotationList.jsx deleted file mode 100644 index 446e9c53..00000000 --- a/annotators/annotation-ui/src/components/AllotationList.jsx +++ /dev/null @@ -1,34 +0,0 @@ -import React, { useState, useEffect } from "react"; -import { Box } from "grommet"; -import axios from "axios"; -import { config } from "./config"; -import { navigate } from "gatsby"; -import { Post } from "./Post"; - -const AllocationList = () => { - const userId = "9404f34a-c36e-4437-82d2-aee3f22fb93c"; - const [posts, setPosts] = useState([]); - const [pageNum, setPageNum] = useState(0); - useEffect(async () => { - const postsRes = await axios.get( - `${config.api_endpoint}/allocation/for-user/?userId=${userId}&page=${pageNum}` - ); - console.log(postsRes); - setPosts(postsRes.data.allocation); - }, []); - - function onPostClicked(ix) { - console.log({ pageNum, ix }); - navigate(`/app/post/${posts[ix].Post.id}`); - } - - return ( - - {posts.map((post, ix) => ( - - ))} - - ); -}; - -export default AllocationList; diff --git a/annotators/annotation-ui/src/components/App.css b/annotators/annotation-ui/src/components/App.css deleted file mode 100644 index c4c61e7f..00000000 --- a/annotators/annotation-ui/src/components/App.css +++ /dev/null @@ -1 +0,0 @@ -@import "~antd/dist/antd.css"; diff --git a/annotators/annotation-ui/src/components/AppContentSection.jsx b/annotators/annotation-ui/src/components/AppContentSection.jsx deleted file mode 100644 index 86566d8a..00000000 --- a/annotators/annotation-ui/src/components/AppContentSection.jsx +++ /dev/null @@ -1,18 +0,0 @@ -import React from "react"; -import { ResponsiveContext, Box } from "grommet"; - -export const AppContentSection = ({ children }) => { - const size = React.useContext(ResponsiveContext); - - return size === "small" ? ( - - {children.map((child) => ( - {child} - ))} - - ) : ( - - {children.map((child, ix) => child)} - - ); -}; diff --git a/annotators/annotation-ui/src/components/AppSideNavBar.jsx b/annotators/annotation-ui/src/components/AppSideNavBar.jsx deleted file mode 100644 index d630244c..00000000 --- a/annotators/annotation-ui/src/components/AppSideNavBar.jsx +++ /dev/null @@ -1,53 +0,0 @@ -import React from "react"; -import { - ResponsiveContext, - Box, - Text, - Sidebar, - Tabs, - Tab, - Anchor, -} from "grommet"; -import { PlainLink } from "./atoms/TattleLinks"; - -export const AppSideNavBar = () => { - const size = React.useContext(ResponsiveContext); - const options = [ - { label: "Annotations", route: "/annotations" }, - { label: "Users", route: "/users" }, - { label: "Posts", route: "/posts" }, - ]; - function onTabChanged(e) { - console.log(e); - } - return size === "small" ? ( - - - - One - - - Two - - - - ) : ( - - - - - Posts - - - - - Users - - - Users - Posts - {size} - - - ); -}; diff --git a/annotators/annotation-ui/src/components/Footer.jsx b/annotators/annotation-ui/src/components/Footer.jsx deleted file mode 100644 index 374615ea..00000000 --- a/annotators/annotation-ui/src/components/Footer.jsx +++ /dev/null @@ -1,65 +0,0 @@ -import React from "react"; -import { Footer, Box, Anchor, Image, Text } from "grommet"; - -export const AppFooter = () => { - return ( -
- - - - - - - ) - - - - - - - - - - - - - - - - - - - Built by{" "} - - CIS - {" "} - and{" "} - - Tattle - {" "} - with support from{" "} - - ONI - - -
- ); -}; diff --git a/annotators/annotation-ui/src/components/NoteWorthyModal.jsx b/annotators/annotation-ui/src/components/NoteWorthyModal.jsx deleted file mode 100644 index ac2d5aa3..00000000 --- a/annotators/annotation-ui/src/components/NoteWorthyModal.jsx +++ /dev/null @@ -1,25 +0,0 @@ -import React from "react"; -import { Box, Text, Button } from "grommet"; - -export function NoteworthyModal({ message, setMessage }) { - return ( - - - {message.message} - {message.payload ? ( - {JSON.stringify(message.payload, null, 4)} - ) : null} - - - - - ); -} diff --git a/annotators/annotation-ui/src/components/NotificationFeed.jsx b/annotators/annotation-ui/src/components/NotificationFeed.jsx deleted file mode 100644 index 405e54de..00000000 --- a/annotators/annotation-ui/src/components/NotificationFeed.jsx +++ /dev/null @@ -1,60 +0,0 @@ -import React, { useState } from "react"; -import { Box, Text, Button } from "grommet"; -import { Clipboard, Close } from "grommet-icons"; -import { CopyToClipboard } from "react-copy-to-clipboard"; - -export function NotificationFeed({ items, onClose }) { - const [copyConfirmation, setCopyConfirmation] = useState(undefined); - function showCopyConfirmation() { - console.log("1"); - setCopyConfirmation("Copied to Clipboard"); - setTimeout(() => { - setCopyConfirmation(undefined); - }, 1000); - } - - return ( - - - Updates - - - - - {items && items.length !== 0 - ? items.map((item) => ( - - {item.message} - {item.payload ? ( - {JSON.stringify(item.payload, null, 4)} - ) : null} - - )) - : null} - - - ); -} diff --git a/annotators/annotation-ui/src/components/Post.jsx b/annotators/annotation-ui/src/components/Post.jsx deleted file mode 100644 index a1207a1d..00000000 --- a/annotators/annotation-ui/src/components/Post.jsx +++ /dev/null @@ -1,49 +0,0 @@ -import React from "react"; -import { Box, Text, Image } from "grommet"; -import { Clock, Alert, Checkmark } from "grommet-icons"; - -export function Post(props) { - return ( - props.onPostClicked(props.ix)} - > - - - {props.post.Post.id} - - {props.post.status === "pending" ? ( - - ) : props.post.status === "malformed" ? ( - - ) : props.post.status === "complete" ? ( - - ) : null} - -
- {props.post.Post.role === "text" ? ( - - {props.post.Post.text} - - ) : null} -
- {props.post.Post.role === "image" ? ( - - - - ) : null} - - ); -} diff --git a/annotators/annotation-ui/src/components/atoms/AppShell.js b/annotators/annotation-ui/src/components/atoms/AppShell.js deleted file mode 100644 index cb7653ca..00000000 --- a/annotators/annotation-ui/src/components/atoms/AppShell.js +++ /dev/null @@ -1,42 +0,0 @@ -import React from "react"; -import { Grommet, ResponsiveContext, Box, Text, Heading } from "grommet"; -import TattleTheme from "./Theme"; -import SEO from "./SEO"; -import { PlainLink } from "./TattleLinks"; -import TattleLogo from "./TattleLogo"; -import { useLocation } from "@reach/router"; -import { Router } from "@reach/router"; - -/** - * @author - * @function ContentPageLayout - **/ - -const AppShell = ({ - children, - footerItems, - headerTarget, - primaryNav, - expandCenter, - contentWidth, - isMDXPage, -}) => { - const size = React.useContext(ResponsiveContext); - const location = useLocation(); - - return ( - - - - {children} - - - ); -}; - -export default AppShell; diff --git a/annotators/annotation-ui/src/components/atoms/PrivateRoute.jsx b/annotators/annotation-ui/src/components/atoms/PrivateRoute.jsx deleted file mode 100644 index 0617a9b7..00000000 --- a/annotators/annotation-ui/src/components/atoms/PrivateRoute.jsx +++ /dev/null @@ -1,16 +0,0 @@ -import React from "react"; -import { navigate } from "gatsby"; -import { isLoggedIn } from "../../controllers/auth"; -import { Box } from "grommet"; - -const PrivateRoute = ({ component: Component, location, ...rest }) => { - if (!isLoggedIn() && location.pathname !== `/app/login`) { - // If we’re not logged in, redirect to the home page. - navigate(`/app/login`); - return null; - } - - return ; -}; - -export default PrivateRoute; diff --git a/annotators/annotation-ui/src/components/atoms/SEO.js b/annotators/annotation-ui/src/components/atoms/SEO.js deleted file mode 100644 index 52029fbd..00000000 --- a/annotators/annotation-ui/src/components/atoms/SEO.js +++ /dev/null @@ -1,80 +0,0 @@ -/** - * SEO component that queries for data with - * Gatsby's useStaticQuery React hook - * - * See: https://www.gatsbyjs.org/docs/use-static-query/ - */ - -import React from "react"; -import PropTypes from "prop-types"; -import Helmet from "react-helmet"; -import { useStaticQuery, graphql } from "gatsby"; - -function SEO({ description, lang, meta, title }) { - const { site } = useStaticQuery(graphql` - query { - site { - siteMetadata { - title - description - author - siteUrl - } - } - } - `); - - const metaDescription = description || site.siteMetadata.description; - const author = site.siteMetadata.author; - // const socialImageURL = allFile.edges[0].node.publicURL; - const baseURL = site.siteMetadata.siteUrl; - - return ( - - - - - {/* */} - - - - - - - {/* */} - {/* \n", - " \n", - " \n", - " " - ], - "text/plain": [ - " Unnamed: 0 ... no_of_anno\n", - "0 0 ... 4\n", - "1 1 ... 4\n", - "2 2 ... 4\n", - "3 3 ... 5\n", - "4 4 ... 3\n", - "\n", - "[5 rows x 4 columns]" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 28.6 ms (started: 2022-02-19 16:20:40 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "df['label'].value_counts()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Vprv6TO5xrDl", - "outputId": "85852bf2-a637-4a08-88ec-970b4ac2e743" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 52852\n", - "1 31616\n", - "Name: label, dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 17 - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 15.2 ms (started: 2022-02-19 16:20:48 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "df=df.loc[df['label'] != 'spam']\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "o6LjKWmeJxAA", - "outputId": "aa56c9bf-ee4e-4aa8-dcc9-ba864709ebc3" - }, - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 16.5 ms (started: 2022-02-19 16:20:45 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "df.loc[(df.label == 'normal'), 'label'] = 0\n", - "df.loc[(df.label == 'abusive'), 'label'] = 1\n", - "df.loc[(df.label == 'hateful'), 'label'] = 1\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CxS21U9gJaEd", - "outputId": "6175c03c-58f1-4b04-9471-a3957dc4f7c5" - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 29.2 ms (started: 2022-02-19 16:20:46 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "X=df.Tweet.values\n", - "y=df.label.values" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "a6WxeLJwvWDr", - "outputId": "36e5382b-9dbd-414a-ba53-ed3cddf6be41" - }, - "execution_count": 18, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 1.8 ms (started: 2022-02-19 16:20:51 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.model_selection import train_test_split\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "f4eFzPUMvk6E", - "outputId": "d32c6171-d112-4570-8d61-43153a52b5b6" - }, - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 18.2 ms (started: 2022-02-19 16:20:52 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "X_train, X_dev, y_train, y_dev = train_test_split(X, y, test_size=0.2, random_state=1)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qjcOSbs1uZYw", - "outputId": "d888ca4d-8ca9-4643-be08-d70367ab5c98" - }, - "execution_count": 20, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 14.3 ms (started: 2022-02-19 16:20:54 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import re" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n4ln_9aUuSz9", - "outputId": "4cd0b0ea-d6f4-4ef2-d7d3-b83e38bc2314" - }, - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 965 µs (started: 2022-02-19 16:20:56 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "def preprocess(text):\n", - " new_text = []\n", - " for t in text.split(\" \"):\n", - " t = '@user' if t.startswith('@') and len(t) > 1 else t\n", - " t = 'http' if t.startswith('http') else t\n", - " new_text.append(t)\n", - " return \" \".join(new_text)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vfPul_2bpOQM", - "outputId": "752d2328-a310-4236-f9d4-7d51c25c8d12" - }, - "execution_count": 22, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 3.92 ms (started: 2022-02-19 16:20:58 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts= np.array([preprocess(text) for text in X_train])\n", - "dev_texts= np.array([preprocess(text) for text in X_dev])\n", - "num_labels=2\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "u2wJtI9otfoM", - "outputId": "224db285-ae08-45cd-ddb0-bb487570de5d" - }, - "execution_count": 23, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 2.56 s (started: 2022-02-19 16:20:59 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts=train_texts.tolist()\n", - "dev_texts=dev_texts.tolist()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "q1qqMnolQtZw", - "outputId": "05787446-4e2e-466d-f553-6a5da8a50fca" - }, - "execution_count": 24, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 744 ms (started: 2022-02-19 16:21:02 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts[0:20]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0c8yp3y75JwV", - "outputId": "549165da-bc27-40d1-9d5f-93ad17adefa6" - }, - "execution_count": 25, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[\"@user Becky at Mania you're gonna show those other women why YOU'RE the best. I'm rooting for you just like always.\",\n", - " 'I hate all these \"safe spaces\" that people think they need. How about just live life? Oh, someone offended you! Do you know what you can -',\n", - " \"@user Then it's easy to understand why anti imperialism involves the global south liberating itself. Not being liberated.\",\n", - " 'BEN IS A LEJ ELLIOTS A DIV I LIKED AND SUBSCRIBED TO YOUR VIDS NOW U SHOULD LIKE THIS AND FOLLOW ME 😛 @user',\n", - " 'im lowkey the sorriest human being. i real deal ate a bowl of shredded cheese last night 🙄😂😂 http',\n", - " \"Trump condemns Syria attack but won't telegraph US response http http\",\n", - " '@user @user if i give a fiver for two adults could he keep the change - carrying a fiver note o… http',\n", - " 'Stunning Babe Showering http -Learn how to squirt http #squirting',\n", - " \"@user @user Laura, nothing is off limits anymore. I'm so sick of the messages Hollywood is sending our youth.\",\n", - " 'Please RT #interiordesign #homedecor #decoration Pallet Wood Wall Mount Silverware Holder... http',\n", - " '[Infographic] 6 steps to successful email campaigns http #emailmarketing #email #biztips http',\n", - " 'My dude okky came in with his head bleeding and no clue where his car is or how fucked up he got 😂😂',\n", - " '@user @user Speaking of Muslims, for over a 1000 years, you conducted genocides in Subcontinent, which cont… http',\n", - " 'I love @user \"This is what happens when a country’s electorate are not the brightest bulbs in the chandel… http',\n", - " \"Find Accredited tax solutions in your area right now with BBB's Tax Resource Center. http\",\n", - " '@user @user Did u really think Asia spread across the Pacific all the way to Hawaii? Fuckn idiot.I hate d… http',\n", - " 'RT @user \"nah boo, you just fucking with the wrong niggas\" -a wronger nigga',\n", - " 'Important to be able to communicate your science across different communities to succeed in industry - Skylar Place @user #SBM2017',\n", - " 'Sis helped oversee what @user called \"one of the world’s largest killings of demonstrators in a single day in recent history\" in 2013.',\n", - " 'RT @user Al Qaeda supporters because her stupid brain cannot function to the degree that allows her to fucking understand that you c…']" - ] - }, - "metadata": {}, - "execution_count": 25 - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 6.41 ms (started: 2022-02-19 16:21:06 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_encodings = tokenizer(train_texts,truncation=True)\n", - "dev_encodings = tokenizer(dev_texts,truncation=True)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Slj1XFSODKSv", - "outputId": "0c41f4a9-0c43-43cc-c1d0-fa088883581d" - }, - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 8.17 s (started: 2022-02-19 16:21:15 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "\n", - "class TaskDatasetB(torch.utils.data.Dataset):\n", - "\n", - " def __init__(self,encodings,labels):\n", - " self.encodings = encodings\n", - " self.labels = labels\n", - "\n", - " def __getitem__(self,idx):\n", - " item = {key: torch.tensor(val[idx]) for key,val in self.encodings.items()}\n", - " item['labels'] = torch.tensor(self.labels[idx])\n", - " return item\n", - "\n", - " def __len__(self):\n", - " return len(self.labels)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vRvVCWTbDMnb", - "outputId": "8f8a0197-419f-45cb-9e9b-a2ee5798e8c6" - }, - "execution_count": 27, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 9.4 ms (started: 2022-02-19 16:21:28 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_dataset = TaskDatasetB(train_encodings,y_train)\n", - "dev_dataset = TaskDatasetB(dev_encodings,y_dev)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0r1oAyXYDTKs", - "outputId": "e610e1bb-4c85-4854-c015-cf9f9b857ff7" - }, - "execution_count": 28, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 1.58 ms (started: 2022-02-19 16:21:31 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cnYpYx3yDWWO", - "outputId": "8234d057-5c5a-466c-cc54-a30e9199914f" - }, - "execution_count": 29, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 1.63 ms (started: 2022-02-19 16:21:33 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import TrainingArguments\n", - "\n", - "training_args = TrainingArguments(\n", - " 'OGBV-gender-twtrobertabase-en-founta_final',\n", - " run_name = \"twt-roberta-en-3epoch\",\n", - " evaluation_strategy = 'epoch',\n", - " save_strategy='epoch',\n", - " num_train_epochs = 1,\n", - " load_best_model_at_end = True,\n", - " push_to_hub='OGBV-gender-twtrobertabase-en-founta_final'\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lYc1a44kDaLY", - "outputId": "7d757a99-a6b5-4c9e-a5f2-2feb2c9fb916" - }, - "execution_count": 30, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 584 ms (started: 2022-02-19 16:21:49 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import accuracy_score,f1_score,precision_score,recall_score\n", - "\n", - "def compute_metrics(eval_preds):\n", - "\n", - " logits,labels = eval_preds\n", - " predictions = np.argmax(logits,axis=-1)\n", - "\n", - " acc = accuracy_score(labels,predictions)\n", - " precision = precision_score(labels,predictions,average='weighted')\n", - " recall = recall_score(labels,predictions,average='weighted')\n", - " f1 = f1_score(labels,predictions,average='weighted')\n", - " \n", - " return {\n", - " 'acc' : acc,\n", - " 'precision' : precision,\n", - " 'recall' : recall,\n", - " 'f1' : f1\n", - " }" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nAN5uVqTDgzO", - "outputId": "8d47e8a7-d316-4581-f9f5-7cb5bfa42696" - }, - "execution_count": 31, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 7.99 ms (started: 2022-02-19 16:21:54 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!apt-get install git-lfs\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "w5uRI8BKDi_I", - "outputId": "65bcd719-f785-4de5-a159-932e572145fa" - }, - "execution_count": 32, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Reading package lists... Done\n", - "Building dependency tree \n", - "Reading state information... 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"\u001b[K |████████████████████████████████| 311 kB 10.1 MB/s \n", - "\u001b[K |████████████████████████████████| 3.3 MB 19.2 MB/s \n", - "\u001b[K |████████████████████████████████| 596 kB 28.3 MB/s \n", - "\u001b[K |████████████████████████████████| 895 kB 29.5 MB/s \n", - "\u001b[K |████████████████████████████████| 67 kB 3.2 MB/s \n", - "\u001b[K |████████████████████████████████| 1.1 MB 38.6 MB/s \n", - "\u001b[K |████████████████████████████████| 1.1 MB 33.7 MB/s \n", - "\u001b[K |████████████████████████████████| 133 kB 45.5 MB/s \n", - "\u001b[K |████████████████████████████████| 243 kB 47.3 MB/s \n", - "\u001b[K |████████████████████████████████| 271 kB 47.1 MB/s \n", - "\u001b[K |████████████████████████████████| 94 kB 3.2 MB/s \n", - "\u001b[K |████████████████████████████████| 144 kB 49.1 MB/s \n", - "\u001b[?25h" - ] - } - ], - "source": [ - "!pip install transformers[sentencepiece]==4.10.0 datasets --quiet\n" - ] - }, - { - "cell_type": "code", - "source": [ - "!python -c \"import transformers;print(transformers.__version__)\"\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Zz4GyJ8VhPAg", - "outputId": "2be401d6-2949-494e-f201-cd39a9e6483a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "4.10.0\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install -qq ipython-autotime\n", - "\n", - "%load_ext autotime" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IYr9XHDYhTKy", - "outputId": "e4ce177d-b4a4-4262-e523-7c073703b42f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 126 µs (started: 2022-02-22 05:27:52 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "torch.cuda.empty_cache()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qUTGnCf5l109", - "outputId": "715239f6-88b6-4815-8964-26db29934200" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 927 ms (started: 2022-02-22 05:27:52 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "import torch\n", - "import random\n", - "from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zeIeWOnMhT8I", - "outputId": "752e5450-a7f5-4fa5-fb74-befd8c06664f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 187 ms (started: 2022-02-22 05:27:53 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "def set_seed(seed: int):\n", - " \"\"\"\n", - " Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if\n", - " installed).\n", - "\n", - " Args:\n", - " seed (:obj:`int`): The seed to set.\n", - " \"\"\"\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " if is_torch_available():\n", - " torch.manual_seed(seed)\n", - " torch.cuda.manual_seed_all(seed)\n", - " # ^^ safe to call this function even if cuda is not available\n", - " if is_tf_available():\n", - " import tensorflow as tf\n", - "\n", - " tf.random.set_seed(seed)\n", - "\n", - "set_seed(1)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nCU4HaVHhYHu", - "outputId": "ca6db46f-4b99-4e0b-b160-ac6addf1decc" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 2.74 s (started: 2022-02-22 05:27:53 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "import numpy as np\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5py3GSi0hanI", - "outputId": "5c83e5ea-b9b1-4b29-9e9e-d05ef5b81ea3" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 1.63 ms (started: 2022-02-22 05:27:56 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import DataCollatorWithPadding\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "f1p348_nhpxE", - "outputId": "b0c01444-0cab-4ff7-f7e0-9eb337c77750" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 255 ms (started: 2022-02-22 05:27:56 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!huggingface-cli login\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "k_pViQVDh4gO", - "outputId": "0ad5b214-f2df-4818-eb44-bc8ed0248584" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", - " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", - " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", - " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", - " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", - "\n", - " To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n", - " (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n", - " \n", - "Token: \n", - "Login successful\n", - "Your token has been saved to /root/.huggingface/token\n", - "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", - "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", - "\n", - "git config --global credential.helper store\u001b[0m\n", - "time: 10.5 s (started: 2022-02-22 05:30:20 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", - "\n", - "checkpoint = \"rohanrajpal/bert-base-en-hi-codemix-cased\"\n", - "\n", - "tokenizer = AutoTokenizer.from_pretrained(checkpoint)" - ], - "metadata": { - "id": "zl3chzW9h93R", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 162, - "referenced_widgets": [ - "47d1cb3223c44f61a041199c742a222f", - "6d160f7fa83948f9b5feb7480ec083d9", - "e4c83b09e0d84149b0587382fd14ae97", - "90ca3de289c94d7f8afce3141c17d9e7", - "d09b100d074e40cb8889c8fb6a0da6fb", - "86dc6a32bfb84792b206a87fedef3e2a", - "e5649d25f3b14292af44009c6f2d5fae", - "0d02b1c5236e4f609f1a2c761581ec64", - "0fc493f1d4b94223977985b1aeb86322", - "99d4ddeddad047afba0790868330310b", - "9a060a5be9414c778c308b590bb692a0", - "2c495a298f2944edad97e55194398c5d", - "da0dc8d5d2a745f0932b2003ff8e0287", - "9ab2248f457d484e881bac9bdb11001e", - "f4a65e5a612e43d38e302689e6220e5d", - "6a482e6548f94bc99f57f4eeaa0ca6f0", - "c6fb559e6b4e4635939960827506c86a", - "25a004c57f79410ba2178049e9cc1bec", - "c6937c9f398043c9b20788fd7833c366", - "24aa321e277e4cfb8888caad9a93929a", - "cc27ebcedbab4d069ad93d8d814fed27", - "3edd3ecb869e4c9ea69df719152fd825", - "14ba06cef4ab4a53b18904d29ba648cf", - "00920db6d1504d8b90c8e6e9a3f2e180", - "3fc0432dd89e48998a2f770f7b4c271f", - "829b2402bf114a1e9a148333ee2d9227", - "338bf0254c8d4a03a5b229b9fdba4fa7", - "8718beeb39c04ce0a1f7f5bf4b058043", - "edec9e643c5d480cb058767d123ff3c6", - "fb019bd01b514946a2b0b68a510990ed", - "d0377999fa2c49e48f7f570993252f0e", - "5c6037d68ac5485ba3d859bd3d3d5fb2", - "a0f00320a93d44a7ae2d0d1ea8101918", - "c7d6fbe06c1843f8afef564cb56440c2", - "4b7412ad0d9f4a869fbf9a75401dbd1c", - "3eb4481297284fd2836cbd5e7aeea459", - "6bcee28c1d594dc090ab2a6f73114c38", - "3f883c77e7844d0f9dbfc769e9edf4b9", - "289c0e06d405488a9c2bf5aee2073a8c", - "bfa5f39d31fc4f1b8b469699efdb8b41", - "bf0105a588ee49ceb18e2a71213edf23", - "3a752ff0b422487b87f24cc41f755cf3", - "15ec897bf1d3448e8d09b5eeb7e9952b", - "69f39a83bc3f460d842df647ea3bec84" - ] - }, - "outputId": "3301b905-d8b0-4fe3-e020-46b63a4d27e5" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "47d1cb3223c44f61a041199c742a222f", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Downloading: 0%| | 0.00/48.0 [00:00\n", - "
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0facebook_corpus_msr_401470Mahmood Ghaznavi Aor ABdali ko bhol gaya ha tu...OAG
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2facebook_corpus_msr_373389chutiya friday ko isliye releae krte kyoki wee...CAG
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1facebook_corpus_msr_690306First stage par dus jootey khaye Grover seOAG
2facebook_corpus_msr_382520Salman aur aamir ki kounsi movie release huyee...OAG
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2996facebook_corpus_msr_356273Agar sarkar ne ab bhi kush nahi kia to ak din ...CAG
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2998facebook_corpus_msr_1906980Yaar isliye to jansatta UNFOLLOW kiya tha. Ab ...CAG
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OAG\n", - "\n", - "[3001 rows x 3 columns]" - ] - }, - "metadata": {}, - "execution_count": 19 - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 32.8 ms (started: 2022-02-22 05:28:03 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "dev.loc[(dev.label == 'NAG'), 'label'] = 0\n", - "dev.loc[(dev.label == 'CAG'), 'label'] = 1\n", - "dev.loc[(dev.label == 'OAG'), 'label'] = 1" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Sj2dOT1J2irN", - "outputId": "223dca49-b2f9-48d1-cf2b-1c41ea6ae9b7" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 7.13 ms (started: 2022-02-22 05:28:03 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import re" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n4ln_9aUuSz9", - "outputId": "8380f9e4-c758-4d66-e020-c1ef68f3ca77" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 1.02 ms (started: 2022-02-22 05:28:03 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "def preprocess(text):\n", - " new_text = []\n", - " for t in text.split(\" \"):\n", - " t = '@user' if t.startswith('@') and len(t) > 1 else t\n", - " t = 'http' if t.startswith('http') else t\n", - " new_text.append(t)\n", - " return \" \".join(new_text)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vfPul_2bpOQM", - "outputId": "994a7133-9093-47e5-ad15-ddb9ae15db00" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 6.8 ms (started: 2022-02-22 05:28:03 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "X_train=train.text.values\n", - "X_dev=dev.text.values\n", - "y_train=train.label.values\n", - "y_dev=dev.label.values" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WIEQexVoqTMi", - "outputId": "3b8af1b5-ed3a-4dc1-c6b3-61ef1b789594" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 3.49 ms (started: 2022-02-22 05:28:42 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train['text'].isnull().sum()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "trsonV6fzL4U", - "outputId": "e09fa9c8-e3f5-4d5e-edc7-76bb2a1da466" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0" - ] - }, - "metadata": {}, - "execution_count": 33 - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 6.74 ms (started: 2022-02-22 05:28:45 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train=train.dropna()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JNeCGn5Ozkh4", - "outputId": "a8554cdd-a440-4a51-a96d-2bb81b26df84" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 16.4 ms (started: 2022-02-22 05:28:39 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts= np.array([preprocess(text) for text in X_train])\n", - "dev_texts= np.array([preprocess(text) for text in X_dev])\n", - "num_labels=2\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "u2wJtI9otfoM", - "outputId": "26517a2b-9c8b-4f64-e1ae-a3a358a21893" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 375 ms (started: 2022-02-22 05:28:49 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts=train_texts.tolist()\n", - "dev_texts=dev_texts.tolist()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "q1qqMnolQtZw", - "outputId": "29508124-49c1-4072-a0ee-7bfa03a52f42" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 50.2 ms (started: 2022-02-22 05:28:52 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts[0:20]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0c8yp3y75JwV", - "outputId": "955e1e27-0238-42e7-dfb7-71a27bd4337c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['Mahmood Ghaznavi Aor ABdali ko bhol gaya ha tu 😂 1 hazar saal tjhy ghulam bana kr rkha tha bhol gaya tu',\n", - " 'Bhai 60sal pehle desh me kya tha pehle pta kro desh waise ka waise kaise hai',\n", - " 'chutiya friday ko isliye releae krte kyoki week end ke 3 din milte h to log free hokr film dekh sake..!! hd h yrr chutiyapa ki',\n", - " 'जय मोदीराज',\n", - " 'UPA walo ne bahot kuch kr diya tha desh k liye',\n", - " 'Pan ko Aadhar se link ki zarurat kuy hai? Supreme Court ne ye bola hi nahi',\n", - " 'काकर पाथर जोड़ के मस्जिद लई बनाय।\\n\\nता चढ़ि मुल्ला बांघ दे , क्या बहरा हुआ खुदाय। \\n\\nयह मैं नहीं कबीर ने कहा था।',\n", - " 'Us raat tere papa k jageh mera sperm gya tha use of technology \\nAur tere papa ko pta v na chala 😂😂😂\\nChutiya',\n", - " 'गटर के कीड़े',\n", - " 'Waise bandhu jet lag se bachne ke liye Raat ko 10 baje ke baad so jao',\n", - " 'get your love back +91-(9815215009) and #all_problems_solution_with_in_72_hours one call change life \\nसमस्या चाहे कैसी भी हो जड़ से खत्म।\\nबड़ी से बड़ी समस्या का समाधान।101%\\nअपने मन चाहे प्यार को पाना, काम कारोबार, किया कराया, सोतन से छुटकारा, पति - पत्नी में अनबन, +91-(9815215009)',\n", - " 'विदिशा से बीजेपी की सुषमा स्वराज आगे',\n", - " 'Bhaii tume lagta bjp ne kia h ye to fir aao politics me sudharo society.....',\n", - " 'Es kutte ko jail me daal do .Desh drohi hai',\n", - " 'सीनाजोरी',\n", - " 'Haan sahi kaha \\nJab pm ko jutey padh saktey wo bhi 56 inch ko \\nTo congress neta kis kheht ki muli hai',\n", - " '#arwin khwabon se bahar niklo aur dekho Kashmir India ka tha, h, aur rhega. And vase bhi Kashmir m hum kaafiron k bahut sare religious places h Jo uski beauty ko chaar chand lgate h tum terrorist ka toh sirf terrorism h jo uski beauty ko brbaad kr rha h. Tum log jisko Jo kuch kehte ho usse janane m hume koi interest nhi h isliye faltu m bako mt k hum isko ye kehte h hum usko woh kehte h vagarah vagarah.',\n", - " 'श्री श्री जिनके लिए पूरी सरकार ने दिल्ली मे यमुना भी बरबाद होने दी सारी सुविधा दी उनसे मिलने से देशविरोध का क्या मतलब बेशक कोई और मिलता तो उसे तमगा और पाकिस्तान का निवास मिलते',\n", - " 'मोदी जी अब ऐसा भी कानुन कर सकते है,,कि गरीब और आम जनता वोट ही ना करे|\\nऔर वोट करना है तो मोदी को ही करे|नही तो आम लोगोको कान पकडनेको कह सकते है|\\nराशन भी बंद कर सकते है|अब सब सत्ता अकेले ही हाथमे है|\\nआम लोग कही ना कही परेशान होने चाहिए,भ्रमिष्ठ होने चाहिए,सोचनेकेलिए दिमाग ही बंद होना चाहिए!!!\\nएैसा ही कानुन बना सकते है|\\nजनता जाए भाडमे !ऐसी ही सोच है मोदीजी की!बस बाकी कुछ नही|',\n", - " 'जुबान दी थी तो जुबान की कीमत निभाई सर मुंडा के ये तो मेरे लिए गर्व की बात है।']" - ] - }, - "metadata": {}, - "execution_count": 36 - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 4.29 ms (started: 2022-02-22 05:28:55 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_encodings = tokenizer(train_texts,truncation=True,)\n", - "dev_encodings = tokenizer(dev_texts,truncation=True)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Slj1XFSODKSv", - "outputId": "c15c7177-687c-4f3a-e1b0-a765f17babac" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 2.06 s (started: 2022-02-22 05:28:58 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "\n", - "class TaskDatasetB(torch.utils.data.Dataset):\n", - "\n", - " def __init__(self,encodings,labels):\n", - " self.encodings = encodings\n", - " self.labels = labels\n", - "\n", - " def __getitem__(self,idx):\n", - " item = {key: torch.tensor(val[idx]) for key,val in self.encodings.items()}\n", - " item['labels'] = torch.tensor(self.labels[idx])\n", - " return item\n", - "\n", - " def __len__(self):\n", - " return len(self.labels)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vRvVCWTbDMnb", - "outputId": "f234ef74-6fa9-494d-928c-648fc8c4485e" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 8.82 ms (started: 2022-02-22 05:29:03 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_dataset = TaskDatasetB(train_encodings,y_train)\n", - "dev_dataset = TaskDatasetB(dev_encodings,y_dev)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0r1oAyXYDTKs", - "outputId": "8de1c0bc-e54f-4639-8eab-217d70ffcd6c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 4.82 ms (started: 2022-02-22 05:29:06 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cnYpYx3yDWWO", - "outputId": "bbc3746d-52b8-4add-b534-e92f4658e479" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 1.74 ms (started: 2022-02-22 05:29:09 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import TrainingArguments\n", - "\n", - "training_args = TrainingArguments(\n", - " 'hin-trac1_fin',\n", - " run_name = \"hi-2epoch\",\n", - " evaluation_strategy = 'epoch',\n", - " save_strategy='epoch',\n", - " num_train_epochs = 2,\n", - " load_best_model_at_end = True,\n", - " push_to_hub='hin-trac1_fin'\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lYc1a44kDaLY", - "outputId": "dd71f910-05a6-4b34-a542-b4d3a3ae93ff" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "PyTorch: setting up devices\n", - "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 15.2 ms (started: 2022-02-22 05:30:40 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import accuracy_score,f1_score,precision_score,recall_score\n", - "\n", - "def compute_metrics(eval_preds):\n", - "\n", - " logits,labels = eval_preds\n", - " predictions = np.argmax(logits,axis=-1)\n", - "\n", - " acc = accuracy_score(labels,predictions)\n", - " precision = precision_score(labels,predictions,average='weighted')\n", - " recall = recall_score(labels,predictions,average='weighted')\n", - " f1 = f1_score(labels,predictions,average='weighted')\n", - " \n", - " return {\n", - " 'acc' : acc,\n", - " 'precision' : precision,\n", - " 'recall' : recall,\n", - " 'f1' : f1\n", - " }" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nAN5uVqTDgzO", - "outputId": "bdbea99d-4b27-4309-bda3-5854e77196ea" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 9.3 ms (started: 2022-02-22 05:30:43 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!apt-get install git-lfs\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "w5uRI8BKDi_I", - "outputId": "a5dccf27-5f3f-4c62-c4b0-5be7c58468a6" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Reading package lists... Done\n", - "Building dependency tree \n", - "Reading state information... Done\n", - "The following packages were automatically installed and are no longer required:\n", - " cuda-command-line-tools-10-0 cuda-command-line-tools-10-1\n", - " cuda-command-line-tools-11-0 cuda-compiler-10-0 cuda-compiler-10-1\n", - " cuda-compiler-11-0 cuda-cuobjdump-10-0 cuda-cuobjdump-10-1\n", - " cuda-cuobjdump-11-0 cuda-cupti-10-0 cuda-cupti-10-1 cuda-cupti-11-0\n", - " cuda-cupti-dev-11-0 cuda-documentation-10-0 cuda-documentation-10-1\n", - " cuda-documentation-11-0 cuda-documentation-11-1 cuda-gdb-10-0 cuda-gdb-10-1\n", - " cuda-gdb-11-0 cuda-gpu-library-advisor-10-0 cuda-gpu-library-advisor-10-1\n", - " cuda-libraries-10-0 cuda-libraries-10-1 cuda-libraries-11-0\n", - " cuda-memcheck-10-0 cuda-memcheck-10-1 cuda-memcheck-11-0 cuda-nsight-10-0\n", - " cuda-nsight-10-1 cuda-nsight-11-0 cuda-nsight-11-1 cuda-nsight-compute-10-0\n", - " cuda-nsight-compute-10-1 cuda-nsight-compute-11-0 cuda-nsight-compute-11-1\n", - " cuda-nsight-systems-10-1 cuda-nsight-systems-11-0 cuda-nsight-systems-11-1\n", - " cuda-nvcc-10-0 cuda-nvcc-10-1 cuda-nvcc-11-0 cuda-nvdisasm-10-0\n", - " cuda-nvdisasm-10-1 cuda-nvdisasm-11-0 cuda-nvml-dev-10-0 cuda-nvml-dev-10-1\n", - " cuda-nvml-dev-11-0 cuda-nvprof-10-0 cuda-nvprof-10-1 cuda-nvprof-11-0\n", - " cuda-nvprune-10-0 cuda-nvprune-10-1 cuda-nvprune-11-0 cuda-nvtx-10-0\n", - " cuda-nvtx-10-1 cuda-nvtx-11-0 cuda-nvvp-10-0 cuda-nvvp-10-1 cuda-nvvp-11-0\n", - " cuda-nvvp-11-1 cuda-samples-10-0 cuda-samples-10-1 cuda-samples-11-0\n", - " cuda-samples-11-1 cuda-sanitizer-11-0 cuda-sanitizer-api-10-1\n", - " cuda-toolkit-10-0 cuda-toolkit-10-1 cuda-toolkit-11-0 cuda-toolkit-11-1\n", - " cuda-tools-10-0 cuda-tools-10-1 cuda-tools-11-0 cuda-tools-11-1\n", - " cuda-visual-tools-10-0 cuda-visual-tools-10-1 cuda-visual-tools-11-0\n", - " cuda-visual-tools-11-1 default-jre dkms freeglut3 freeglut3-dev\n", - " keyboard-configuration libargon2-0 libcap2 libcryptsetup12\n", - " libdevmapper1.02.1 libfontenc1 libidn11 libip4tc0 libjansson4\n", - " libnvidia-cfg1-510 libnvidia-common-460 libnvidia-common-510\n", - " libnvidia-extra-510 libnvidia-fbc1-510 libnvidia-gl-510 libpam-systemd\n", - " libpolkit-agent-1-0 libpolkit-backend-1-0 libpolkit-gobject-1-0 libxfont2\n", - " libxi-dev libxkbfile1 libxmu-dev libxmu-headers libxnvctrl0 libxtst6\n", - " nsight-compute-2020.2.1 nsight-compute-2022.1.0 nsight-systems-2020.3.2\n", - " nsight-systems-2020.3.4 nsight-systems-2021.5.2 nvidia-dkms-510\n", - " nvidia-kernel-common-510 nvidia-kernel-source-510 nvidia-modprobe\n", - " nvidia-settings openjdk-11-jre policykit-1 policykit-1-gnome python3-xkit\n", - " screen-resolution-extra systemd systemd-sysv udev x11-xkb-utils\n", - " xserver-common xserver-xorg-core-hwe-18.04 xserver-xorg-video-nvidia-510\n", - "Use 'apt autoremove' to remove them.\n", - "The following NEW packages will be installed:\n", - " git-lfs\n", - "0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n", - "Need to get 2,129 kB of archives.\n", - "After this operation, 7,662 kB of additional disk space will be used.\n", - "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n", - "Fetched 2,129 kB in 1s (1,848 kB/s)\n", - "Selecting previously unselected package git-lfs.\n", - "(Reading database ... 155113 files and directories currently installed.)\n", - "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n", - "Unpacking git-lfs (2.3.4-1) ...\n", - "Setting up git-lfs (2.3.4-1) ...\n", - "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n", - "time: 7.34 s (started: 2022-02-22 05:29:19 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "model = AutoModelForSequenceClassification.from_pretrained(checkpoint,num_labels = 2,ignore_mismatched_sizes=True)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 154, - "referenced_widgets": [ - "cd4f8f5511964092bc3b07295af2bcbf", - "d1062ad15eb546f19e4c0f4d6dd34201", - "257487c05df5493caeaa3aeee9b6f0a6", - "5c48a875e4834b7b9315266b8061bc27", - "826173a72ef548d0ae865ef125891de9", - "1a4298d72c7945e3b24b6845677b5805", - "124d11ede7334f2e8c132c502c642106", - "fcfa9fc4eccc4f6ab304830f4dcd03cd", - "b48d19ff7082465a9370df61d73b4236", - "2168146c902e4ee08f3bd47fc0cdca01", - "fcfa750fd75f4d9e8f4355168fa3737a" - ] - }, - "id": "lcCk0mhNf5Qq", - "outputId": "91373ca9-a9bc-4c31-a5ce-1eced2f9e6ff" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cd4f8f5511964092bc3b07295af2bcbf", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Downloading: 0%| | 0.00/712M [00:00\n", - " \n", - " \n", - " [3000/3000 29:50, Epoch 2/2]\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
EpochTraining LossValidation LossAccPrecisionRecallF1
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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "hin_df['len']=seq_len" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "v6ohwspcUlwi", - "outputId": "ee5c63ea-f86a-40d6-8645-967aa72fc053" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "c=hin_df[hin_df['len']>70]\n", - "c.shape" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Q3DGGtKHUpvp", - "outputId": "a670dd8c-7cf3-4fc4-e562-c2486e082f41" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(136, 44)" - ] - }, - "metadata": {}, - "execution_count": 15 - } - ] - }, - { - "cell_type": "code", - "source": [ - "import torch" - ], - "metadata": { - "id": "RWByw1hnQ7hN" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "device = torch.device('cuda')\n", - "model.eval()\n", - "_ = model.to(device)" - ], - "metadata": { - "id": "uo_cqFVvTQfW" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "label_map= {\n", - " 0: 'None', \n", - " 1: 'Hate'\n", - " }" - ], - "metadata": { - "id": "c4Sf5YliTXI2" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "torch.cuda.empty_cache()\n" - ], - "metadata": { - "id": "b4EQnm5k77ii" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "offense_list = []\n", - "offense_confidence = []\n", - "bs = 16\n", - "for x in trange(len(hin_df['tweet'])//bs+1):\n", - " batch_x = list(hin_df['tweet'][x*bs:x*bs+bs])\n", - " clean_x = [preprocess(tweet) for tweet in batch_x]\n", - " encoded_input = tokenizer(clean_x, return_tensors='pt', padding=True, truncation=True, max_length=70)\n", - " encoded_input.to(device)\n", - " output = model(**encoded_input)\n", - " out = softmax(output[0].data, dim=-1).detach().cpu()\n", - " labels = [label_map[i] for i in torch.argmax(out,-1).numpy()]\n", - " offense_list.extend(labels)\n", - " for x_ind, y_ind in enumerate(torch.argmax(out,-1)):\n", - " offense_confidence.append(round(out[x_ind, y_ind].item(),2))\n", - " if(x%1000 == 0):\n", - " print('GPU memory: %.1f' % (torch.cuda.memory_allocated() // 1024 ** 2))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 916, - "referenced_widgets": [ - "b0eec0220a1d4280b06137f8aeeb3f78", - "3ae6f4a49fd94cb3904f96a7d2cbad77", - "9030f5527ef7428c8cb4b0cc84c54165", - "f8d61529f1664052830bd419ab454d31", - "4619554a1af1464f84d7b2a7deff5c9e", - "3980cd8614e84b74bbdc4132e1c3b0e6", - "fb6cee98f437465fab2a3231bb1a054d", - "6e667dd2a57043dfb560a6581ac0899d", - "f67d47a97ff44a06ad782bc2061d314d", - "5e4e153ef8404d699675983967e3efb2", - "25c891bf58db4d7b917e86235fec7bd5" - ] - }, - "id": "P5XnwE4wUWl0", - "outputId": "8eca2930-14b1-4002-c88e-86af69d2d19e" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b0eec0220a1d4280b06137f8aeeb3f78", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - " 0%| | 0/51000 [00:00\n", - "
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22619dbbaade83f8b60cc9ac471331270877128781825#RheaChakraborty So many days passed, CBI coul...[]NaNhttps://twitter.com/Utpal24378053/status/13312...13030375512891023362020-11-24 21:48:05 ISTen0020211124094226hashtag#RheaChakrabortytextNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN260.85Hate
33619dbbaade83f8b60cc9ac481331248718214033411How is #RheaChakraborty ??[]NaNhttps://twitter.com/IWillSpeakToo/status/13312...10988741102416896002020-11-24 20:20:02 ISTen0020211124094226hashtag#RheaChakrabortytextNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN40.54None
44619dbbaade83f8b60cc9ac491331226810579750918All members of yuva shiv sena party of the lik...[]NaNhttps://twitter.com/surabhi82707088/status/133...13239215482819665932020-11-24 18:52:59 ISTen0020211124094226hashtag#RheaChakrabortytextNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN380.97Hate
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156005356005361e51a402584e2832c8b53351079508688627474432Both murdered by cowardly twisted ethnic natio...['https://pbs.twimg.com/media/DvstWJtXcAA7Lf-....https://s3.ap-south-1.amazonaws.com/ogbv.tattl...https://twitter.com/StrategNL/status/107950868...934106122018-12-31 04:15:02 ISTen[]False20220117125656keywordnationalistsimage1.079509e+18NaNNaN[]1.0[]StrategNL🌎🇳🇱🌍🇪🇺🌏 aka Ronald JH Elzenga ⚔️☮︎NaNNaNNaN0.0NaNNaN1.0NaNNaN530.0NaNNaNNaN[]NaNNaNstrategnl1.02018-12-3104:15:02280.96Hate
156005656005661e51a402584e2832c8b53361079508591726522368@e0a4093c6d394e7 @laruedupass @AndrewKnight226...[]NaNhttps://twitter.com/PaulWhelan745/status/10795...5828670732018-12-31 04:14:39 ISTen[{'screen_name': 'laruedupass', 'name': 'Stur ...False20220117125656keywordnationaliststext1.079093e+18NaNNaN[]0.0[]Paul WhelanNaNNaNNaN0.0NaNNaN0.0NaNNaN530.0NaNNaNNaN[]NaNNaNpaulwhelan7450.02018-12-3104:14:39220.54None
156025656025661e51a860843dffd91b22a551079214709457412096@ShayarSalman @ela_mishra #चाटुकार of the year...[]NaNhttps://twitter.com/iamstkhan/status/107921470...10466455298563645442018-12-30 08:46:52 ISTen[{'screen_name': 'ShayarSalman', 'name': 'Salm...False20220117125806keywordचाटुकारtext1.079084e+18NaNNaN['चाटुकार', 'award']0.0[]Tabrez khan🇮🇳NaNNaNNaN0.0NaNNaN0.0NaNNaN530.0NaNNaNNaN[]NaNNaNiamstkhan0.02018-12-3008:46:5270.92None
156049656049661e55dc1a4e5ef51c302949a1417343940559204352#திருட்டு_பள்ளன் Down pointing backhand index...[]NaNhttps://twitter.com/jeeva27771760/status/14173...14018459040977428482021-07-20 10:11:43 ISTen[]False20220117174457hashtag#திருட்டு_பள்ளன்text1.417344e+18NaNNaN['திருட்டு_பள்ளன்']0.0[]jeeva_006🔰NaNNaNNaN0.0NaNNaN3.0NaNNaN530.0NaNNaNNaN[]NaNNaNjeeva277717600.02021-07-2010:11:43290.95None
156052756052761e56664fe26558c312062ac1335191641741611009@aayushpurohitt His her rendukum oraye meaning...[]NaNhttps://twitter.com/kodi_speaks/status/1335191...13124455046267248652020-12-05 17:27:48 ISTen[{'screen_name': 'aayushpurohitt', 'name': 'aa...False20220117182148hashtag#கோத்தாtext1.335143e+18NaNNaN['கோத்தா']0.0[]Dr.கொடி..🖤♥️NaNNaNNaN1.0NaNNaN0.0NaNNaN530.0NaNNaNNaN[]NaNNaNkodi_speaks0.02020-12-0517:27:4890.52None
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" installed).\n", - "\n", - " Args:\n", - " seed (:obj:`int`): The seed to set.\n", - " \"\"\"\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " if is_torch_available():\n", - " torch.manual_seed(seed)\n", - " torch.cuda.manual_seed_all(seed)\n", - " # ^^ safe to call this function even if cuda is not available\n", - " if is_tf_available():\n", - " import tensorflow as tf\n", - "\n", - " tf.random.set_seed(seed)\n", - "\n", - "set_seed(1)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nCU4HaVHhYHu", - "outputId": "201ab8cc-4fe1-425c-a41a-639af798c84a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 2.62 s (started: 2022-02-24 05:14:27 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "import numpy as np\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5py3GSi0hanI", - "outputId": "dbee633b-91d5-4693-ad79-6a3547a6d916" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 1.44 ms (started: 2022-02-24 05:14:30 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import DataCollatorWithPadding\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "f1p348_nhpxE", - "outputId": "492560d7-a27a-48f9-d837-959c3a184fd6" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 241 ms (started: 2022-02-24 05:14:30 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!huggingface-cli login\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "k_pViQVDh4gO", - "outputId": "69cedf75-8e5d-4dcb-b2a6-003f6ad5f7ff" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", - " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", - " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", - " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", - " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", - "\n", - " To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n", - " (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n", - " \n", - "Token: \n", - "Login successful\n", - "Your token has been saved to /root/.huggingface/token\n", - "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", - "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", - "\n", - "git config --global credential.helper store\u001b[0m\n", - "time: 16 s (started: 2022-02-24 05:14:49 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", - "\n", - "MODEL = \"ai4bharat/indic-bert\"\n", - "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n", - "model = AutoModelForSequenceClassification.from_pretrained(MODEL)" - ], - "metadata": { - "id": "zl3chzW9h93R", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 237, - "referenced_widgets": [ - "105e8489cc154907a506a8ebe3ebd181", - "c3a0ffa860b7413eb407057794fde939", - "d5e826209c614db0a55645c4283e26a4", - "74c2dbfc19f440b084ec77f69a11612d", - "228861e27a06457b9f7e1f9d05816400", - "57d939343a6b4a90b3b2893b46e1c6f4", - "da5fe51c1ecc452c97b01ab76b8b4783", - "adf598612af94aca89ad46c3e7758a8d", - "54f3f55017464d3ab5cc2edd307570b1", - "6722c9f89e4c4ba49f27809b0934b5cd", - "c9159b203e99497e80963dae1a8757ad", - "b5543dd409d0488e89da9671779d30e9", - "899ddf9cef344206ac8a6245169c8437", - "b499aae648564891b864ca31bbd83a9e", - "dc332c65502e4059bb603a05109200fa", - "6ef9a26fcc2240079630023eb6712426", - "1286b7bdce7745f18b7823703e4c4826", - "afceea2c5ea44726b84650e66dd755d9", - "478dcb8b382045eabc759d623e51fe94", - "2668a36f5ef2462cae999b3d271abb7b", - "661ec2f88db74cbc87450e159e00233c", - "f9a78a2296114433b658f7f04c8a362d", - "0fdfc55623194b4c80003898754f97e9", - "cd2b255bf70747408ab919f89d034d0e", - "56d8927a32ee4e5b90c27e5e0e51786b", - "c269a76770a3481a93a51b8921e8c918", - "4c9b2ef19e9f43bc95d755e009c1fb17", - "26094cdd59f441fd86614fd98db005f6", - "31b6b782fa6a414f8e7204cc3c4294ab", - "dc8df0741337480aa4aae73971cbc919", - "4c84c69e3ed24db781693c798527e1dc", - "0a78d29dc32b46408f1cb4467c32e91b", - "15f8c5ff89294edb8fe05f9237e3076a" - ] - }, - "outputId": "395989d9-6f6a-47d9-ccc8-e19ae8fb2571" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "105e8489cc154907a506a8ebe3ebd181", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Downloading: 0%| | 0.00/507 [00:00\n", - "
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3karthick subburaj anne .... intha padam vetri ...Not_offensiveNaN
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4386Lady super star Manju warrier Fans Hit likeNot_offensiveNaN
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"dev.loc[(dev.label == 'Offensive_Targeted_Insult_Other'), 'label'] = 1\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h-2NVAl5QDvy", - "outputId": "9cb31f65-6f7b-4bea-9d43-3bdd35e5222c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 16.3 ms (started: 2022-02-24 05:16:39 +00:00)\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " self._setitem_single_column(loc, value, pi)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import re" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n4ln_9aUuSz9", - "outputId": "98cddc5a-b64f-4652-bdc8-6422e97f8b65" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 1.02 ms (started: 2022-02-24 05:16:44 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "def preprocess(text):\n", - " new_text = []\n", - " for t in text.split(\" \"):\n", - " t = '@user' if t.startswith('@') and len(t) > 1 else t\n", - " t = 'http' if t.startswith('http') else t\n", - " new_text.append(t)\n", - " return \" \".join(new_text)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vfPul_2bpOQM", - "outputId": "5c79f128-3e33-4710-db5d-d5d4d6bf9490" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 5.8 ms (started: 2022-02-24 05:16:46 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "X_train=train.text.values\n", - "X_dev=dev.text.values\n", - "y_train=train.label.values\n", - "y_dev=dev.label.values" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WIEQexVoqTMi", - "outputId": "b8eabf6f-37c1-42e8-8572-72336084e6ce" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 3.2 ms (started: 2022-02-24 05:16:49 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts= np.array([preprocess(text) for text in X_train])\n", - "dev_texts= np.array([preprocess(text) for text in X_dev])\n", - "num_labels=2\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "u2wJtI9otfoM", - "outputId": "e356179f-a1e2-496a-fca2-47cd7fae1374" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 349 ms (started: 2022-02-24 05:16:50 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts=train_texts.tolist()\n", - "dev_texts=dev_texts.tolist()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "q1qqMnolQtZw", - "outputId": "a700e4a4-8ac6-4d20-ce61-6f4ff22f7a41" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 64.9 ms (started: 2022-02-24 05:16:52 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_texts[0:20]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0c8yp3y75JwV", - "outputId": "d22c343e-10c0-46ce-9f4a-e610ec1a68e9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['movie vara level la Erika poguthu',\n", - " 'Padam nalla comedy padama irukum polaye..',\n", - " 'karthick subburaj anne .... intha padam vetri adaya unagalukku ennudaya valthukkal...',\n", - " 'கவுண்டர் தேவர்.சார்பாக வெற்றி பெற வாழ்த்துக்கள் 🦁',\n", - " 'ippo intha trailer ah parkuravana oru like podunga',\n", - " 'En thalaivan yogi babu irukkaar. Padam vera level laa irukkum',\n", - " 'Nerkonda parvai... Sema sema sema trailer',\n", - " 'ஏய் இது 96 யார் ஏமாத்தறீங்க? செம பின்னிட்டீங்க',\n", - " 'படம் கண்டிப்பாக வெற்றி பெற வேண்டும் செம்ம vara level',\n", - " 'Correct. Enga apa military da oodi vilaiyada solli tharuvaaaayaa',\n", - " 'Dislike pottavan ratham khaki dha savan',\n", - " 'ANANDHI ARMY HIT LIKE!! Ennayum ipdi aakitingale Anandi... #AnbukkuNanAdimai',\n", - " 'Thalaivaa mass nadipin nayagan surya anna fans hits like',\n", - " 'இந்த படம் கண்டிப்பாக வெற்றியடைய வேண்டும் என வாழ்த்துகிறேன்....',\n", - " 'Hit like if anybody laughed during this 1.06 .... Thaamasuuuu!!',\n", - " 'I Love vijay Anna, u r so cute anna',\n", - " 'Dei Rajini pavam da ne varaven poraven ellam karithuppatha koraiya pesittu erukkan 1:58',\n", - " 'செட்டியார் இன மக்கள் சார்பாக எனது சொந்த செலவில் 100 டிக்கெட் இலவசமாக வழங்குகிறேன். படக்குழுவினர்க்கு எனது மனமார்ந்த வாழ்த்துக்கள்',\n", - " 'Dhanush fans hit like here.....',\n", - " 'Dey dey deyyy,, loosu pasangala,, munna pinna jayalalitha amma va pathrukingalada gambeeramna ennanu avanga kannula tha paakanum,, amma oda history ah ithu,, soniya gandhi mari iruku chaiiii,,, avanga bio edukanumna a-z pathutu vanthu edungadaa,, dummy akkaathinga']" - ] - }, - "metadata": {}, - "execution_count": 32 - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 4.73 ms (started: 2022-02-24 05:16:55 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_encodings = tokenizer(train_texts,truncation=True,max_length=128)\n", - "dev_encodings = tokenizer(dev_texts,truncation=True,max_length=128)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Slj1XFSODKSv", - "outputId": "517b068f-2ffc-4504-d724-70c78964863c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 5.05 s (started: 2022-02-24 05:17:24 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "\n", - "class TaskDatasetB(torch.utils.data.Dataset):\n", - "\n", - " def __init__(self,encodings,labels):\n", - " self.encodings = encodings\n", - " self.labels = labels\n", - "\n", - " def __getitem__(self,idx):\n", - " item = {key: torch.tensor(val[idx]) for key,val in self.encodings.items()}\n", - " item['labels'] = torch.tensor(self.labels[idx])\n", - " return item\n", - "\n", - " def __len__(self):\n", - " return len(self.labels)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vRvVCWTbDMnb", - "outputId": "a18590b4-4b96-4d5f-dd04-f2ad61212d0d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 18.5 ms (started: 2022-02-24 05:17:33 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "train_dataset = TaskDatasetB(train_encodings,y_train)\n", - "dev_dataset = TaskDatasetB(dev_encodings,y_dev)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0r1oAyXYDTKs", - "outputId": "6dc0d52c-91d8-4b9d-83f6-55f0628ab504" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 6.22 ms (started: 2022-02-24 05:17:37 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cnYpYx3yDWWO", - "outputId": "97abca15-eb74-4080-f692-6c5b6af23c3b" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 2.19 ms (started: 2022-02-24 05:17:40 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import TrainingArguments\n", - "\n", - "training_args = TrainingArguments(\n", - " 'OGBV-gender-indicbert-ta-eacl_finals',\n", - " run_name = \"twt-indicbert-ta-5epoch\",\n", - " evaluation_strategy = 'epoch',\n", - " save_strategy='epoch',\n", - " num_train_epochs = 3,\n", - " learning_rate=2e-5,\n", - " weight_decay=0.01, \n", - " load_best_model_at_end = True,\n", - " push_to_hub='OGBV-gender-indicbert-ta-eacl_finals'\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lYc1a44kDaLY", - "outputId": "3963e9d2-dd17-4b4f-9fce-5289caa5ca21" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 80.3 ms (started: 2022-02-24 05:19:13 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import accuracy_score,f1_score,precision_score,recall_score\n", - "\n", - "def compute_metrics(eval_preds):\n", - "\n", - " logits,labels = eval_preds\n", - " predictions = np.argmax(logits,axis=-1)\n", - "\n", - " acc = accuracy_score(labels,predictions)\n", - " precision = precision_score(labels,predictions,average='weighted')\n", - " recall = recall_score(labels,predictions,average='weighted')\n", - " f1 = f1_score(labels,predictions,average='weighted')\n", - " \n", - " return {\n", - " 'acc' : acc,\n", - " 'precision' : precision,\n", - " 'recall' : recall,\n", - " 'f1' : f1\n", - " }" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nAN5uVqTDgzO", - "outputId": "0dec277a-ec10-4c19-c0a9-53aa4ff9dcbb" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 28 ms (started: 2022-02-24 05:19:18 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!apt-get install git-lfs\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "w5uRI8BKDi_I", - "outputId": "4a3b9913-5564-4e07-c994-ba91661b2837" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Reading package lists... Done\n", - "Building dependency tree \n", - "Reading state information... Done\n", - "The following package was automatically installed and is no longer required:\n", - " libnvidia-common-470\n", - "Use 'apt autoremove' to remove it.\n", - "The following NEW packages will be installed:\n", - " git-lfs\n", - "0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n", - "Need to get 2,129 kB of archives.\n", - "After this operation, 7,662 kB of additional disk space will be used.\n", - "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n", - "Fetched 2,129 kB in 2s (868 kB/s)\n", - "Selecting previously unselected package git-lfs.\n", - "(Reading database ... 155320 files and directories currently installed.)\n", - "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n", - "Unpacking git-lfs (2.3.4-1) ...\n", - "Setting up git-lfs (2.3.4-1) ...\n", - "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n", - "time: 7.68 s (started: 2022-02-24 05:19:20 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import Trainer\n", - "\n", - "trainer = Trainer(\n", - " model = model,\n", - " args = training_args,\n", - " train_dataset = train_dataset,\n", - " eval_dataset = dev_dataset,\n", - " data_collator = data_collator,\n", - " compute_metrics = compute_metrics,\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "c3DELw-GDjjd", - "outputId": "b1ac93f9-f06c-4655-9e0b-4a7686a12d2d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:1004: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n", - " FutureWarning,\n", - "Cloning https://huggingface.co/Maha/OGBV-gender-indicbert-ta-eacl_finals into local empty directory.\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 22.3 s (started: 2022-02-24 05:19:47 +00:00)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "trainer.train()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 745 - }, - "id": "6izJd1eUDlLT", - "outputId": "4b581e56-3a8f-40d8-aebc-eefe49cffab3" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "***** Running training *****\n", - " Num examples = 33685\n", - " Num Epochs = 3\n", - " Instantaneous batch size per device = 8\n", - " Total train batch size (w. parallel, distributed & accumulation) = 8\n", - " Gradient Accumulation steps = 1\n", - " Total optimization steps = 12633\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "
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] - }, - "metadata": {}, - "execution_count": 46 - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "time: 19.1 s (started: 2022-02-24 06:09:54 +00:00)\n" - ] - } - ] - } - ] -} \ No newline at end of file diff --git a/pooling/stratified_pooling/eng_pooling_final.ipynb b/pooling/stratified_pooling/eng_pooling_final.ipynb deleted file mode 100644 index 424f2fe2..00000000 --- a/pooling/stratified_pooling/eng_pooling_final.ipynb +++ /dev/null @@ -1,1596 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "eng_pooling_final.ipynb", - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "N8Pu8-psjLaP", - "outputId": "7e71a5f6-72f6-4b80-afba-7b97d0f10005" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd" - ], - "metadata": { - "id": "X0J4OS9CjZuo" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np" - ], - "metadata": { - "id": "H3mWjwltjxhE" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "dff = pd.read_csv(\"/content/drive/MyDrive/eng_conf_full.csv\",lineterminator=\"\\n\")\n" - ], - "metadata": { - "id": "j_lq7qGE4Cy-" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "tot=pd.read_csv('/content/drive/MyDrive/total_data_twitter.csv',lineterminator=\"\\n\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sl3gHfMYUaI7", - "outputId": "1328c0b9-f7e7-48a1-8996-924743f0d69b" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (11,17,24,31) have mixed types.Specify dtype option on import or set low_memory=False.\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "tot=tot[['_id','language','content_type']]" - ], - "metadata": { - "id": "3ksMuTvkQmfw" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df=dff.merge(tot, on='_id', how='inner')\n" - ], - "metadata": { - "id": "MySnHizIQmqf" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df=df.loc[df['content_type'] == 'text']\n" - ], - "metadata": { - "id": "mHMTfPbzQt0d" - }, - "execution_count": 8, - "outputs": [] - }, - 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Unnamed: 0_idtweet_idtweeteng_cnerg_confidenceeng_trac1_confidenceeng_founta_confidenceeng_hateeval_confidenceeng_offenseeval_confidencelanguagecontent_typemean_all
6287628761c0336ce7958c0f42f015421301225127871479813To the bitch who tried merging INTO my car, I ...0.860.970.990.970.95entext0.948
126771267761c1e01478e60889d95c6aa51372253879908372483Your white ass can go to hell. Stupid bitch0.870.970.990.960.95entext0.948
25040425040461e519642584e2832c8b51b71079527183436132352Fuck that whore. She ain’t shit. When I come o...0.890.970.990.960.93entext0.948
6612661261c033c1301de3f6a4c397d81471716113109168129This nerd bitch doesn't even lift. What a faggot.0.900.970.990.930.94entext0.946
7203720361c0348ae7958c0f42f018ef1240704795637198849I really rue the day any one of these whores b...0.870.970.990.960.94entext0.946
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30612130612161cbfabf68b8e44d2c3a48d81211992266581172224Thank you so much guys. Happy new year 🤗. ☮️0.030.040.010.020.02entext0.024
27677227677261c2336a0c7c664fcf0d58121377268746327269379I am officially closing down this Page, It's b...0.030.050.010.010.02entext0.024
30385430385461cbf8d634ebea570c110e711218643689670684674Thank you, everyone! 🌹🌱✨0.030.040.010.020.02entext0.024
30349630349661cbf89268b8e44d2c3a45ff1340025088544894976Thank you 💕💞see you soon0.020.040.010.030.02entext0.024
30583730583761cbfa7d68b8e44d2c3a48821218040879354540032You were amazing! 👏🏼❣️🌹0.020.040.010.020.02entext0.022
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[]\n", - "\n", - "[8000 rows x 5 columns]" - ] - }, - "metadata": {}, - "execution_count": 36 - } - ] - }, - { - "cell_type": "code", - "source": [ - "rslt.to_csv('eng_pooled_new.csv',index=False)" - ], - "metadata": { - "id": "pkbEUDmt8EtH" - }, - "execution_count": 38, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/pooling/stratified_pooling/hin_pooling_final.ipynb b/pooling/stratified_pooling/hin_pooling_final.ipynb deleted file mode 100644 index b6c6a20d..00000000 --- a/pooling/stratified_pooling/hin_pooling_final.ipynb +++ /dev/null @@ -1,1882 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "hin_pooling_final.ipynb", - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "N8Pu8-psjLaP", - "outputId": "57fc1081-f630-4199-c81d-c5e0f8207479" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd" - ], - "metadata": { - "id": "X0J4OS9CjZuo" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np" - ], - "metadata": { - "id": "H3mWjwltjxhE" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "dff = pd.read_csv(\"/content/drive/MyDrive/full_inference/hin/hin_conf_full.csv\",lineterminator=\"\\n\")\n" - ], - "metadata": { - "id": "j_lq7qGE4Cy-" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "tot=pd.read_csv('/content/drive/MyDrive/total_data_twitter.csv',lineterminator=\"\\n\")" - ], - "metadata": { - "colab": { - 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11225711225761cb45341d947ed5f33318f11445788990741749760भोसडीके\" जय हींद \" जो तुने राजनीती कर रहा है।य...1.0भोस्डीके1.00.910.89hitext0.95
941029410261cb3233a144172bdda52b7a1266365792091701251एक नया मादरजात पैदा हुआ है जिसका नाम है दीपक झ...1.0मादरजात1.00.910.89hitext0.95
17320017320061e51a460843dffd91b229b61076287904937324545@RKhatri14 मुल्ले पाकिस्तान जाये और हिन्दू तो ...1.0कुत्ती1.00.910.89hitext0.95
10597310597361cb4008874027a5e071797e1219292002795180032#SecularMaskOfd हमे पता नहि क्या कहते है ,इतना...1.0हरामी1.00.910.89hitext0.95
.......................................
24056124056161e18a7672115c8f12abad091185508072875184128@PoonamHockey शुभकामनाएं 💐💐💐💐💐0.0PoonamHockey0.00.140.02hitext0.04
22643322643361e1155fcc498f75d018733c1419250418807316480@sanaak21 @AshrafZohoor आपकी राय? https://t.c...0.0sanaak210.00.140.02hitext0.04
23058323058361e124b2d27c0a6f757eb6ac1343916664837066752@mangeshkarlata 💐💐कोटी कोटी नमन 🙏🙏0.0mangeshkarlata0.00.140.02hitext0.04
23674223674261e137391bdc4f8ef30a05ee1210074299140210688@realshooterdadi नमन सरदार जी🙏🙏0.0realshooterdadi0.00.140.02hitext0.04
23829623829661e13b6f05d2110784ed31011339467318850510849@realshooterdadi ओम शांति 🙏0.0realshooterdadi0.00.140.02hitext0.04
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237356 rows × 12 columns

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"outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df['mean_all'].plot(kind='hist')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 285 - }, - "id": "mbIE5UzWkCF_", - "outputId": "cda394bc-fdef-4885-a2a9-567614a83edb" - }, - "execution_count": 20, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 20 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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24541624541661e1a42dff8ef2dfd8f9e30f1189535643304517632@anitahasnandani सो कोल्ड ड्रामा😂😂😂0.03anitahasnandani0.030.140.02hitext0.06
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10601810601861cb400c874027a5e07179921219275480487956480@varnishant हरामी सिर्फ Brahman में ही नहीं ह...0.99हरामी1.000.850.87hitext0.93
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[]\n", - "\n", - "[8000 rows x 5 columns]" - ] - }, - "metadata": {}, - "execution_count": 39 - } - ] - }, - { - "cell_type": "code", - "source": [ - "rslt.to_csv('hin_pooled_new.csv',index=False)" - ], - "metadata": { - "id": "pkbEUDmt8EtH" - }, - "execution_count": 41, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/pooling/stratified_pooling/tam_pooling_final.ipynb b/pooling/stratified_pooling/tam_pooling_final.ipynb deleted file mode 100644 index 429d0e73..00000000 --- a/pooling/stratified_pooling/tam_pooling_final.ipynb +++ /dev/null @@ -1,1532 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "tam_pooling_final.ipynb", - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "N8Pu8-psjLaP", - "outputId": "b45fd4be-725a-43f4-bb28-93e92cf049d8" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd" - ], - "metadata": { - "id": "X0J4OS9CjZuo" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np" - ], - "metadata": { - "id": "H3mWjwltjxhE" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "dff = pd.read_csv(\"/content/drive/MyDrive/full_inference/tam/new/tam_conf_full.csv\",lineterminator=\"\\n\")\n" - ], - "metadata": { - "id": "j_lq7qGE4Cy-" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "tot=pd.read_csv('/content/drive/MyDrive/total_data_twitter.csv',lineterminator=\"\\n\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n_4XVZTEPN0h", - "outputId": "7dc1353e-487a-426d-f9f2-5188ec293868" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (11,17,24,31) have mixed types.Specify dtype option on import or set low_memory=False.\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "tot=tot[['_id','language','content_type']]" - ], - "metadata": { - "id": "XaziP0YiPOkt" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df=dff.merge(tot, on='_id', how='inner')" - ], - "metadata": { - "id": "CqiBXRjnPS6F" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df=df.drop(['tam_hasoc21codemix_confidence'], axis = 1)\n" - ], - "metadata": { - "id": "owmB93haU6M9" - }, - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df=df.drop_duplicates(subset=['tweet']) #remove suplicates based on tweet\n" - ], - "metadata": { - "id": "BGoFaKe17EeG" - }, - "execution_count": 11, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df=df.loc[df['content_type'] == 'text']\n" - ], - "metadata": { - "id": "dFXhi6bFPqQr" - }, - "execution_count": 13, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df.shape" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JpProNYE7Fd4", - "outputId": "382db9fd-0085-491f-f549-03957c574437" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(76960, 9)" - ] - }, - "metadata": {}, - "execution_count": 14 - } - ] - }, - { - "cell_type": "code", - "source": [ - "cols = ['tam_fire20_confidence','tam_eacl21_indic_confidence']\n", - "df['mean_all'] = df[cols].mean(axis=1)\n", - "df.sort_values(by='mean_all', ascending=False, inplace=True)" - ], - "metadata": { - "id": "cvpYxl3ijTiW" - }, - "execution_count": 16, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 939 - }, - "id": "tag2gR2jyXlR", - "outputId": "8a851c41-28c0-4662-e888-1a583ea7145f" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "
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219082190861c5afee7de587f16dcb4d611227190931637637121@OnlyVijayism @thalayinviswasi @ramk8059 @ramk...0.54ஊம்பு0.11tatext0.32
815008150061e10bc41cf48a3db760b5db1456037670782738436@priya_Bshankar அழகான தமிழ் பொண்ணு0.05priya_Bshankar0.12tatext0.08
342263422661c1952c3779a06d1c6584d31371869444452978691@SPK_TNCC அண்ணா வாழ்த்துகள், நீங்கள் அதிக வாக்...0.17அலி0.25tatext0.21
778837788361cc4c4fd43c3dbf473bebbb1346004524226396160@kaviyrasan நீங்க மற்றவர் ட்வீட்க்கு வருவதே அப...0.50_SUMI_Twitz_0.14tatext0.32
.................................
8647864761c5a95673e846c228c44e471329476154005147648ஏற்க்கனவே உங்களுக்கு புஸ்ஸி ஆனந்தா SAC யா னு ய...0.60புண்ட0.47tatext0.53
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160771607761c5ad6b7de587f16dcb44b91399698089510850565தலைவன் குஞ்சு மேல செம காண்டுல இருக்காங்க போல.....0.82ஊம்பு0.32tatext0.57
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815008150061e10bc41cf48a3db760b5db1456037670782738436@priya_Bshankar அழகான தமிழ் பொண்ணு0.05priya_Bshankar0.12tatext0.08<handle replaced>அழகான தமிழ் பொண்ணு
342263422661c1952c3779a06d1c6584d31371869444452978691@SPK_TNCC அண்ணா வாழ்த்துகள், நீங்கள் அதிக வாக்...0.17அலி0.25tatext0.21<handle replaced>அண்ணா வாழ்த்துகள், நீங்கள் அத...
778837788361cc4c4fd43c3dbf473bebbb1346004524226396160@kaviyrasan நீங்க மற்றவர் ட்வீட்க்கு வருவதே அப...0.50_SUMI_Twitz_0.14tatext0.32<handle replaced>நீங்க மற்றவர் ட்வீட்க்கு வருவ...
....................................
8647864761c5a95673e846c228c44e471329476154005147648ஏற்க்கனவே உங்களுக்கு புஸ்ஸி ஆனந்தா SAC யா னு ய...0.60புண்ட0.47tatext0.53ஏற்க்கனவே உங்களுக்கு புஸ்ஸி ஆனந்தா SAC யா னு ய...
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See more project details here:https://tattle.co.in/products/ogbv/ - -# Scraping methodology - -Open source library [instagram-scraper](https://github.com/arc298/instagram-scraper) is used for scraping,later on the images and videos are uploaded to aws -s3 buckets and finally the post meta-data along with s3-url is uploaded to mongo-db - -# Run locally - -1. Fork the Repository -2. Install required Python Packages `pip install requirements.txt` -3. Set up an AWS S3 bucket to store the scraped content (images, videos, text) and a MongoDB to store the scraped metadata - (timestamps, likes, tags ) -4. Create a .env file in the same folder and save your Instagram MongoDB collection and S3 access credentials in the .env file. These should be in the following format - - ``` - INSTAGRAM_DB_USERNAME = - INSTAGRAM_DB_PASSWORD = - INSTAGRAM_DB_NAME = - INSTAGRAM_DB_COLLECTION = - - AWS_ACCESS_KEY_ID = - AWS_SECRET_ACCESS_KEY_ID = - AWS_BUCKET = - AWS_USERNAME = - ``` - 5.Run the scraper as : `python dev_instagramscraper.py -u -m ` - -# Want to contribute to this repository? - -We have a [guide](docs/contributing.md) for you - -# Licence -When you submit code changes, your submissions are understood to be under the same licence that covers the project - GPL-3. Feel free to contact the maintainers if that's a concern. diff --git a/scrapers/Instagram/db_config.py b/scrapers/Instagram/db_config.py deleted file mode 100644 index c0db90ca..00000000 --- a/scrapers/Instagram/db_config.py +++ /dev/null @@ -1,17 +0,0 @@ -import os -from dotenv import load_dotenv -load_dotenv() - -twitter_db_config = { - "db_username" : os.environ.get("TWITTER_DB_USERNAME"), - "db_password" : os.environ.get("TWITTER_DB_PASSWORD"), - "db_name" : os.environ.get("TWITTER_DB_NAME"), - "db_collection" : os.environ.get("TWITTER_DB_COLLECTION") -} - -instagram_db_config = { - "db_username" : os.environ.get("INSTAGRAM_DB_USERNAME"), - "db_password" : os.environ.get("INSTAGRAM_DB_PASSWORD"), - "db_name" : os.environ.get("INSTAGRAM_DB_NAME"), - "db_collection" : os.environ.get("INSTAGRAM_DB_COLLECTION") -} \ No newline at end of file diff --git a/scrapers/Instagram/dev_instagramscraper.py b/scrapers/Instagram/dev_instagramscraper.py deleted file mode 100644 index f4c39bc5..00000000 --- a/scrapers/Instagram/dev_instagramscraper.py +++ /dev/null @@ -1,205 +0,0 @@ -import time -import argparse -import instagram_scraper as insta -import subprocess -import json -from dotenv import load_dotenv -load_dotenv() -import os -import boto3 -from pymongo import MongoClient -from db_config import twitter_db_config,instagram_db_config -import glob -import botocore -import uuid - -FOLDER = './data/instagram/' - -def scraper_util(userhandles,maxposts): - - """ - Takes a .txt file containing userhandles,max posts to scrape as input arguments and creates a json file - for a given user-handle containing all the required fields. - - Args: - userhandles (.txt file) : contains the userhandles to scrape - maxposts (int) : no. of posts to scrape,if set to MAX,scrapes all the posts - - Returns: - None - """ - - if maxposts=="MAX": - subprocess.run(["instagram-scraper","-f",userhandles,"--media-metadata","--media-types","none"]) - else: - subprocess.run(["instagram-scraper","-f",userhandles,"-m",maxposts,"--media-metadata","--media-types","none"]) - - time.sleep(1) - - with open(userhandles,'r') as users_file: - - users = users_file.readlines() - #print(users[0].strip('\n')) - - for user in users: - print(user) - - -def initialize_s3(): - - """ - Setup the s3 bucket and returns the aws-base-url,bucket-name ,aws-s3 object and aws-resource object - Args: - None - Returns: - aws base url,aws bucket,s3 bucket object,s3 resource object - """ - - aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID") - aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY_ID") - aws_username = os.environ.get("AWS_USERNAME") - aws_bucket = os.environ.get("AWS_BUCKET") - s3_client = boto3.client("s3", aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - s3_resource = boto3.resource('s3', aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - #print(list(s3_resource.buckets.all())) - #ogbv_bucket = s3_resource.Bucket(aws_bucket) #Find the list of files stored in the given bucket - #print(ogbv_bucket.objects.all()) - - return aws_username, aws_bucket, s3_client, s3_resource - -def upload_to_s3(s3,file,filename,bucket,content_type): - - """ - Upload the images/videos to s3-bucket - Args: - s3 (s3 client object) - file (.jpg/.mp4) - file to upload - filename - filename to store an uploaded file on s3 (or) Key - bucket - aws s3 bucket name - content_type - type of the data - - - Returns: - None - """ - - with open(file,"rb") as data: - s3.upload_fileobj(Fileobj = data, - Bucket = bucket, - Key = filename, - ExtraArgs = {'ContentType':content_type, - 'ACL':'public-read'}) - -def count_s3_files(s3, bucket): - - """ - Count the no. of files in the s3 bucket - - Args: - s3 - s3 client object - bucket (str) - s3 bucket name - Returns: - file_count (int) - count of files - """ - - paginator = s3.get_paginator('list_objects_v2') - pages = paginator.paginate(Bucket=bucket) - file_count = 0 - for page in pages: - for obj in page['Contents']: - file_count += 1 - return file_count - -def get_s3_url(aws_bucket,key): - - """ - Get the s3-url of the uploaded image/video - Args: - aws_bucket (str) - name of the s3 bucket - key - filename stored on s3 bucket - - Returns: - object_url - s3 url of the uploaded image/video - """ - - config = botocore.client.Config(region_name = 'ap-south-1',signature_version = botocore.UNSIGNED) - object_url = boto3.client('s3', config=config).generate_presigned_url('get_object', ExpiresIn=0, Params={'Bucket': aws_bucket, 'Key': key}) - print(object_url) - - -def initialize_mongo(data_config): - - """ - Takes the MongoDB config objects of a Tattle service and returns the service's MongoDB collection - Args: - data_config (dict): A config dictionary imported from db_config - Example: - from db_config import instagram_db_config - collection = initialize_mongo(instagram_db_config) - """ - - mongo_url = "mongodb+srv://"+data_config["db_username"]+":"+data_config["db_password"]+"@tattle-data-fkpmg.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE" - cli = MongoClient(mongo_url) - db = cli[data_config["db_name"]] - coll = db[data_config["db_collection"]] - print(coll.count_documents({})) - - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - - parser.add_argument( - "-u", - "--userhandles", - ) - - parser.add_argument( - "-m", - "--maxposts" - ) - - #Add hashtags parser - - args = parser.parse_args() - - if not os.path.exists(FOLDER): - os.makedirs(FOLDER) - - os.chdir(FOLDER) - - #Scrap Instagram posts of the given users - scraper_util(args.userhandles,args.maxposts) - - #Initialize S3 - aws_username,aws_bucket,s3_client,s3_resource = initialize_s3() - - #List of files to upload - - #Upload images/videos to s3 bucket - filename = str(uuid.uuid4()) - print(filename) - upload_to_s3(s3_client,'./ig_data/4n.jpg',filename,aws_bucket,'image') - - #Fetch the urls of uploaded - get_s3_url(aws_bucket,filename) - - #Count the no. of files in the s3 bucket - print(count_s3_files(s3_client,aws_bucket)) - - #Initialize Mongo - initialize_mongo(instagram_db_config) - - """ - Upload to Mongo - """ - - #buckets_count = s3_client.list_buckets() - #bucket_names = [i['Name'] for i in (buckets_count['Buckets'])] - #print(bucket_names) - #print((buckets_count['Buckets'])) \ No newline at end of file diff --git a/scrapers/Instagram/docs/contributing.md b/scrapers/Instagram/docs/contributing.md deleted file mode 100644 index 6667d3b0..00000000 --- a/scrapers/Instagram/docs/contributing.md +++ /dev/null @@ -1,47 +0,0 @@ -# Contributing - -Firstly, we are really grateful that you are considering contributing to Tattle. We welcome contributions of all sorts - filing a bug report, suggesting improvements, proposing new feature, adding documentations, writing tests etc. - -By contributing to Tattle, you are agreeing to our community guidelines (will be linked soon) - -Contributing to Tattle takes 4 easy steps - -1. 👋Say Hi -2. 🔨Do your thing -3. 📞Tell us -4. 🎉Celebrate - -## 👋Say Hi - -The very first thing you should do is letting us know that you are interested in contributing by the following means : - -1. If you are unsure about how to contribute, simply join our Slack and introduce yourself and mention what interests you about us. We'll reach out and assist you further. -2. If there's a particular improvement you want to suggest, or a bug you want to fix, simply create a Github Issue regarding it and we'll reach out to assist you further. - -## 🔨Do your thing - -The Running Locally section above should help you access and run the code. Once you are able to run the code locally, you can make the changes you want. Test the features and add appropriate documentation for it if needed. - -### Pair programming - -We offer pair programming sessions with community members to familiarize them with the product and the code base. This will give you an opportunity to clarify any doubts regarding the codebase and the features that interest you. - -## 📞Tell us - -All code changes happen via pull request. We use [Github Flow](https://guides.github.com/introduction/flow/). The easiest way to let us know if you want to combine your changes into the core code is to make a Pull Request (PR) - -In your PR, please mention the following : - -* What does this PR do? -* How do we test this PR? - -We don't strictly follow test driven development (TDD) but any contributions that include tests are greatly appreciated. - -## 🎉Celebrate - -We typically review a PR within 2-3 days. We might offer you some feedback to your PR and merge it! If you reached till this stage, Congratulations and join us afterwards for virtual coffee and tea on slack 🙂 - -# Licence - -When you submit code changes, your submissions are understood to be under the same licence that covers the project - GPL . Feel free to contact the maintainers if that's a concern. - diff --git a/scrapers/Instagram/notebooks/Instagram Scraper.ipynb b/scrapers/Instagram/notebooks/Instagram Scraper.ipynb deleted file mode 100644 index 64a35088..00000000 --- a/scrapers/Instagram/notebooks/Instagram Scraper.ipynb +++ /dev/null @@ -1,410 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "30e48e9b", - "metadata": {}, - "source": [ - "### Insta-scrape" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "7212197e", - "metadata": {}, - "outputs": [], - "source": [ - "test_profile = \"rhea_chakraborty\"" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "22bb7356", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting insta-scrape\n", - " Downloading insta_scrape-2.1.2-py3-none-any.whl (26 kB)\n", - "Requirement already satisfied: requests in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from insta-scrape) (2.26.0)\n", - "Requirement already satisfied: beautifulsoup4 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from insta-scrape) (4.10.0)\n", - "Requirement already satisfied: soupsieve>1.2 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from beautifulsoup4->insta-scrape) (2.2.1)\n", - "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from requests->insta-scrape) (2.0.6)\n", - "Requirement already satisfied: idna<4,>=2.5 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from requests->insta-scrape) (3.2)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from requests->insta-scrape) (1.26.6)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from requests->insta-scrape) (2021.5.30)\n", - "Installing collected packages: insta-scrape\n", - "Successfully installed insta-scrape-2.1.2\n" - ] - } - ], - "source": [ - "!pip3 install insta-scrape" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "174c1249", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages/instascrape/core/_static_scraper.py:136: MissingCookiesWarning: Request header does not contain cookies! It's recommended you pass at least a valid sessionid otherwise Instagram will likely redirect you to their login page.\n", - " MissingCookiesWarning\n" - ] - } - ], - "source": [ - "from instascrape import Profile\n", - "\n", - "prof = Profile('rhea_chakraborty')\n", - "prof.scrape()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "60db2699", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages/instascrape/core/_static_scraper.py:136: MissingCookiesWarning: Request header does not contain cookies! It's recommended you pass at least a valid sessionid otherwise Instagram will likely redirect you to their login page.\n", - " MissingCookiesWarning\n" - ] - } - ], - "source": [ - "recents = prof.get_recent_posts()\n", - "photos = [post for post in recents if not post.is_video]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d76c6ddd", - "metadata": {}, - "outputs": [], - "source": [ - "for post in photos:\n", - " fname = post.upload_date.strftime(\"%Y-%m-%d %Hh%Mm\")\n", - " post.download(f\"{fname}.jpg\")" - ] - }, - { - "cell_type": "markdown", - "id": "b87dd488", - "metadata": {}, - "source": [ - "#### with selenium" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "14243f99", - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages/instascrape/core/_static_scraper.py:136: MissingCookiesWarning: Request header does not contain cookies! It's recommended you pass at least a valid sessionid otherwise Instagram will likely redirect you to their login page.\n", - " MissingCookiesWarning\n" - ] - }, - { - "ename": "InstagramLoginRedirectError", - "evalue": "Instagram is redirecting you to the login page instead of the page you are trying to scrape. This could be occuring because you made too many requests too quickly or are not logged into Instagram on your machine. Try passing a valid session ID to the scrape method as a cookie to bypass the login requirement", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInstagramLoginRedirectError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_27933/3090233884.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# \"cookie\": f\"sessionid={SESSIONID};\"}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mjoe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mProfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_profile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mjoe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscrape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;31m# Scraping the posts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/nlp/lib/python3.7/site-packages/instascrape/core/_static_scraper.py\u001b[0m in \u001b[0;36mscrape\u001b[0;34m(self, mapping, keys, exclude, headers, inplace, session, webdriver)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0mscraped_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m \u001b[0mreturn_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_json_from_source\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 145\u001b[0m \u001b[0mflat_json_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflatten_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreturn_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"json_dict\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/nlp/lib/python3.7/site-packages/instascrape/core/_static_scraper.py\u001b[0m in \u001b[0;36m_get_json_from_source\u001b[0;34m(self, source, headers, session)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mjson_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson_dict_arr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_scrape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0mreturn_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"json_dict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/nlp/lib/python3.7/site-packages/instascrape/core/_static_scraper.py\u001b[0m in \u001b[0;36m_validate_scrape\u001b[0;34m(self, json_dict)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0mjson_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdetermine_json_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mjson_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"LoginAndSignupPage\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"LoginAndSignupPage\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mInstagramLoginRedirectError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 302\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mjson_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"HttpErrorPage\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"HttpErrorPage\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0msource_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"url\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"Source\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mInstagramLoginRedirectError\u001b[0m: Instagram is redirecting you to the login page instead of the page you are trying to scrape. This could be occuring because you made too many requests too quickly or are not logged into Instagram on your machine. Try passing a valid session ID to the scrape method as a cookie to bypass the login requirement" - ] - } - ], - "source": [ - "from selenium.webdriver import Chrome\n", - "from instascrape import Profile, scrape_posts\n", - "\n", - "# Creating our webdriver\n", - "webdriver = Chrome(\"/usr/bin/chromedriver\")\n", - "\n", - "# Scraping Joe Biden's profile\n", - "SESSIONID = '49595509200%3AtlMIcng6vl6xED%3A2'\n", - " \n", - "\n", - "#headers = {\"user-agent\": \"Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Mobile Safari/537.36 Edg/87.0.664.57\",\n", - "# \"cookie\": f\"sessionid={SESSIONID};\"}\n", - "joe = Profile(test_profile)\n", - "joe.scrape()\n", - "\n", - "# Scraping the posts\n", - "posts = joe.get_posts(webdriver=webdriver, login_first=True)\n", - "#posts = joe.get_posts(webdriver=webdriver)\n", - "scraped, unscraped = scrape_posts(posts, silent=False, pause=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "6a05826b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting instagramy\n", - " Downloading instagramy-4.4-py3-none-any.whl (20 kB)\n", - "Installing collected packages: instagramy\n", - "Successfully installed instagramy-4.4\n" - ] - } - ], - "source": [ - "!pip install instagramy\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "cf2face5", - "metadata": {}, - "outputs": [], - "source": [ - "from instagramy import InstagramUser" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a5843dac", - "metadata": {}, - "outputs": [], - "source": [ - "session_id = \"49595509200%3AtlMIcng6vl6xED%3A2\"" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "b93cca0c", - "metadata": {}, - "outputs": [], - "source": [ - "user = InstagramUser(test_profile,sessionid=session_id)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "02fa2eee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "user.is_verified" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "24fffa4a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(user.posts)" - ] - }, - { - "cell_type": "markdown", - "id": "e893d62a", - "metadata": {}, - "source": [ - "### Instagram-Scraper" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "d80ab680", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting instagram-scraper==1.9.0\n", - " Downloading instagram-scraper-1.9.0.tar.gz (35 kB)\n", - "Requirement already satisfied: requests>=2.18.4 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from instagram-scraper==1.9.0) (2.26.0)\n", - "Requirement already satisfied: tqdm>=3.8.0 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from instagram-scraper==1.9.0) (4.62.3)\n", - "Collecting moviepy>=1.0.0\n", - " Downloading moviepy-1.0.3.tar.gz (388 kB)\n", - "\u001b[K |████████████████████████████████| 388 kB 285 kB/s eta 0:00:01\n", - "\u001b[?25hCollecting decorator<5.0,>=4.0.2\n", - " Downloading decorator-4.4.2-py2.py3-none-any.whl (9.2 kB)\n", - "Collecting proglog<=1.0.0\n", - " Downloading proglog-0.1.9.tar.gz (10 kB)\n", - "Requirement already satisfied: numpy>=1.17.3 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from moviepy>=1.0.0->instagram-scraper==1.9.0) (1.21.2)\n", - "Collecting imageio<3.0,>=2.5\n", - " Downloading imageio-2.9.0-py3-none-any.whl (3.3 MB)\n", - "\u001b[K |████████████████████████████████| 3.3 MB 385 kB/s eta 0:00:01\n", - "\u001b[?25hCollecting imageio_ffmpeg>=0.2.0\n", - " Downloading imageio_ffmpeg-0.4.5-py3-none-manylinux2010_x86_64.whl (26.9 MB)\n", - "\u001b[K |████████████████████████████████| 26.9 MB 1.1 MB/s eta 0:00:011 |█▋ | 1.4 MB 1.1 MB/s eta 0:00:23\n", - "\u001b[?25hRequirement already satisfied: pillow in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from imageio<3.0,>=2.5->moviepy>=1.0.0->instagram-scraper==1.9.0) (8.3.2)\n", - "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from requests>=2.18.4->instagram-scraper==1.9.0) (2.0.6)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from requests>=2.18.4->instagram-scraper==1.9.0) (2021.5.30)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from requests>=2.18.4->instagram-scraper==1.9.0) (1.26.6)\n", - "Requirement already satisfied: idna<4,>=2.5 in /home/mlkorra/anaconda3/envs/nlp/lib/python3.7/site-packages (from requests>=2.18.4->instagram-scraper==1.9.0) (3.2)\n", - "Building wheels for collected packages: instagram-scraper, moviepy, proglog\n", - " Building wheel for instagram-scraper (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for instagram-scraper: filename=instagram_scraper-1.9.0-py3-none-any.whl size=35678 sha256=73c8b82d3048871456f63f94f519debcab73c72c9f4555b592ea1fd45065b080\n", - " Stored in directory: /home/mlkorra/.cache/pip/wheels/4a/28/cf/b0b93a08fc6d3e1a7a31ff99b93c00c466c5d2b07914cda791\n", - " Building wheel for moviepy (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for moviepy: filename=moviepy-1.0.3-py3-none-any.whl size=110744 sha256=1ee629d5ce36aa9b25bf6dc30ddcdd2a38501dfb44c9e5b2663a7ca3043758b3\n", - " Stored in directory: /home/mlkorra/.cache/pip/wheels/56/dc/2b/9cd600d483c04af3353d66623056fc03faed76b7518faae4df\n", - " Building wheel for proglog (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for proglog: filename=proglog-0.1.9-py3-none-any.whl size=6157 sha256=a106681ec6b469380ea04f27a074a878f05435af31de39808b9c077c7b960936\n", - " Stored in directory: /home/mlkorra/.cache/pip/wheels/12/36/1f/dc61e6ac10781d63cf6fa045eb09fa613a667384e12cb6e6e0\n", - "Successfully built instagram-scraper moviepy proglog\n", - "Installing collected packages: proglog, imageio-ffmpeg, imageio, decorator, moviepy, instagram-scraper\n", - " Attempting uninstall: decorator\n", - " Found existing installation: decorator 5.0.9\n", - " Uninstalling decorator-5.0.9:\n", - " Successfully uninstalled decorator-5.0.9\n", - "Successfully installed decorator-4.4.2 imageio-2.9.0 imageio-ffmpeg-0.4.5 instagram-scraper-1.9.0 moviepy-1.0.3 proglog-0.1.9\n" - ] - } - ], - "source": [ - "!pip install instagram-scraper==1.9.0" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b3c4833c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Searching mullo_ka_.jija for profile pic: 100% 1/1 [00:00<00:00, 2565.32 images/s]\n", - "Searching mullo_ka_.jija for posts: 20 media [00:01, 12.47 media/s]\n", - "Downloading: 100%|##########| 20/20 [00:03<00:00, 5.91it/s]\n" - ] - } - ], - "source": [ - "!instagram-scraper -f users.txt " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "2eeae66e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/bin/bash: instagram-scraper: command not found\r\n" - ] - } - ], - "source": [ - "!instagram-scraper -f users.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c6f3f746", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: instagram-scraper 1.10.2\r\n", - "Uninstalling instagram-scraper-1.10.2:\r\n", - " Successfully uninstalled instagram-scraper-1.10.2\r\n" - ] - } - ], - "source": [ - "!pip uninstall instagram-scraper --yes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21e9894e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "nlp", - "language": "python", - "name": "nlp" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/scrapers/Instagram/requirements.txt b/scrapers/Instagram/requirements.txt deleted file mode 100644 index 6e4c5916..00000000 --- a/scrapers/Instagram/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -pymongo==3.12.0 -boto3==1.18.60 -botocore==1.21.60 -instagram-scraper==1.9.0 \ No newline at end of file diff --git a/scrapers/Twitter/README.md b/scrapers/Twitter/README.md deleted file mode 100644 index 8bfd74f5..00000000 --- a/scrapers/Twitter/README.md +++ /dev/null @@ -1,46 +0,0 @@ -# Introduction - -This repository contains code for scraping publicly available data from targeted user handles,hashtags and keywords on Twitter https://twitter.com/ - -# Why are we scraping this data - -For the project on detecting and responding to online gender based violence, we are collecting data from Twitter that can be annotated and used for detection models. See more project details here:https://tattle.co.in/products/ogbv/ - -# Scraping methodology - -Open source library [twint](https://github.com/twintproject/twint/) is used for scraping,later on the images and videos are uploaded to aws -s3 buckets and finally the post meta-data along with s3-url is uploaded to mongo-db - -# Run locally - -1. Fork the Repository -2. Install required Python Packages `pip install requirements.txt`and follow the instructions in the [twint repo](https://github.com/twintproject/twint/) to install - twint library -4. Set up an AWS S3 bucket to store the scraped content (images, videos, text) and a MongoDB to store the scraped metadata - (timestamps, likes, tags ) -4. Create a .env file in the same folder and save your Twitter MongoDB collection and S3 access credentials in the .env file. These should be in the following format - - ``` - TWITTER_DB_USERNAME = - TWITTER_DB_PASSWORD = - TWITTER_DB_NAME = - TWITTER_DB_COLLECTION = - - AWS_ACCESS_KEY_ID = - AWS_SECRET_ACCESS_KEY_ID = - AWS_BUCKET = - AWS_USERNAME = - ``` - 5.Run the scraper as : `python dev_twitter_scraper.py -u -m ` - -# Wiki - -https://github.com/tattle-made/OGBV/wiki/Twitter-Scraper - - -# Want to contribute to this repository? - -We have a [guide](../Instagram/docs/contributing.md) for you. - -# Licence -When you submit code changes, your submissions are understood to be under the same licence that covers the project - GPL-3. Feel free to contact the maintainers if that's a concern. diff --git a/scrapers/Twitter/db_config.py b/scrapers/Twitter/db_config.py deleted file mode 100644 index c0db90ca..00000000 --- a/scrapers/Twitter/db_config.py +++ /dev/null @@ -1,17 +0,0 @@ -import os -from dotenv import load_dotenv -load_dotenv() - -twitter_db_config = { - "db_username" : os.environ.get("TWITTER_DB_USERNAME"), - "db_password" : os.environ.get("TWITTER_DB_PASSWORD"), - "db_name" : os.environ.get("TWITTER_DB_NAME"), - "db_collection" : os.environ.get("TWITTER_DB_COLLECTION") -} - -instagram_db_config = { - "db_username" : os.environ.get("INSTAGRAM_DB_USERNAME"), - "db_password" : os.environ.get("INSTAGRAM_DB_PASSWORD"), - "db_name" : os.environ.get("INSTAGRAM_DB_NAME"), - "db_collection" : os.environ.get("INSTAGRAM_DB_COLLECTION") -} \ No newline at end of file diff --git a/scrapers/Twitter/dev_twitter_datauploader.py b/scrapers/Twitter/dev_twitter_datauploader.py deleted file mode 100644 index f3b327ee..00000000 --- a/scrapers/Twitter/dev_twitter_datauploader.py +++ /dev/null @@ -1,405 +0,0 @@ -from ast import parse -from typing import Hashable -from requests.api import head -from dotenv import load_dotenv -load_dotenv() -import nest_asyncio -nest_asyncio.apply() -from pathlib import Path -from pymongo import MongoClient -import argparse -import twint -import time -import json -import glob -import os -import boto3 -import botocore -import requests -from uuid import uuid4 -from PIL import Image -from io import BytesIO -from db_config import twitter_db_config -import datetime -from tqdm import tqdm -import logging - -FOLDER = Path('./data/twitter/') - -headers = { - "User-Agent" : "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36", - "Content-Type": "text/html", -} - -def initialize_s3(): - - """ - Setup the s3 bucket and returns the aws-base-url,bucket-name ,aws-s3 object and aws-resource object - Args: - None - Returns: - aws base url,aws bucket,s3 bucket object,s3 resource object - """ - - aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID") - aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY_ID") - aws_username = os.environ.get("AWS_USERNAME") - aws_bucket = os.environ.get("AWS_BUCKET") - s3_client = boto3.client("s3", aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - s3_resource = boto3.resource('s3', aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - #print(list(s3_resource.buckets.all())) - - #print(ogbv_bucket.objects.all()) - - - #ogbv_bucket = s3_resource.Bucket(aws_bucket) #Find the list of files stored in the given bucket - # Delete all files - #ogbv_bucket.objects.delete() - - - # List of files - # allfiles = ogbv_bucket.objects.all() - # for file in allfiles: - # print(file.key) - - return aws_username, aws_bucket, s3_client, s3_resource - -def upload_to_s3(s3,file,filename,bucket,content_type): - - """ - Upload the images/videos to s3-bucket - Args: - s3 (s3 client object) - file (.jpg/.mp4) - file to upload - filename - filename to store an uploaded file on s3 (or) Key - bucket - aws s3 bucket name - content_type - type of the data - - - Returns: - None - """ - - # Implement Error Handling - try: - - with open(file,"rb") as data: - s3.upload_fileobj(Fileobj = data, - Bucket = bucket, - Key = filename, - ExtraArgs = {'ContentType':content_type, - 'ACL':'public-read'}) - - #print(f"{file} successfully uploaded to s3") - logging.info(f"{file} successfully uploaded to s3") - - except Exception as exc: - print(f'{exc}') - logging.info(f'{exc}') - -def get_s3_url(s3_client,aws_bucket,image_url): - - """ - Download the image/video from image_url,upload it to s3-bucket and get the s3-url of the uploaded image/video - Args: - #aws_bucket (str) - name of the s3 bucket - #key - filename stored on s3 bucket - image_url - image/video url of the image/video on twitter - - Returns: - object_url - s3 url of the uploaded image/video - """ - - s3_url = "" - - # check if the image_url exists for the tweet - if image_url: - - query = {"image_url": image_url} - - # check if the image exists on MongdDB - if coll.find_one(query): - #print("Image already exists on MongoDB") - logging.info("Image already exists on MongoDB") - - return -1 - - else: - #print("image url :",image_url) - logging.info(f"image url : {image_url}") - key = str(uuid4()) - filename = key + '.jpg' - - # download the image from the url and store locally - r = requests.get(image_url[0],headers=headers) - try: - - image = Image.open(BytesIO(r.content)) - except Exception as esc: - logging.info(f'Error : {esc}') - - images_path = FOLDER/'images' - - if not images_path.exists(): - os.makedirs(images_path) - - try: - image.save(images_path/filename) - except Exception as esc: - logging.info(f'Error : {esc}') - - #print("Image saved on local disk....") - logging.info("Image saved on local disk....") - upload_to_s3(s3_client,images_path/filename,key,aws_bucket,'image') - - config = botocore.client.Config(region_name = 'ap-south-1',signature_version = botocore.UNSIGNED) - s3_url = boto3.client('s3', config=config).generate_presigned_url('get_object', ExpiresIn=0, Params={'Bucket': aws_bucket, 'Key': key}) - #print(s3_url) - logging.info(f"s3_url : {s3_url}") - - return s3_url - -def count_docs(coll): - - """ - Count the no. of docs/posts in the mongodb collection - Args: - coll (str) - mongodb collection - """ - - print("Count of posts : ") - print(coll.count_documents({})) - logging.info(f"Count of posts : {coll.count_documents({})}") - -def initialize_mongo(data_config): - - """ - Takes the MongoDB config objects of a Tattle service and returns the service's MongoDB collection - Args: - data_config (dict): A config dictionary imported from db_config - Example: - from db_config import instagram_db_config - collection = initialize_mongo(instagram_db_config) - """ - - mongo_url = "mongodb+srv://"+ data_config["db_username"] + ":"+ data_config["db_password"]+"@tattle-data-fkpmg.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE" - cli = MongoClient(mongo_url) - db = cli[data_config["db_name"]] - coll = db[data_config["db_collection"]] - - # count the no of post in the collection - count_docs(coll) - - #coll.update_many({},{"$unset": {"retweet": ""}}) - #coll.delete_many({}) - - #cursor = coll.find({}) - # print("cursor") - # print(cursor) - # for document in cursor: - # print("krey") - # print(document.keys()) # print all fields of this document. - - return coll - -def upload_to_mongo(data,coll): - - """ - Upload posts metadata to mongodb - Args: - data (dict) : contains all the required fields - coll : upload to mongo-db collection - Returns: - None - """ - - # check if the tweet/post already exists on mongo-db using tweet_id - query = {"tweet_id": data['tweet_id']} - - if coll.find_one(query): - print("Tweet already exists on Mongodb") - logging.info("Tweet already exists on Mongodb") - - # coll.update_one( - # query, - # { - # query, - # ({"_id": b["_id"]}, {"$set": {"geolocCountry": myGeolocCountry}}) - - # } - # ) - - # coll.update_one(query, - # {"$set": {"report",data['repost']}} - # ) - - # print("Added repost field") - - else: - coll.insert_one(data) - -def find_content_type(thumbnail_link): - - if thumbnail_link: - content = thumbnail_link.split('/')[3] - - if content == "media": - content_type = "image" - - elif content == "tweet_video_thumb": - content_type = "gif" - - elif content == "ext_tw_video_thumb" or "amplify_video_thumb": - content_type = "video" - - logging.info(content) - - else: - content_type = "text" - - return content_type - -def change_folder(FOLDER): - - """ - Create dir(if doesn't exist) and change to that dir - Args: - FOLDER (Path Object) - folder to create - """ - - #if not os.path.exists(FOLDER): - if not FOLDER.exists(): - os.makedirs(FOLDER) - - # implement error handling - os.chdir(FOLDER) - -if __name__ == "__main__": - - #root_logger = logging.getLogger() - - #logger1 = logging.FileHandl - logging.basicConfig(filename='twitterdatauploader.log', level=logging.INFO) - - #startnote = 'Scraping started ' + str(time.strftime("%Y%m%d%H%M%S")) - #logging.info(startnote) - - aws_username,aws_bucket,s3_client,s3_resource = initialize_s3() - logging.info("S3 Initialized") - - coll = initialize_mongo(twitter_db_config) - logging.info("Mongo Initialized") - - count_docs(coll) - - PATH = os.getcwd() - print(PATH) - logging.info(PATH) - - #json_files = glob.glob(PATH + "/data/twitter/hashtags/14_முட்டாஉபி" + "/*.json",recursive=False) - #json_files = glob.glob(PATH + "/data/twitter/keyw0rds/2021_kumarikumari333" + "/*.json",recursive=False) - #json_files = glob.glob(PATH + "/data/twitter/handles/2021_aartichabria" + "/*.json",recursive=False) - #json_files = glob.glob(PATH + "/data/twitter/replies/**/" + "/*.json",recursive=False) - json_files = glob.glob(PATH + "/data/twitter/replies_influencers" + "/*.json",recursive=False) - - for json_file in tqdm((json_files)): - - #print(f'Uploading the posts from {json_file} file') - logging.info(f'Uploading the posts from {json_file} file') - - try: - - with open(json_file,'r') as f: - - posts = f.readlines() - posts = [post.strip('\n') for post in posts] - - len_posts = len(posts) - - print(f'No. of posts uploading : {len_posts}') - logging.info(f'No. of posts uploading : {len_posts}') - - mongodb_posts = [] - - search_note = json_file.split('/')[-1][10:-5] - print(search_note) - with open('searchterms.txt','a') as txt: - txt.write(search_note) - txt.write("\n") - - for idx,post in enumerate(posts): - - mongo_dict = {} - post_dict = json.loads(post) - - mongo_dict['tweet_id'] = post_dict['id'] - mongo_dict['tweet'] = post_dict['tweet'] - mongo_dict['image_url'] = post_dict['photos'] - - s3_status = get_s3_url(s3_client,aws_bucket,post_dict['photos']) - - if s3_status==-1: - print("image already uploaded") - #continue - else: - mongo_dict['s3_url'] = s3_status - - mongo_dict['tweet_url'] = post_dict['link'] - mongo_dict['user_id'] = post_dict['user_id'] - mongo_dict['timestamp_of_creation'] = post_dict['created_at'] - mongo_dict['language'] = post_dict['language'] - mongo_dict['reply'] = post_dict['reply_to'] - mongo_dict['repost'] = post_dict['retweet'] - - mongo_dict['timestamp_of_scraping'] = time.strftime("%Y%m%d%H%M%S") - mongo_dict['content_type'] = find_content_type(post_dict['thumbnail']) - - # #latest - mongo_dict['conversation_id'] = post_dict['conversation_id'] - mongo_dict['date_created'] = post_dict['date'] - mongo_dict['time_created'] = post_dict['time'] - mongo_dict['timezone'] = post_dict['timezone'] - mongo_dict['username'] = post_dict['username'] - mongo_dict['name'] = post_dict['name'] - mongo_dict['place'] = post_dict['place'] - mongo_dict['mentions'] = post_dict['mentions'] - mongo_dict['urls'] = post_dict['urls'] - mongo_dict['replies_count'] = post_dict['replies_count'] - mongo_dict['retweets_count'] = post_dict['retweets_count'] - mongo_dict['likes_count'] = post_dict['likes_count'] - mongo_dict['hashtags'] = post_dict['hashtags'] - mongo_dict['quote_url'] = post_dict['quote_url'] - mongo_dict['near'] = post_dict['near'] - mongo_dict['video'] = post_dict['video'] - mongo_dict['geo'] = post_dict['geo'] - mongo_dict['source'] = post_dict['source'] - mongo_dict['user_rt_id'] = post_dict['user_rt_id'] - mongo_dict['user_rt'] = post_dict['user_rt'] - mongo_dict['retweet_id'] = post_dict['retweet_id'] - mongo_dict['retweet_date'] = post_dict['retweet_date'] - mongo_dict['translate'] = post_dict['translate'] - mongo_dict['trans_src'] = post_dict['trans_src'] - mongo_dict['trans_dest'] = post_dict['trans_dest'] - - # #update - # #mongo_dict['type'] = 'hashtag' - # #mongo_dict['type'] = 'keyword' - - mongo_dict['type'] = 'reply' - mongo_dict['search'] = json_file.split('/')[-1][10:-5] - - logging.info(f"Search : {mongo_dict['search']}") - post_count_note = str(idx) + "/" + str(len_posts) - logging.info(f"Post : {post_count_note}") - logging.info(f'{mongo_dict}') - - upload_to_mongo(mongo_dict,coll) - - count_docs(coll) - - except Exception as exc: - logging.info(exc) \ No newline at end of file diff --git a/scrapers/Twitter/dev_twitterscraper.py b/scrapers/Twitter/dev_twitterscraper.py deleted file mode 100644 index 838eb91c..00000000 --- a/scrapers/Twitter/dev_twitterscraper.py +++ /dev/null @@ -1,468 +0,0 @@ -from ast import parse -from typing import Hashable -from requests.api import head -from dotenv import load_dotenv -load_dotenv() -import nest_asyncio -nest_asyncio.apply() -from pathlib import Path -from pymongo import MongoClient -import argparse -import twint -import time -import json -import glob -import os -import boto3 -import botocore -import requests -from uuid import uuid4 -from PIL import Image -from io import BytesIO -from db_config import twitter_db_config -import datetime - -FOLDER = Path('./data/twitter/') - -headers = { - "User-Agent" : "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36", - "Content-Type": "text/html", -} - -def initialize_s3(): - - """ - Setup the s3 bucket and returns the aws-base-url,bucket-name ,aws-s3 object and aws-resource object - Args: - None - Returns: - aws base url,aws bucket,s3 bucket object,s3 resource object - """ - - aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID") - aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY_ID") - aws_username = os.environ.get("AWS_USERNAME") - aws_bucket = os.environ.get("AWS_BUCKET") - s3_client = boto3.client("s3", aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - s3_resource = boto3.resource('s3', aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - #print(list(s3_resource.buckets.all())) - - #print(ogbv_bucket.objects.all()) - - - #ogbv_bucket = s3_resource.Bucket(aws_bucket) #Find the list of files stored in the given bucket - # Delete all files - #ogbv_bucket.objects.delete() - - - # List of files - # allfiles = ogbv_bucket.objects.all() - # for file in allfiles: - # print(file.key) - - return aws_username, aws_bucket, s3_client, s3_resource - -def upload_to_s3(s3,file,filename,bucket,content_type): - - """ - Upload the images/videos to s3-bucket - Args: - s3 (s3 client object) - file (.jpg/.mp4) - file to upload - filename - filename to store an uploaded file on s3 (or) Key - bucket - aws s3 bucket name - content_type - type of the data - - - Returns: - None - """ - - # Implement Error Handling - try: - - with open(file,"rb") as data: - s3.upload_fileobj(Fileobj = data, - Bucket = bucket, - Key = filename, - ExtraArgs = {'ContentType':content_type, - 'ACL':'public-read'}) - - print(f"{file} successfully uploaded to s3") - - except Exception as exc: - print(f'{exc}') - -def get_s3_url(s3_client,aws_bucket,image_url): - - """ - Download the image/video from image_url,upload it to s3-bucket and get the s3-url of the uploaded image/video - Args: - #aws_bucket (str) - name of the s3 bucket - #key - filename stored on s3 bucket - image_url - image/video url of the image/video on twitter - - Returns: - object_url - s3 url of the uploaded image/video - """ - - s3_url = "" - - # check if the image_url exists for the tweet - if image_url: - - query = {"image_url": image_url} - - # check if the image exists on MongdDB - if coll.find_one(query): - print("Image already exists on MongoDB") - - else: - print("image url :",image_url) - - key = str(uuid4()) - filename = key + '.jpg' - - # download the image from the url and store locally - r = requests.get(image_url[0],headers=headers) - try: - - image = Image.open(BytesIO(r.content)) - except Exception as esc: - print(f'Error : {esc}') - - images_path = FOLDER/'images' - - if not images_path.exists(): - os.makedirs(images_path) - - try: - image.save(images_path/filename) - except Exception as esc: - print(f'Error : {esc}') - - print("Image saved on local disk....") - - upload_to_s3(s3_client,images_path/filename,key,aws_bucket,'image') - - config = botocore.client.Config(region_name = 'ap-south-1',signature_version = botocore.UNSIGNED) - s3_url = boto3.client('s3', config=config).generate_presigned_url('get_object', ExpiresIn=0, Params={'Bucket': aws_bucket, 'Key': key}) - print(s3_url) - - return s3_url - -def count_docs(coll): - - """ - Count the no. of docs/posts in the mongodb collection - Args: - coll (str) - mongodb collection - """ - - print("Count of posts : ") - print(coll.count_documents({})) - -def initialize_mongo(data_config): - - """ - Takes the MongoDB config objects of a Tattle service and returns the service's MongoDB collection - Args: - data_config (dict): A config dictionary imported from db_config - Example: - from db_config import instagram_db_config - collection = initialize_mongo(instagram_db_config) - """ - - mongo_url = "mongodb+srv://"+ data_config["db_username"] + ":"+ data_config["db_password"]+"@tattle-data-fkpmg.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE" - cli = MongoClient(mongo_url) - db = cli[data_config["db_name"]] - coll = db[data_config["db_collection"]] - - # count the no of post in the collection - count_docs(coll) - - #coll.update_many({},{"$unset": {"retweet": ""}}) - #coll.delete_many({}) - - #cursor = coll.find({}) - # print("cursor") - # print(cursor) - # for document in cursor: - # print("krey") - # print(document.keys()) # print all fields of this document. - - return coll - -def upload_to_mongo(data,coll): - - """ - Upload posts metadata to mongodb - Args: - data (dict) : contains all the required fields - coll : upload to mongo-db collection - Returns: - None - """ - - # check if the tweet/post already exists on mongo-db using tweet_id - query = {"tweet_id": data['tweet_id']} - - if coll.find_one(query): - print("Tweet already exists on Mongodb") - - # coll.update_one( - # query, - # { - # query, - # ({"_id": b["_id"]}, {"$set": {"geolocCountry": myGeolocCountry}}) - - # } - # ) - - # coll.update_one(query, - # {"$set": {"report",data['repost']}} - # ) - - # print("Added repost field") - - else: - coll.insert_one(data) - -from datetime import date, timedelta - -def daterange(start_date, end_date): - for n in range(int((end_date - start_date).days)): - yield start_date + timedelta(n) - -# start_date = date(2020, 10, 2) -# end_date = date(2020, 10, 5) -# for single_date in daterange(start_date, end_date): -# until = single_date.strftime("%Y-%m-%d") -# print(type(until)) -# print(single_date.strftime("%Y-%m-%d")) - -def scraper_util(hashtag,handle,keyword,limit): - - """ - Scraper utility func;contains all the configurations reqd for scraping - Args: - hashtag (str): hashtag to scrape - handle (str): user handle to scrape - keyword (str): keyword to scrape - limit (int): no. of tweets to scrape - Returns: - None - """ - - - timestr = time.strftime("%Y%m%d%H%M%S") - - c = twint.Config() - c.Limit = limit - if not limit: - c.Limit = 100 - - #c.Format = "ID {id}" - c.Store_json = True - c.Hide_output = True - #c.Since = "2021-01-01" - #c.Until = "2021-10-20" - #c.Until = "2020-12-30" - #c.Until = "2020-12-24" - #c.Until = "2020-12-21" - #c.Until = "2020-12-17" - #c.Until = "2020-12-11" - #c.Until = "2020-12-06" - #c.Until = "2020-12-02" - #c.Since = "2020-11-25" - #c.Until = "2020-10-07" - c.Until = "2020-09-23" - print("c.until") - print(c.Until) - - - if hashtag: - print("Hashtag") - print(hashtag) - c.Show_hashtags = True - c.Search = "#" + hashtag - c.Output = timestr + hashtag + ".json" - - elif handle: - print("Handle") - print(handle) - c.Username = handle - c.Output = timestr + handle + c.Until + ".json" - - elif keyword: - print("keyword") - print(keyword) - c.Show_hashtags = False - c.Search = keyword - c.Output = timestr + keyword + ".json" - - twint_util = twint.run.Search(c) - -def run_scraper(hashtags,handles,keywords,limit): - - """ - Scraper function to scrape tweets based on hastags,handles,keywords - Args: - hastags - - """ - - # Implement reading the search items from a file - if hashtags: - #change_folder(FOLDER/'hashtags') - hashtags = hashtags.split(',') - for hashtag in hashtags: - scraper_util(hashtag,handles,keywords,limit) - - elif handles: - #change_folder(FOLDER/'handles') - handles = handles.split(',') - for handle in handles: - scraper_util(hashtags,handle,keywords,limit) - - elif keywords: - #change_folder(FOLDER/'keywords') - for keyword in keywords: - scraper_util(hashtags,handles,keyword,limit) - -def find_content_type(thumbnail_link): - - if thumbnail_link: - content = thumbnail_link.split('/')[3] - - if content == "media": - content_type = "image" - - elif content == "tweet_video_thumb": - content_type = "gif" - - elif content == "ext_tw_video_thumb" or "amplify_video_thumb": - content_type = "video" - - print(content) - - else: - content_type = "text" - - return content_type - -def change_folder(FOLDER): - - """ - Create dir(if doesn't exist) and change to that dir - Args: - FOLDER (Path Object) - folder to create - """ - - #if not os.path.exists(FOLDER): - if not FOLDER.exists(): - os.makedirs(FOLDER) - - # implement error handling - os.chdir(FOLDER) - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - - parser.add_argument( - "-t", - "--hashtags", - type=str - ) - - parser.add_argument( - "-u", - "--handles", - type=str - ) - - parser.add_argument( - "-k", - "--keywords", - type=str - ) - - parser.add_argument( - "-m", - "--max", - type=str - ) - - args = parser.parse_args() - - start_date = date(2020, 9, 1) - end_date = date(2020, 9, 30) - - # for single_date in daterange(start_date, end_date): - # until = single_date.strftime("%Y-%m-%d") - # print(until) - # time.sleep(1) - #run_scraper(args.hashtags,args.handles,args.keywords,args.max) - #run_scraper(args.hashtags,args.handles,args.keywords) - - aws_username,aws_bucket,s3_client,s3_resource = initialize_s3() - - coll = initialize_mongo(twitter_db_config) - - count_docs(coll) - - # #json_file = 'data/twitter/hashtags/20211021194333RheaChakraborty.json' - - PATH = os.getcwd() - print(PATH) - - json_files = glob.glob(PATH + "/sansbarrier2020" + "/*.json",recursive=False) - - for json_file in json_files: - print(json_file) - #break - - print(f'Uploading the posts from {json_file} file') - - with open(json_file,'r') as f: - - posts = f.readlines() - posts = [post.strip('\n') for post in posts] - - print(f'No. of posts uploading : {len(posts)}') - - mongodb_posts = [] - - for post in posts: - - mongo_dict = {} - post_dict = json.loads(post) - - #if post_dict['photos']: - - mongo_dict['tweet_id'] = post_dict['id'] - mongo_dict['tweet'] = post_dict['tweet'] - mongo_dict['image_url'] = post_dict['photos'] - mongo_dict['s3_url'] = get_s3_url(s3_client,aws_bucket,post_dict['photos']) - mongo_dict['tweet_url'] = post_dict['link'] - mongo_dict['user_id'] = post_dict['user_id'] - mongo_dict['timestamp_of_creation'] = post_dict['created_at'] - mongo_dict['language'] = post_dict['language'] - mongo_dict['reply'] = 1 if post_dict['reply_to'] else 0 - mongo_dict['repost'] = 1 if post_dict['retweet'] else 0 - mongo_dict['timestamp_of_scraping'] = time.strftime("%Y%m%d%H%M%S") - mongo_dict['type'] = 'handle' - mongo_dict['search'] = 'sansbarrier' - mongo_dict['content_type'] = find_content_type(post_dict['thumbnail']) - print(mongo_dict) - - upload_to_mongo(mongo_dict,coll) - - count_docs(coll) - - \ No newline at end of file diff --git a/scrapers/Twitter/dev_twitterscraper_replies.py b/scrapers/Twitter/dev_twitterscraper_replies.py deleted file mode 100644 index bf7c0c80..00000000 --- a/scrapers/Twitter/dev_twitterscraper_replies.py +++ /dev/null @@ -1,347 +0,0 @@ -from ast import parse -from typing import Hashable -from requests.api import head -from dotenv import load_dotenv -load_dotenv() -import nest_asyncio -nest_asyncio.apply() -from pathlib import Path -from pymongo import MongoClient -import argparse -import twint -import time -import json -import glob -import os -import boto3 -import botocore -import requests -from uuid import uuid4 -from PIL import Image -from io import BytesIO -from db_config import twitter_db_config -from collections import Counter - -FOLDER = Path('./data/twitter/') - -headers = { - "User-Agent" : "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36", - "Content-Type": "text/html", -} - -# def initialize_s3(): - -# """ -# Setup the s3 bucket and returns the aws-base-url,bucket-name ,aws-s3 object and aws-resource object -# Args: -# None -# Returns: -# aws base url,aws bucket,s3 bucket object,s3 resource object -# """ - -# aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID") -# aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY_ID") -# aws_username = os.environ.get("AWS_USERNAME") -# aws_bucket = os.environ.get("AWS_BUCKET") -# s3_client = boto3.client("s3", aws_access_key_id = aws_access_key_id, -# aws_secret_access_key= aws_secret_access_key) - -# s3_resource = boto3.resource('s3', aws_access_key_id = aws_access_key_id, -# aws_secret_access_key= aws_secret_access_key) - -# #print(list(s3_resource.buckets.all())) -# #ogbv_bucket = s3_resource.Bucket(aws_bucket) #Find the list of files stored in the given bucket -# #print(ogbv_bucket.objects.all()) - -# return aws_username, aws_bucket, s3_client, s3_resource - -# def upload_to_s3(s3,file,filename,bucket,content_type): - -# """ -# Upload the images/videos to s3-bucket -# Args: -# s3 (s3 client object) -# file (.jpg/.mp4) - file to upload -# filename - filename to store an uploaded file on s3 (or) Key -# bucket - aws s3 bucket name -# content_type - type of the data - - -# Returns: -# None -# """ - -# # Implement Error Handling -# with open(file,"rb") as data: -# s3.upload_fileobj(Fileobj = data, -# Bucket = bucket, -# Key = filename, -# ExtraArgs = {'ContentType':content_type, -# 'ACL':'public-read'}) - -# print(f"{file} successfully uploaded to s3") - -# def get_s3_url(s3_client,aws_bucket,image_url): - -# """ -# Download the image/video from image_url,upload it to s3-bucket and get the s3-url of the uploaded image/video -# Args: -# #aws_bucket (str) - name of the s3 bucket -# #key - filename stored on s3 bucket -# image_url - image/video url of the image/video on twitter - -# Returns: -# object_url - s3 url of the uploaded image/video -# """ - -# s3_url = "" - -# # check if the image_url exists for the tweet -# if image_url: - -# query = {"image_url": image_url} - -# # check if the image exists on MongdDB -# if coll.find_one(query): -# print("Image already exists on MongoDB") - -# else: -# print("image url :",image_url) - -# key = str(uuid4()) -# filename = key + '.jpg' - -# # download the image from the url and store locally -# r = requests.get(image_url[0],headers=headers) -# image = Image.open(BytesIO(r.content)) - -# images_path = FOLDER/'images' - -# if not images_path.exists(): -# os.makedirs(images_path) - -# image.save(images_path/filename) -# print("Image saved on local disk....") - -# upload_to_s3(s3_client,images_path/filename,key,aws_bucket,'image') - -# config = botocore.client.Config(region_name = 'ap-south-1',signature_version = botocore.UNSIGNED) -# s3_url = boto3.client('s3', config=config).generate_presigned_url('get_object', ExpiresIn=0, Params={'Bucket': aws_bucket, 'Key': key}) -# print(s3_url) - -# return s3_url - -# def count_docs(coll): - -# """ -# Count the no. of docs/posts in the mongodb collection -# Args: -# coll (str) - mongodb collection -# """ - -# print("Count of posts : ") -# print(coll.count_documents({})) - -# def initialize_mongo(data_config): - -# """ -# Takes the MongoDB config objects of a Tattle service and returns the service's MongoDB collection -# Args: -# data_config (dict): A config dictionary imported from db_config -# Example: -# from db_config import instagram_db_config -# collection = initialize_mongo(instagram_db_config) -# """ - -# mongo_url = "mongodb+srv://"+ data_config["db_username"] + ":"+ data_config["db_password"]+"@tattle-data-fkpmg.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE" -# cli = MongoClient(mongo_url) -# db = cli[data_config["db_name"]] -# coll = db[data_config["db_collection"]] - -# # count the no of post in the collection -# count_docs(coll) - -# return coll - -# def upload_to_mongo(data,coll): - -# """ -# Upload posts metadata to mongodb -# Args: -# data (dict) : contains all the required fields -# coll : upload to mongo-db collection -# Returns: -# None -# """ - -# # check if the tweet/post already exists on mongo-db using tweet_id -# query = {"tweet_id": data['tweet_id']} - -# if coll.find_one(query): -# print("Tweet already exists on Mongodb") -# else: -# coll.insert_one(data) - -from datetime import date, timedelta - -def daterange(start_date, end_date): - for n in range(int((end_date - start_date).days)): - yield end_date - timedelta(n) - - -def scraper_util(hashtag,handle,keyword,limit,until): - - """ - Scraper utility func;contains all the configurations reqd for scraping - Args: - hashtag (str): hashtag to scrape - handle (str): user handle to scrape - keyword (str): keyword to scrape - limit (int): no. of tweets to scrape - Returns: - None - """ - - - - replies = twint.Config() - - if hashtag: - print("Hashtag") - print(hashtag) - " - - elif handle: - print("Handle") - print(handle) - - replies.Until = until - replies.Pandas = True - replies.To = '@' + handle - replies.Hide_output = True - replies.Store_json = True - replies.Output = until + handle + ".json" - - try: - twint.run.Search(replies) - #df = twint.storage.panda.Tweets_df - except Exception as exc: - print(f'{exc}') - - elif keyword: - print("keyword") - print(keyword) - - -def run_scraper(hashtags,handles,keywords,limit,until): - - """ - Scraper function to scrape tweets based on hastags,handles,keywords - Args: - hastags - - """ - - # Implement reading the search items from a file - if hashtags: - #change_folder(FOLDER/'hashtags') - hashtags = hashtags.split(',') - for hashtag in hashtags: - scraper_util(hashtag,handles,keywords,limit,until) - - elif handles: - - handles = handles.split(',') - for handle in handles: - scraper_util(hashtags,handle,keywords,limit,until) - - elif keywords: - - #change_folder(FOLDER/'keywords') - keywords = keywords.split(',') - for keyword in keywords: - scraper_util(hashtags,handles,keyword,limit,until) - -def change_folder(FOLDER): - - """ - Create dir(if doesn't exist) and change to that dir - Args: - FOLDER (Path Object) - folder to create - """ - - #if not os.path.exists(FOLDER): - if not FOLDER.exists(): - os.makedirs(FOLDER) - - # implement error handling - os.chdir(FOLDER) - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - - parser.add_argument( - "-t", - "--hashtags", - type=str - ) - - parser.add_argument( - "-u", - "--handles", - type=str - ) - - parser.add_argument( - "-k", - "--keywords", - type=str - ) - - parser.add_argument( - "-m", - "--max", - type=str - ) - - args = parser.parse_args() - - month_dict = {"January":(1,30),"February":(1,28),"March":(1,31),"April":(1,30),"May":(1,31),"June":(1,30),"July":(1,31),"August":(1,31),"September":(1,30),"October":(1,31),"November":(1,30),"December":(1,31)} - - - if args.hashtags: - - hasha = '2020_' +args.hashtags - new_folder = FOLDER/'hashtags'/hasha - - elif args.keywords: - - hasha = '202_' + args.keywords - new_folder = FOLDER/'keyw0rds'/hasha - - elif args.handles: - - hasha = '2020_' +args.handles - new_folder =FOLDER/'replies'/hasha - - if not new_folder.exists(): - os.makedirs(new_folder) - - os.chdir(new_folder) - -# # if args.hashtags or args.handles or args.keywords: - -# # if os.makedirs() - - for idx,month in enumerate(month_dict.values()): - #print(month[0]) - start_date = date(2020,idx+1,month[0]) - end_date = date(2020,idx+1,month[1]) - - for single_date in daterange(start_date, end_date): - until = single_date.strftime("%Y-%m-%d") - print(until) - time.sleep(1) - run_scraper(args.hashtags,args.handles,args.keywords,args.max,until) - \ No newline at end of file diff --git a/scrapers/Twitter/dev_twitterscraper_replies_until_since.py b/scrapers/Twitter/dev_twitterscraper_replies_until_since.py deleted file mode 100644 index ca26fa8f..00000000 --- a/scrapers/Twitter/dev_twitterscraper_replies_until_since.py +++ /dev/null @@ -1,223 +0,0 @@ -from ast import parse -from typing import Hashable -from requests.api import head -from dotenv import load_dotenv -load_dotenv() -import nest_asyncio -nest_asyncio.apply() -from pathlib import Path -from pymongo import MongoClient -import argparse -import twint -import time -import json -import glob -import os -import boto3 -import botocore -import requests -from uuid import uuid4 -from PIL import Image -from io import BytesIO -from db_config import twitter_db_config -from collections import Counter -import logging -import threading - -FOLDER = Path('./data/twitter/') - -headers = { - "User-Agent" : "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36", - "Content-Type": "text/html", -} - - -from datetime import date, timedelta - -def daterange(start_date, end_date): - for n in range(int((end_date - start_date).days)): - yield end_date - timedelta(n) - - -def scraper_util(hashtag,handle,keyword,limit,until,since): - - """ - Scraper utility func;contains all the configurations reqd for scraping - Args: - hashtag (str): hashtag to scrape - handle (str): user handle to scrape - keyword (str): keyword to scrape - limit (int): no. of tweets to scrape - Returns: - None - """ - - - - replies = twint.Config() - - if hashtag: - print("Hashtag") - print(hashtag) - - - elif handle: - print("Handle") - print(handle) - - - replies.Limit = limit - - replies.Until = until - replies.since = since - replies.Pandas = True - replies.To = '@' + handle - replies.Hide_output = True - replies.Store_json = True - replies.Output = until + handle + ".json" - - try: - twint.run.Search(replies) - #df = twint.storage.panda.Tweets_df - except Exception as exc: - print(f'{exc}') - - elif keyword: - print("keyword") - print(keyword) - - -def run_scraper(hashtags,handles,keywords,limit,until,since): - - """ - Scraper function to scrape tweets based on hastags,handles,keywords - Args: - hastags - - """ - - # Implement reading the search items from a file - if hashtags: - - hashtags = hashtags.split(',') - for hashtag in hashtags: - scraper_util(hashtag,handles,keywords,limit,until) - - elif handles: - - handles = handles.split(',') - for handle in handles: - scraper_util(hashtags,handle,keywords,limit,until,since) - - elif keywords: - - keywords = keywords.split(',') - for keyword in keywords: - scraper_util(hashtags,handles,keyword,limit,until) - -def change_folder(FOLDER): - - """ - Create dir(if doesn't exist) and change to that dir - Args: - FOLDER (Path Object) - folder to create - """ - - #if not os.path.exists(FOLDER): - if not FOLDER.exists(): - os.makedirs(FOLDER) - - # implement error handling - os.chdir(FOLDER) - -if __name__ == "__main__": - - logging.basicConfig(filename='twitterrepliestoinfluencers21.log', level=logging.INFO) - startnote = 'Scraping started ' + str(time.strftime("%Y%m%d%H%M%S")) - logging.info(startnote) - - parser = argparse.ArgumentParser() - - parser.add_argument( - "-t", - "--hashtags", - type=str - ) - - parser.add_argument( - "-u", - "--handles", - type=str - ) - - parser.add_argument( - "-k", - "--keywords", - type=str - ) - - parser.add_argument( - "-m", - "--max", - type=str - ) - - parser.add_argument( - "--until", - type=str - ) - - parser.add_argument( - "--since", - type=str - ) - - args = parser.parse_args() - - #month_dict = {"January":(1,30),"February":(1,28),"March":(1,31),"April":(1,30),"May":(1,31),"June":(1,30),"July":(1,31),"August":(1,31),"September":(1,30),"October":(1,31),"November":(1,30),"December":(1,31)} - - if args.hashtags: - - pass - # hasha = '2020_' +args.hashtags - # new_folder = FOLDER/'hashtags'/hasha - - elif args.keywords: - - pass - # hasha = '2020_' + args.keywords - # new_folder = FOLDER/'keyw0rds'/hasha - - elif args.handles: - - with open(args.handles,'r') as txt: - - handles_list = txt.readlines() - - new_folder = FOLDER/'replies_influencers' - if not new_folder.exists(): - os.makedirs(new_folder) - - os.chdir(new_folder) - - for handle in handles_list: - handle = handle.strip('\n') - #print(handle) - scrap_note = "Scraping : " + handle - logging.info(f'{scrap_note}') - - - t = threading.Thread(target=run_scraper,args=(args.hashtags,handle,args.keywords,args.max,args.until,args.since,)) - - t.start() - - PATH = os.getcwd() - print(PATH) - end_note = 'Scraping done : ' + str(time.strftime("%Y%m%d%H%M%S")) + handle - logging.info(f'{end_note}') - - end_note = 'Final Scraping done : ' + str(time.strftime("%Y%m%d%H%M%S")) + handle - logging.info(f'{end_note}') - - #t.join() - #time.sleep(5) \ No newline at end of file diff --git a/scrapers/Twitter/dev_twitterscraper_until_since.py b/scrapers/Twitter/dev_twitterscraper_until_since.py deleted file mode 100644 index 1067c74a..00000000 --- a/scrapers/Twitter/dev_twitterscraper_until_since.py +++ /dev/null @@ -1,402 +0,0 @@ -import logging -from ast import parse -import threading -from typing import Hashable -from requests.api import head -#from dotenv import load_dotenv -#load_dotenv() -#import nest_asyncio -#nest_asyncio.apply() -from pathlib import Path -#from pymongo import MongoClient -import argparse -import twint -import time -import json -import glob -import os -#import boto3 -#import botocore -import requests -from uuid import uuid4 -#from PIL import Image -from io import BytesIO -#from db_config import twitter_db_config - -FOLDER = Path('./data/twitter/') - -headers = { - "User-Agent" : "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36", - "Content-Type": "text/html", -} - -def initialize_s3(): - - """ - Setup the s3 bucket and returns the aws-base-url,bucket-name ,aws-s3 object and aws-resource object - Args: - None - Returns: - aws base url,aws bucket,s3 bucket object,s3 resource object - """ - - aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID") - aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY_ID") - aws_username = os.environ.get("AWS_USERNAME") - aws_bucket = os.environ.get("AWS_BUCKET") - s3_client = boto3.client("s3", aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - s3_resource = boto3.resource('s3', aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - #print(list(s3_resource.buckets.all())) - #ogbv_bucket = s3_resource.Bucket(aws_bucket) #Find the list of files stored in the given bucket - #print(ogbv_bucket.objects.all()) - - return aws_username, aws_bucket, s3_client, s3_resource - -def upload_to_s3(s3,file,filename,bucket,content_type): - - """ - Upload the images/videos to s3-bucket - Args: - s3 (s3 client object) - file (.jpg/.mp4) - file to upload - filename - filename to store an uploaded file on s3 (or) Key - bucket - aws s3 bucket name - content_type - type of the data - - - Returns: - None - """ - - # Implement Error Handling - with open(file,"rb") as data: - s3.upload_fileobj(Fileobj = data, - Bucket = bucket, - Key = filename, - ExtraArgs = {'ContentType':content_type, - 'ACL':'public-read'}) - - print(f"{file} successfully uploaded to s3") - -def get_s3_url(s3_client,aws_bucket,image_url): - - """ - Download the image/video from image_url,upload it to s3-bucket and get the s3-url of the uploaded image/video - Args: - #aws_bucket (str) - name of the s3 bucket - #key - filename stored on s3 bucket - image_url - image/video url of the image/video on twitter - - Returns: - object_url - s3 url of the uploaded image/video - """ - - s3_url = "" - - # check if the image_url exists for the tweet - if image_url: - - query = {"image_url": image_url} - - # check if the image exists on MongdDB - if coll.find_one(query): - print("Image already exists on MongoDB") - - else: - print("image url :",image_url) - - key = str(uuid4()) - filename = key + '.jpg' - - # download the image from the url and store locally - r = requests.get(image_url[0],headers=headers) - image = Image.open(BytesIO(r.content)) - - images_path = FOLDER/'images' - - if not images_path.exists(): - os.makedirs(images_path) - - image.save(images_path/filename) - print("Image saved on local disk....") - - upload_to_s3(s3_client,images_path/filename,key,aws_bucket,'image') - - config = botocore.client.Config(region_name = 'ap-south-1',signature_version = botocore.UNSIGNED) - s3_url = boto3.client('s3', config=config).generate_presigned_url('get_object', ExpiresIn=0, Params={'Bucket': aws_bucket, 'Key': key}) - print(s3_url) - - return s3_url - -def count_docs(coll): - - """ - Count the no. of docs/posts in the mongodb collection - Args: - coll (str) - mongodb collection - """ - - print("Count of posts : ") - print(coll.count_documents({})) - -def initialize_mongo(data_config): - - """ - Takes the MongoDB config objects of a Tattle service and returns the service's MongoDB collection - Args: - data_config (dict): A config dictionary imported from db_config - Example: - from db_config import instagram_db_config - collection = initialize_mongo(instagram_db_config) - """ - - mongo_url = "mongodb+srv://"+ data_config["db_username"] + ":"+ data_config["db_password"]+"@tattle-data-fkpmg.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE" - cli = MongoClient(mongo_url) - db = cli[data_config["db_name"]] - coll = db[data_config["db_collection"]] - - # count the no of post in the collection - count_docs(coll) - - return coll - -def upload_to_mongo(data,coll): - - """ - Upload posts metadata to mongodb - Args: - data (dict) : contains all the required fields - coll : upload to mongo-db collection - Returns: - None - """ - - # check if the tweet/post already exists on mongo-db using tweet_id - query = {"tweet_id": data['tweet_id']} - - if coll.find_one(query): - print("Tweet already exists on Mongodb") - else: - coll.insert_one(data) - -from datetime import date, timedelta - -def daterange(start_date, end_date): - for n in range(int((end_date - start_date).days)): - yield end_date - timedelta(n) - - -def scraper_util(hashtag,handle,keyword,limit,until,since): - - """ - Scraper utility func;contains all the configurations reqd for scraping - Args: - hashtag (str): hashtag to scrape - handle (str): user handle to scrape - keyword (str): keyword to scrape - limit (int): no. of tweets to scrape - Returns: - None - """ - - - timestr = time.strftime("%Y%m%d%H%M%S") - c = twint.Config() - - - #c.Proxy_host = "51.158.68.68" - #c.Proxy_port = "8761" - #c.Proxy_type = "http" - - c.Limit = limit - if not limit: - c.Limit = 100 - - #c.Format = "ID {id}" - c.Store_json = True - c.Hide_output = True - c.Since = since - c.Until = until - #print("c.until") - print(c.Until) - - if hashtag: - print("Hashtag") - print(hashtag) - c.Show_hashtags = True - c.Search = "#" + hashtag - c.Output = c.Until + hashtag + ".json" - - elif handle: - print("Handle") - print(handle) - c.Username = handle - c.Output = c.Until + handle + ".json" - - elif keyword: - print("keyword") - print(keyword) - c.Show_hashtags = False - c.Search = keyword - c.Output = c.Until + keyword + ".json" - - try: - twint_util = twint.run.Search(c) - except Exception as exc: - print(f'{exc}') - - end_note = c.Output - logging.info(f"Scraping done : {end_note}!!!!!") - -def run_scraper(hashtags,handles,keywords,limit,until,since): - - """ - Scraper function to scrape tweets based on hastags,handles,keywords - Args: - hastags - - """ - - # Implement reading the search items from a file - if hashtags: - - hashtags = hashtags.split(',') - for hashtag in hashtags: - scraper_util(hashtag,handles,keywords,limit,until,since) - - elif handles: - - handles = handles.split(',') - for handle in handles: - scraper_util(hashtags,handle,keywords,limit,until,since) - - elif keywords: - - keywords = keywords.split(',') - for keyword in keywords: - scraper_util(hashtags,handles,keyword,limit,until,since) - -def change_folder(FOLDER): - - """ - Create dir(if doesn't exist) and change to that dir - Args: - FOLDER (Path Object) - folder to create - """ - - #if not os.path.exists(FOLDER): - if not FOLDER.exists(): - os.makedirs(FOLDER) - - # implement error handling - os.chdir(FOLDER) - -def helper(search_term): - - - with open(search_term,'r') as txt: - - terms_list = txt.readlines() - search_term = search_term.split('.')[0] - print(search_term) - search = '1819' + search_term - - new_folder = FOLDER/search - - if not new_folder.exists(): - os.makedirs(new_folder) - - os.chdir(new_folder) - - for term in terms_list: - term = term.strip('\n') - - scrap_note = "Scraping : " + term - logging.info(f'{scrap_note}') - - if search_term == 'handle': - t = threading.Thread(target=run_scraper,args=(args.hashtags,term,args.keywords,args.max,args.until,args.since,)) - elif search_term == 'keyword': - t = threading.Thread(target=run_scraper,args=(args.hashtags,args.handles,term,args.max,args.until,args.since,)) - elif search_term == 'hashtag': - t = threading.Thread(target=run_scraper,args=(term,args.handles,args.keywords,args.max,args.until,args.since,)) - - t.start() - - PATH = os.getcwd() - print(PATH) - end_note = 'Scraping done : ' + str(time.strftime("%Y%m%d%H%M%S")) + term - logging.info(f'{end_note}') - - #time.join() - #time.sleep(5) - - end_note = 'Final Scraping done : ' + str(time.strftime("%Y%m%d%H%M%S")) + term - logging.info(f'{end_note}') - -if __name__ == "__main__": - - logging.basicConfig(filename='twitterscraper2019.log', level=logging.INFO) - startnote = 'Scraping started ' + str(time.strftime("%Y%m%d%H%M%S")) - logging.info(startnote) - - parser = argparse.ArgumentParser() - - parser.add_argument( - "-t", - "--hashtags", - type=str - ) - - parser.add_argument( - "-u", - "--handles", - type=str - ) - - parser.add_argument( - "-k", - "--keywords", - type=str - ) - - parser.add_argument( - "-m", - "--max", - type=str - ) - - parser.add_argument( - "-y", - "--year", - type=int - ) - - parser.add_argument( - "--until", - type=str - ) - - parser.add_argument( - "--since", - type=str - ) - - args = parser.parse_args() - - if args.hashtags: - - helper(args.hashtags) - - elif args.keywords: - - helper(args.keywords) - - elif args.handles: - - helper(args.handles) - \ No newline at end of file diff --git a/scrapers/Twitter/dev_twitterscraper_withlogging.py b/scrapers/Twitter/dev_twitterscraper_withlogging.py deleted file mode 100644 index a59f37b0..00000000 --- a/scrapers/Twitter/dev_twitterscraper_withlogging.py +++ /dev/null @@ -1,376 +0,0 @@ -import logging -from ast import parse -from typing import Hashable -from requests.api import head -#from dotenv import load_dotenv -#load_dotenv() -#import nest_asyncio -#nest_asyncio.apply() -from pathlib import Path -#from pymongo import MongoClient -import argparse -import twint -import time -import json -import glob -import os -#import boto3 -#import botocore -import requests -from uuid import uuid4 -#from PIL import Image -from io import BytesIO -#from db_config import twitter_db_config - -FOLDER = Path('./data/twitter/') - -headers = { - "User-Agent" : "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36", - "Content-Type": "text/html", -} - -def initialize_s3(): - - """ - Setup the s3 bucket and returns the aws-base-url,bucket-name ,aws-s3 object and aws-resource object - Args: - None - Returns: - aws base url,aws bucket,s3 bucket object,s3 resource object - """ - - aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID") - aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY_ID") - aws_username = os.environ.get("AWS_USERNAME") - aws_bucket = os.environ.get("AWS_BUCKET") - s3_client = boto3.client("s3", aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - s3_resource = boto3.resource('s3', aws_access_key_id = aws_access_key_id, - aws_secret_access_key= aws_secret_access_key) - - #print(list(s3_resource.buckets.all())) - #ogbv_bucket = s3_resource.Bucket(aws_bucket) #Find the list of files stored in the given bucket - #print(ogbv_bucket.objects.all()) - - return aws_username, aws_bucket, s3_client, s3_resource - -def upload_to_s3(s3,file,filename,bucket,content_type): - - """ - Upload the images/videos to s3-bucket - Args: - s3 (s3 client object) - file (.jpg/.mp4) - file to upload - filename - filename to store an uploaded file on s3 (or) Key - bucket - aws s3 bucket name - content_type - type of the data - - - Returns: - None - """ - - # Implement Error Handling - with open(file,"rb") as data: - s3.upload_fileobj(Fileobj = data, - Bucket = bucket, - Key = filename, - ExtraArgs = {'ContentType':content_type, - 'ACL':'public-read'}) - - print(f"{file} successfully uploaded to s3") - -def get_s3_url(s3_client,aws_bucket,image_url): - - """ - Download the image/video from image_url,upload it to s3-bucket and get the s3-url of the uploaded image/video - Args: - #aws_bucket (str) - name of the s3 bucket - #key - filename stored on s3 bucket - image_url - image/video url of the image/video on twitter - - Returns: - object_url - s3 url of the uploaded image/video - """ - - s3_url = "" - - # check if the image_url exists for the tweet - if image_url: - - query = {"image_url": image_url} - - # check if the image exists on MongdDB - if coll.find_one(query): - print("Image already exists on MongoDB") - - else: - print("image url :",image_url) - - key = str(uuid4()) - filename = key + '.jpg' - - # download the image from the url and store locally - r = requests.get(image_url[0],headers=headers) - image = Image.open(BytesIO(r.content)) - - images_path = FOLDER/'images' - - if not images_path.exists(): - os.makedirs(images_path) - - image.save(images_path/filename) - print("Image saved on local disk....") - - upload_to_s3(s3_client,images_path/filename,key,aws_bucket,'image') - - config = botocore.client.Config(region_name = 'ap-south-1',signature_version = botocore.UNSIGNED) - s3_url = boto3.client('s3', config=config).generate_presigned_url('get_object', ExpiresIn=0, Params={'Bucket': aws_bucket, 'Key': key}) - print(s3_url) - - return s3_url - -def count_docs(coll): - - """ - Count the no. of docs/posts in the mongodb collection - Args: - coll (str) - mongodb collection - """ - - print("Count of posts : ") - print(coll.count_documents({})) - -def initialize_mongo(data_config): - - """ - Takes the MongoDB config objects of a Tattle service and returns the service's MongoDB collection - Args: - data_config (dict): A config dictionary imported from db_config - Example: - from db_config import instagram_db_config - collection = initialize_mongo(instagram_db_config) - """ - - mongo_url = "mongodb+srv://"+ data_config["db_username"] + ":"+ data_config["db_password"]+"@tattle-data-fkpmg.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE" - cli = MongoClient(mongo_url) - db = cli[data_config["db_name"]] - coll = db[data_config["db_collection"]] - - # count the no of post in the collection - count_docs(coll) - - return coll - -def upload_to_mongo(data,coll): - - """ - Upload posts metadata to mongodb - Args: - data (dict) : contains all the required fields - coll : upload to mongo-db collection - Returns: - None - """ - - # check if the tweet/post already exists on mongo-db using tweet_id - query = {"tweet_id": data['tweet_id']} - - if coll.find_one(query): - print("Tweet already exists on Mongodb") - else: - coll.insert_one(data) - -from datetime import date, timedelta - -def daterange(start_date, end_date): - for n in range(int((end_date - start_date).days)): - yield end_date - timedelta(n) - - -def scraper_util(hashtag,handle,keyword,limit,until): - - """ - Scraper utility func;contains all the configurations reqd for scraping - Args: - hashtag (str): hashtag to scrape - handle (str): user handle to scrape - keyword (str): keyword to scrape - limit (int): no. of tweets to scrape - Returns: - None - """ - - - timestr = time.strftime("%Y%m%d%H%M%S") - c = twint.Config() - - - c.Limit = limit - if not limit: - c.Limit = 100 - - #c.Format = "ID {id}" - c.Store_json = True - c.Hide_output = True - - c.Until = until - print("c.until") - print(c.Until) - - - if hashtag: - print("Hashtag") - print(hashtag) - c.Show_hashtags = True - c.Search = "#" + hashtag - c.Output = c.Until + hashtag + ".json" - - elif handle: - print("Handle") - print(handle) - c.Username = handle - c.Output = c.Until + handle + ".json" - - elif keyword: - print("keyword") - print(keyword) - c.Show_hashtags = False - c.Search = keyword - c.Output = c.Until + keyword + ".json" - - try: - twint_util = twint.run.Search(c) - except Exception as exc: - print(f'{exc}') - -def run_scraper(hashtags,handles,keywords,limit,until): - - """ - Scraper function to scrape tweets based on hastags,handles,keywords - Args: - hastags - - """ - - # Implement reading the search items from a file - if hashtags: - #change_folder(FOLDER/'hashtags') - hashtags = hashtags.split(',') - for hashtag in hashtags: - scraper_util(hashtag,handles,keywords,limit,until) - - elif handles: - #if os.getcwd() != FOLDER/'handles': - # change_folder(FOLDER/'handles') - - handles = handles.split(',') - for handle in handles: - scraper_util(hashtags,handle,keywords,limit,until) - - elif keywords: - - #change_folder(FOLDER/'keywords') - keywords = keywords.split(',') - for keyword in keywords: - scraper_util(hashtags,handles,keyword,limit,until) - -def change_folder(FOLDER): - - """ - Create dir(if doesn't exist) and change to that dir - Args: - FOLDER (Path Object) - folder to create - """ - - #if not os.path.exists(FOLDER): - if not FOLDER.exists(): - os.makedirs(FOLDER) - - # implement error handling - os.chdir(FOLDER) - -if __name__ == "__main__": - - - logging.basicConfig(filename='twitterscraper2019.log', level=logging.INFO) - logging.info('Scraping started') - - parser = argparse.ArgumentParser() - - parser.add_argument( - "-t", - "--hashtags", - type=str - ) - - parser.add_argument( - "-u", - "--handles", - type=str - ) - - parser.add_argument( - "-k", - "--keywords", - type=str - ) - - parser.add_argument( - "-m", - "--max", - type=str - ) - - parser.add_argument( - "-y", - "--year", - type=int - ) - - args = parser.parse_args() - - month_dict = {"January":(1,30),"February":(1,28),"March":(1,31),"April":(1,30),"May":(1,31),"June":(1,30),"July":(1,31),"August":(1,31),"September":(1,30),"October":(1,31),"November":(1,30),"December":(1,31)} - - - if args.hashtags: - - hasha = str(args.year) + '_' +args.hashtags - new_folder = FOLDER/'hashtags'/hasha - logging.info(f'{new_folder}') - - elif args.keywords: - - hasha = str(args.year) + '_' + args.keywords - new_folder = FOLDER/'keyw0rds'/hasha - logging.info(f'{new_folder}') - - elif args.handles: - - hasha = str(args.year) + '_' +args.handles - new_folder =FOLDER/'handles'/hasha - logging.info(f'{new_folder}') - - if not new_folder.exists(): - os.makedirs(new_folder) - - os.chdir(new_folder) - - - for idx,month in enumerate(month_dict.values()): - - start_date = date(args.year,idx+1,month[0]) - end_date = date(args.year,idx+1,month[1]) - - for single_date in daterange(start_date, end_date): - until = single_date.strftime("%Y-%m-%d") - print(until) - time.sleep(1) - upload_folder = str(new_folder) + '\t' + str(until) - logging.info(f'{upload_folder}') - run_scraper(args.hashtags,args.handles,args.keywords,args.max,until) - - - PATH = os.getcwd() - print(PATH) \ No newline at end of file diff --git a/scrapers/Twitter/notebooks/EDA - twitter data-Jan6-Deduplicate - Replies.ipynb b/scrapers/Twitter/notebooks/EDA - twitter data-Jan6-Deduplicate - Replies.ipynb deleted file mode 100644 index edd1175a..00000000 --- a/scrapers/Twitter/notebooks/EDA - twitter data-Jan6-Deduplicate - Replies.ipynb +++ /dev/null @@ -1,1613 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv('twitter_data_jan6.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "original_rows = data.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### De- duplicate\n", - "-- on tweets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.drop_duplicates(subset=['Tweet'],keep='last')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "duplicate_rows = original_rows - data.shape[0]\n", - "duplicate_rows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_graph():\n", - " \n", - "\n", - " df = pd.DataFrame({'speed': speed,\n", - " 'lifespan': lifespan}, index=index)\n", - " ax = df.plot.bar(rot=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Type'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Type'].value_counts().plot(kind='barh',title='Dist of types')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Collection Strategy\n", - "\n", - "- Tweets are scraped from 2021,2020 by iterating through all the dates\n", - "- few tweets are also fetched by the scraper from previous years\n", - "- fetched replies only to trolled accounts (2021)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### DIstributions of year" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def extract_year(timestamp):\n", - " \n", - " return timestamp.split('-')[0]\n", - " \n", - "\n", - "data['year'] = data['Timestamp Of Creation'].apply(extract_year)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['year'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['year'].value_counts().plo\n", - "2017 170\n", - "2015 42\n", - "2016 41\n", - "t(kind='barh',title='Dist of Years')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Distribution of month" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import calendar\n", - "month_mapper = {index: month for index, month in enumerate(calendar.month_abbr) if month}\n", - "\n", - "def extract_month(timestamp):\n", - " \n", - " return month_mapper[int(timestamp.split('-')[1])]\n", - "\n", - "data['month'] = data['Timestamp Of Creation'].apply(extract_month)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['month'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['month'].value_counts().plot(kind='barh',title='Dist of month')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Months with most tweets \n", - " - December\n", - " - May\n", - " - April\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Avg length of tweet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def extract_len(tweet):\n", - " \n", - " return len(tweet)\n", - "\n", - "\n", - "data['tweet_length'] = data['Tweet'].apply(extract_len)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['tweet_length'].mean()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- avg length of tweets : 128" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Search distribution" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Search'].value_counts()[:20]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "data['Search'].value_counts()[:10].plot(kind='barh',title='Top 10 -Dist of search terms')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tweets with urls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Urls'].value_counts()*100/data.shape[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-- 93% tweets doesn't have any urls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Replies Count" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Replies Count'].value_counts()[:5]*100/data.shape[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 66.3% tweets with Zero replies" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Content type - distribution - " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ct = data['Content Type'].value_counts()*100/data.shape[0]\n", - "ct" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ct.plot(kind='barh')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Place " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Place'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-- no significant information from place" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Language" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Language'].value_counts()[:10]*100/data.shape[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- English - 35.9%\n", - "- Hindi - 33%\n", - "- Tamil - 14.7%" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Language'].value_counts()[:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### RT count" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Retweets Count'].value_counts()[:10]*100/data.shape[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 71% tweets with no retweets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### User Rt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['User Rt'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- no significant information from 'User Rt'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Quote url\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "data['Quote URL'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- highest quoted tweet : 42 times" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hashtags" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Hashtags'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['Hashtags'].value_counts()[:3]*100/data.shape[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### hashtags not in search terms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "data['Hashtags'][0][1:-1].split(' ')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_hashtags = [hashtag[1:-1].split(' ') for hashtag in list(data['Hashtags']) if (not pd.isna(hashtag)) and (hashtag!='')]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import itertools\n", - "\n", - "all_hashs = list(itertools.chain(*all_hashtags))\n", - "all_hashs = [h for h in all_hashs if h!='']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(all_hashs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### most used hashtags" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import Counter\n", - "\n", - "h = Counter(all_hashs)\n", - "h.most_common(30)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hashtags = list(set(all_hashs))\n", - "print(len(hashtags))\n", - "\n", - "hashtags = [hashtag.strip('\"') for hashtag in hashtags if hashtag!='']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hashtags\n", - "len(hashtags)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "search_terms = list(data['Search'].unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "update_hashtags = set(hashtags) - set(search_terms)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "update_hashtags\n", - "len(update_hashtags)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "update_hashtags" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### common hashtags not present in search terms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "search_terms = [s.strip('#') for s in search_terms]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_hashs_upd = [hashtag.strip('\"') for hashtag in all_hashs if hashtag!='']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "search_terms = [s.lower() for s in search_terms]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#all_hashs_upd = [ for hashtag in all_hashs_upd]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_hashs = [h for h in all_hashs if h.strip('\"') not in search_terms]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ch = Counter(all_hashs)\n", - "ch.most_common(20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Falsecases -> fakecases\n", - "\n", - "PotentialLegalTerrorist -> legalterrorism\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 71% tweets with no hashtags\n", - "- womenaretrash is the most used hashtag\n", - "- " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Count of hashtags" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def count_hashtags(hashtags):\n", - " \n", - " if not pd.isna(hashtags):\n", - " \n", - " return len(hashtags)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['count_hashtag'] = data['Hashtags'].apply(count_hashtags)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['count_hashtag'].max()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['count_hashtag'].idxmax()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['count_hashtag'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(data.iloc[27381]['Hashtags'])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Replies " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_type = data.groupby(['Type'])\n", - "df_reply = df_type.get_group('reply')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df_reply['Timestamp Of Creation'].iloc[0].split(':')[0].split(' ')[1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Time Distribution" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_hour(time):\n", - " \n", - " return time.split(':')[0].split(' ')[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply['time'] = df_reply['Timestamp Of Creation'].apply(get_hour)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply['time'].value_counts()[:10].plot(kind='barh',title = 'Time of Reply (in hour)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply['Content Type'].value_counts().plot(kind='barh',title = 'Content type dist. of replies')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply['Language'].value_counts()[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply['Language'].value_counts()[:10].plot(kind='barh',title = 'Language dist. of replies')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Type Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_type = data.groupby(['Type'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reply" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply = df_type.get_group('reply')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply['Search'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(df_reply['Search'].unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_reply['Search'].value_counts()[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Hashtag" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hashtag = df_type.get_group('hashtag')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hashtag.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hashtag['Search'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hashtag['Search'].value_counts()[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Keyword" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_keyword = df_type.get_group('keyword')\n", - "df_keyword.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_keyword['Search'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_keyword['Search'].value_counts()[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Handle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_handle = df_type.get_group('handle')\n", - "df_handle.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_handle['Search'].uniqueque()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_handle['Search'].value_counts()[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Todo : Analysis on Replies (Content type,Length)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lang vs Type" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_lang = data.groupby(['Language'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### English" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng = df_lang.get_group('en')\n", - "df_eng.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_type = df_eng.groupby(['Type'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reply - en" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_reply = df_eng_type.get_group('reply')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_reply['Search'].value_counts()[:10].plot(kind='barh',title='Dist of Replies (English)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Hashtag - en" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_hashtag = df_eng_type.get_group('hashtag')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_hashtag['Search'].value_counts()[:10].plot(kind='barh',title='Dist of hashtags (English)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Most Frequent Hashtags in tweets (not present in search terms)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import itertools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_freq_unused_hashtags(df,n):\n", - " \n", - " search_terms = list(df['Search'].unique())\n", - " search_terms = [s.strip('#') for s in search_terms]\n", - " \n", - " all_hashtags = [hashtag[1:-1].split(' ') for hashtag in list(df['Hashtags']) if (not pd.isna(hashtag)) and (hashtag!='')]\n", - " all_hashs = list(itertools.chain(*all_hashtags))\n", - " all_hashs = [h for h in all_hashs if h!='']\n", - " \n", - " all_hashs_upd = [hashtag.strip('\"') for hashtag in all_hashs if hashtag!='']\n", - " search_terms = [s.lower() for s in search_terms]\n", - " \n", - " all_hashs = [h for h in all_hashs if h.strip('\"') not in search_terms]\n", - " \n", - " ch = Counter(all_hashs)\n", - " print(ch.most_common(n))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "get_freq_unused_hashtags(df_eng,20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Handle - en" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_handle = df_eng_type.get_group('handle')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_handle['Search'].value_counts()[:10].plot(kind='barh',title='Dist of handle (English)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Keyword - en" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_keyword = df_eng_type.get_group('keyword')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_eng_keyword['Search'].value_counts()[:10].plot(kind='barh',title='Dist of keywords (English)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hindi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin = df_lang.get_group('hi')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin_type = df_hin.groupby(['Type'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reply - hi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin_reply = df_hin_type.get_group('reply')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df_hin_reply['Search'].value_counts()[:10].plot(kind='barh',title='Dist of reply (Hindi)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Hashtag - hi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin_hashtag = df_hin_type.get_group('hashtag')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin_hashtag['Search'].value_counts()[:10].plot(kind='barh',title='Dist of hashtag (Hindi)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin_hashtag['Search'].value_counts()[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Most Frequent Hashtags in tweets (not present in search terms)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "get_freq_unused_hashtags(df_hin,20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Handle - hi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin_handle = df_hin_type.get_group('handle')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin_handle['Search'].value_counts()[:10].plot(kind='barh',title='Dist of handle (Hindi)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Keyword - hi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_hin_keyword = df_hin_type.get_group('keyword')\n", - "df_hin_keyword['Search'].value_counts()[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tamil" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_tam = df_lang.get_group('ta')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_tam_type = df_tam.groupby(['Type'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reply - ta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df_tam_reply = df_tam_type.get_group('reply')\n", - "df_tam_reply['Search'].value_counts()[:10].plot(kind='barh',title='Dist of reply (Tamil)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Hashtag - ta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_tam_hashtag = df_tam_type.get_group('hashtag')\n", - "df_tam_hashtag['Search'].value_counts()[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Handle -ta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_tam_handle = df_tam_type.get_group('handle')\n", - "df_tam_handle['Search'].value_counts()[:10].plot(kind='barh',title='Dist of handle (Tamil)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Most Frequent Hashtags in tweets (not present in search terms)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "get_freq_unused_hashtags(df_tam,20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Keyword - ta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_tam_keyword = df_tam_type.get_group('keyword')\n", - "df_tam_keyword['Search'].value_counts()[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.7.11 64-bit ('nlp': conda)", - "language": "python", - "name": "python3711jvsc74a57bd07afbde5199b4215604d9ba5a0bf2f330e5a9d3140d2f7848025bd250c5fae2df" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/scrapers/Twitter/requirements.txt b/scrapers/Twitter/requirements.txt deleted file mode 100644 index 2c25d4df..00000000 --- a/scrapers/Twitter/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -pymongo==3.12.0 -boto3==1.18.60 -botocore==1.21.60 -Pillow -uuid diff --git a/uli-website/.npmrc b/uli-website/.npmrc new file mode 100644 index 00000000..521a9f7c --- /dev/null +++ b/uli-website/.npmrc @@ -0,0 +1 @@ +legacy-peer-deps=true diff --git a/uli-website/gatsby-config.js b/uli-website/gatsby-config.js index d56c2fd6..9822dfe7 100644 --- a/uli-website/gatsby-config.js +++ b/uli-website/gatsby-config.js @@ -6,16 +6,7 @@ module.exports = { "gatsby-plugin-styled-components", "gatsby-plugin-image", "gatsby-plugin-react-helmet", - { - resolve: "gatsby-plugin-mdx", - options: { - defaultLayouts: { - default: require.resolve( - "./src/components/molecules/ContentPageShell.jsx" - ), - }, - }, - }, + "gatsby-plugin-mdx", "gatsby-plugin-sharp", "gatsby-transformer-sharp", { diff --git a/uli-website/gatsby-node.mjs b/uli-website/gatsby-node.mjs new file mode 100644 index 00000000..cd24c283 --- /dev/null +++ b/uli-website/gatsby-node.mjs @@ -0,0 +1,14 @@ +import { createFilePath } from "gatsby-source-filesystem" + +export const onCreateNode = ({ node, getNode, actions }) => { + const { createNodeField } = actions + if (node.internal.type === 'Mdx') { + const relativeFilePath = createFilePath({ node, getNode }) + console.log(relativeFilePath); + createNodeField({ + node, + name: 'slug', + value: `${relativeFilePath}` + }); + } +}; diff --git a/uli-website/package-lock.json b/uli-website/package-lock.json index c32ecaae..11b22b33 100644 --- a/uli-website/package-lock.json +++ b/uli-website/package-lock.json @@ -8,35 +8,42 @@ "name": "uli", "version": "1.0.0", "dependencies": { - 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"@mdx-js/react": "^1.6.22", - "babel-plugin-styled-components": "^2.0.7", - "gatsby": "^4.13.1", - "gatsby-plugin-image": "^2.13.0", - "gatsby-plugin-manifest": "^4.19.0", - "gatsby-plugin-mdx": "^3.13.0", - "gatsby-plugin-react-helmet": "^5.13.0", - "gatsby-plugin-s3": "^0.3.8", - "gatsby-plugin-sharp": "^4.13.0", - "gatsby-plugin-styled-components": "^5.13.0", - "gatsby-source-filesystem": "^4.13.0", - "gatsby-transformer-sharp": "^4.13.0", - "grommet": "^2.23.0", - "i18next": "^21.8.14", - "react": "^17.0.1", - "react-code-blocks": "^0.1.4", - "react-dom": "^17.0.1", - "react-helmet": "^6.1.0", - "react-i18next": "^11.18.1", - "styled-components": "^5.3.5" + "@mdx-js/react": "3.0.0", + "babel-plugin-styled-components": "2.1.4", + "gatsby": "5.12.9", + "gatsby-plugin-image": "3.12.3", + "gatsby-plugin-manifest": "5.12.3", + "gatsby-plugin-mdx": "5.12.3", + "gatsby-plugin-react-helmet": "6.12.0", + "gatsby-plugin-s3": "0.4.1", + "gatsby-plugin-sharp": "5.12.3", + "gatsby-plugin-styled-components": "6.12.0", + "gatsby-source-filesystem": "5.12.1", + "gatsby-transformer-sharp": "5.12.3", + "grommet": "2.34.1", + "i18next": "23.7.6", + "react": "18.2.0", + "react-code-blocks": "0.1.5", + "react-dom": "18.2.0", + "react-helmet": "6.1.0", + "react-i18next": "13.5.0", + "styled-components": "5.3.11" } } diff --git a/uli-website/src/pages/about.mdx b/uli-website/src/pages/about.mdx index 2615f61d..b9122be2 100644 --- a/uli-website/src/pages/about.mdx +++ b/uli-website/src/pages/about.mdx @@ -1,5 +1,8 @@ +import ContentPageShell from "../components/molecules/ContentPageShell.jsx" import MultiLingualAbout from "../components/atoms/MultiLingualAbout"; + + ## About Uli The lines between online and offline life are fast splintering. Online gender-based violence against gender and sexual minorities and misogyny results in offline harm and results in people receding from online spaces. Uli (meaning chisel in Tamil) is our attempt to hand the chisel and power over to users most affected by online gender-based violence. Uli aims to build and demand for responsibity and inclusivity in the systems that shape our online life. @@ -63,3 +66,4 @@ Through our work with partners we continue to hear requests for additional featu This project also engages with some difficult and critical questions around machine learning, the democratization of technology, expertise and online safety. We are also open to partnering with researchers who can helps us refine our approach (at all levels), while also contributing to action-centred research in these areas. + \ No newline at end of file diff --git a/uli-website/src/pages/blog/approach.mdx b/uli-website/src/pages/blog/approach.mdx index e43e52f9..8eb7fa61 100644 --- a/uli-website/src/pages/blog/approach.mdx +++ b/uli-website/src/pages/blog/approach.mdx @@ -5,6 +5,11 @@ author: "CIS and Tattle" project: " " date: 01-08-2021 --- + +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + ** This is one of the earliest articulations of the project vision and approach, written in the July/August 2021 ** The graphic narrative titled ‘Personal (Cyber) Space’ published in 2016 by Parthasarthy and Malhotra narrates an experience of a young internet user. The animated short comic hosted by Kadak, a South Asian women collective, asks: ‘If one says something, there’s the fear of hateful response. But if one doesn’t say something, isn’t that silence counterproductive?’ only to end with the question, ‘so what does one say?’ @@ -38,3 +43,5 @@ The first three stakeholders are those who are at the receiving end of online ge In 2021, we conducted focus group discussions and interviews. We cataloged the request for features we heard at the time, [here](https://docs.google.com/document/d/e/2PACX-1vQ29pzrZtFOxgb5yPgA1a-y_0LZbGRureUx0E0LZvRx2VgqANOYrAvDNuqYHQMpVcQPgH3ql-52YSu9/pub). We prioritized features based on their perceived importance and the level of effort required in developing them. + + \ No newline at end of file diff --git a/uli-website/src/pages/blog/getting-started-with-aws-copilot.mdx b/uli-website/src/pages/blog/getting-started-with-aws-copilot.mdx index a098cf31..1a5f71aa 100644 --- a/uli-website/src/pages/blog/getting-started-with-aws-copilot.mdx +++ b/uli-website/src/pages/blog/getting-started-with-aws-copilot.mdx @@ -6,6 +6,10 @@ project: date: 28-08-2022 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + Around a few days ago, I was introduced to, what is, from the time of writing a relatively new framework for hosting containers in the cloud, known as AWS co-pilot. This blog post serves as a collection of all that I've learnt and more regarding the usage of co-pilot, a potential way to deploy and host Uli, using copilot and relevant resources which were useful to me in learning about co-pilot and its various use cases. ## Configuring AWS: @@ -198,3 +202,5 @@ $ copilot storage init -n my-bucket -t S3 -w frontend This will automatically create a bucked for when the frontend service is created with relevant permissions. Similar commands can be run for dynamoDB as well. These wold be most of the commands required to get started with copilot and its features relevant to ULI, I shall update this blog later if more commands come to my mind. + + \ No newline at end of file diff --git a/uli-website/src/pages/blog/index.jsx b/uli-website/src/pages/blog/index.jsx index 5bd5bd05..3319f1df 100644 --- a/uli-website/src/pages/blog/index.jsx +++ b/uli-website/src/pages/blog/index.jsx @@ -6,6 +6,7 @@ import { NavLink } from "../../components/atoms/UliCore"; const BlogIndex = ({ data }) => { const blogs = data.allMdx.nodes; + console.log(blogs); return ( @@ -15,7 +16,7 @@ const BlogIndex = ({ data }) => { {blogs.map((blog) => { return ( - + {blog.frontmatter.name}, {" " + blog.frontmatter.author} @@ -31,23 +32,25 @@ const BlogIndex = ({ data }) => { ); }; -export const query = graphql` - query BlogIndexQuery { - allMdx( - filter: { fileAbsolutePath: { regex: "/.*/src/pages/blog/" } } - sort: { fields: frontmatter___date, order: DESC } - ) { - nodes { +export const query = graphql`query BlogIndexQuery { + allMdx( + filter: {internal: {contentFilePath: {regex: "/.*/src/pages/blog/"}}} + sort: {frontmatter: {date: DESC}} + ) { + nodes { + fields { slug - frontmatter { - name - author - date - } - fileAbsolutePath + } + frontmatter { + name + author + date + } + internal { + contentFilePath } } } -`; +}`; export default BlogIndex; diff --git a/uli-website/src/pages/blog/making-of-August-2023.mdx b/uli-website/src/pages/blog/making-of-August-2023.mdx index b8b9d16f..e207f706 100644 --- a/uli-website/src/pages/blog/making-of-August-2023.mdx +++ b/uli-website/src/pages/blog/making-of-August-2023.mdx @@ -6,6 +6,10 @@ project: " " date: 07-09-2023 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + ## The Evolution of Uli It has been nearly a year since the last email update from the Uli team! After the launch in August last year we had a six month period of demoing, planning for the next iteration and fundraising. Uli has been moving full steam since March of this year. But in the busyness of getting the next phase launched, we forgot the lovely ritual of these email updates! We are remedying the situation and will send you monthly(ish) updates for the next year of development. @@ -27,3 +31,4 @@ The tasks for the next few months are cut out. There is plenty to be done and yo It is worth mentioning that many reports have come out over the last few months documenting the challenge and scale of online/facilitated gender based violence. A report by [Center for International Governance Innovation](https://www.cigionline.org/articles/tech-facilitated-gender-based-violence-is-an-international-human-rights-concern-finds-new-research/) reports on experiences of online harms in 18 different countries. [Another report](https://www.sddirect.org.uk/resource/technology-facilitated-gender-based-violence-preliminary-landscape-analysis) commissioned by the Global Partnership for Action on Gender-Based Online Harassment provides a landscape analysis for a coherent research agenda. Reading these reports, alongside user-research, has been quite interesting. One of the things we noted from the focus group discussions in 2021 was the [everyday-ness](https://uli.tattle.co.in/blog/making-of-dec-2021) of gendered abuse, and its manifestation as fatigue. The everyday-ness, beyond the doxxing and cyber-stalking that get noted in FIRs and media, continues to echo through our conversations this year. To make visible something that has become mundane and to de-normalize it- that remains a key goal for us. We have some ideas brewing (beyond building specific features in Uli), but if you are working on creative projects or solutions towards the same goals, we would love to exchange notes and brainstorm together. Please email us uli_support@tattle.co.in. + \ No newline at end of file diff --git a/uli-website/src/pages/blog/making-of-dec-2021.mdx b/uli-website/src/pages/blog/making-of-dec-2021.mdx index 9851c9fb..d635f067 100644 --- a/uli-website/src/pages/blog/making-of-dec-2021.mdx +++ b/uli-website/src/pages/blog/making-of-dec-2021.mdx @@ -6,6 +6,10 @@ project: " " date: 15-12-2021 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + # The making of the ML tool to mitigate online gender based violence in three Indian languages **This post is adapted from the Uli newsletter update sent in December 2021** @@ -37,3 +41,5 @@ To create guidelines for the annotators, we had long discussions internally over Because the meaning of oGBV is contested, the expert annotators will have the final word on what is and is not oGBV. Finally, we are wary that like all prediction models, unacknowledged prejudices will affect the proposed model. We are now evaluating the final model to understand how it may be biased against specific identity groups. + + \ No newline at end of file diff --git a/uli-website/src/pages/blog/making-of-feb-2022.mdx b/uli-website/src/pages/blog/making-of-feb-2022.mdx index e9ba97c2..990bac50 100644 --- a/uli-website/src/pages/blog/making-of-feb-2022.mdx +++ b/uli-website/src/pages/blog/making-of-feb-2022.mdx @@ -6,6 +6,10 @@ project: " " date: 15-02-2022 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + Today we will dwell on the features that we have added to the beta version of the tool and while our machine continues to learn the difference between a friendly post and a post meant to harass on the basis of the ongoing annotations, these additional features will keep the tool alive. Whoever said that “A user interface is like a joke. If you have to explain it, it’s not that good” was mostly referring to the final product. However, we know that a joke or a UI is not written or designed in a day. These things go through multiple iterations. Design decisions require evaluation and explanation before they can be accepted or scrapped for better alternatives. We would like you to help us make these decisions. @@ -21,3 +25,5 @@ The ‘Your Slur List” feature allows you to build a customized slur list with ![image](/slur_replace.png) Once the slur list is activated, this is what you will see if a post contains words that are in the slur list: These features are not perfect and still require a lot of work. For example, the tool should be able to capture spelling variations such as “Bitch” and “B!tch” but should distinguish between “whore” and “where”. However, it’s great to concretize an idea into something that can be felt and touched (with a mouse pointer at least!) and improved upon. + + \ No newline at end of file diff --git a/uli-website/src/pages/blog/making-of-jan-2022.mdx b/uli-website/src/pages/blog/making-of-jan-2022.mdx index f54f3afc..288b10a7 100644 --- a/uli-website/src/pages/blog/making-of-jan-2022.mdx +++ b/uli-website/src/pages/blog/making-of-jan-2022.mdx @@ -6,6 +6,10 @@ project: " " date: 15-01-2022 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + # The making of the ML tool to mitigate online gender based violence in three Indian languages This post is adapted from the Uli newsletter update sent in January 2022 @@ -38,3 +42,5 @@ Which means that the worst is yet to come. However, we will be doing a beta test of our plug-in in February to test non-ML features that are building parallelly. During this beta test with 50 users, we will test the archiving as well as the simple filtering feature but more on this later. If nothing else, we are sure that the non-ML features will keep the plug-in alive till we figure out how to model context into these machines. + + \ No newline at end of file diff --git a/uli-website/src/pages/blog/making-of-mar-2022.mdx b/uli-website/src/pages/blog/making-of-mar-2022.mdx index 921ca34f..ad590e85 100644 --- a/uli-website/src/pages/blog/making-of-mar-2022.mdx +++ b/uli-website/src/pages/blog/making-of-mar-2022.mdx @@ -6,6 +6,10 @@ project: " " date: 26-04-2022 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + **This post is adapted from the Uli newsletter updates sent in March and April 2022** We are now very close to bringing to life our machine learning tool that mitigates online gender-based violence (oGBV). We are working with two amazing designers who will use histories, narratives, forms and shapes to further empower this tool and the stories of its community. @@ -47,3 +51,5 @@ Uli (“Uli” உளி) was a name suggested by one of our teammates. ![image](/uli_name.png) It is a simple tool - a chisel, that allows one to make a room of one’s own or courtyards where people can come together, share stories, and reflect on the future we all want to see. Its logo, whose style is based in the feminist practice of quilt-making, resembles a courtyard of moving ideas and communities. It is an ode to the efforts of different groups, organizations and movements that strive to empower each one of us. + + \ No newline at end of file diff --git a/uli-website/src/pages/blog/responsible-display.mdx b/uli-website/src/pages/blog/responsible-display.mdx index 8e0c8daf..35996ef5 100644 --- a/uli-website/src/pages/blog/responsible-display.mdx +++ b/uli-website/src/pages/blog/responsible-display.mdx @@ -6,6 +6,10 @@ project: date: 16-04-2022 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + # Responsible Way of Showcasing the Countering of OGBV _Author: Yash Budhwar, Tattle Civic Tech_ @@ -17,10 +21,10 @@ Given the goals of Uli (accessibility, transparency of process), explaining the - Block Party's [instructions](https://www.blockpartyapp.com/how-it-works/) contain an easily digestible pathway and focus on diagrammatic and headline representation of its features, rather than the harmful tweet itself. There is an emphasis on instructional material and usage of safety words like “day-to-day more bearable,” “strength,” “anti-harassment,” “without the mental health impact,” etc. The harmful content is itself faded or represented with an unclear, hazy picture which serves to remove the focus away from it. The takeaway from this would be to focus on the empowering functions and features of the plugin, with simple instructional diagrammatic flow representations. - FB’s [Hateful Memes dataset](https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set/) and their blog post is helpful as it highlights the specific kind of content the tool can moderate (in their case, it is memes). While also explaining how their dataset can be used by multiple stakeholders and the sensitivity of such data, it does so by utilising examples of meme content that will be found in the dataset once the context is taken into picture. The takeaway from this would be to highlight the use-cases of the plugin from the perspective of the different stakeholders and to dispel any privacy concerns users of the plugin might have. - The game, [Get Bad News](https://www.getbadnews.com/#intro) houses a [handy booklet](https://www.getbadnews.com/wp-content/uploads/2019/03/Bad-News-Game-info-sheet-for-educators-English.pdf) on its About page on what disinformation is, how it's being tackled by different authorities and how to properly utilise its tool, amidst other relevant information. While the length of the document may come in the way of accessibilty, the way it talks about disinformation (by referring users to current tools and solutions) instead of trying to define it, can be a helpful tactic. -- This online educating tool () posits similarly sensitive information but by focusing on its use cases, features, and stakeholders. Another similar product- and feature-packed explanation is available here () -- The simplicity and messaging of this web plugin is to be admired ().\ +- This [online educating tool](https://get.checkology.org/) posits similarly sensitive information but by focusing on its use cases, features, and stakeholders. Another similar product- and feature-packed explanation is available [here](https://idir.uta.edu/claimbuster/) +- The simplicity and messaging of this [web plugin](https://adobserver.org/) is to be admired.\ It is available in one other language, is contained in one webpage only and has no links to anywhere else. The content is put across in a survivor-centric manner and is empowering such as through messaging like “how you can help.” It takes a very personal experience and topic (political advertisements) and handles it very deftly while still being sensitive. -- This community-centric tool () focuses on feelings of belonging and safety. It highlights user experiences and features of the tool as the main representations on the website. Another potentiality for Uli could be quotes from users of the plugin. +- This [community-centric tool](https://iheartmob.org/) focuses on feelings of belonging and safety. It highlights user experiences and features of the tool as the main representations on the website. Another potentiality for Uli could be quotes from users of the plugin. Overall Discussion points:: @@ -29,3 +33,5 @@ Overall Discussion points:: - Highlight use-cases and stakeholders. - Empower users through educating and disseminating relevant, user-centric, remedy- and action-focused content. - Diagrammatic representations and process flow diagrams are the simplest and easiest to understand in an alerted/agitated state of mind. + + \ No newline at end of file diff --git a/uli-website/src/pages/blog/tech4dev-user-research-lessons.mdx b/uli-website/src/pages/blog/tech4dev-user-research-lessons.mdx index 3cf9a5e3..332dab25 100644 --- a/uli-website/src/pages/blog/tech4dev-user-research-lessons.mdx +++ b/uli-website/src/pages/blog/tech4dev-user-research-lessons.mdx @@ -6,6 +6,10 @@ project: date: 18-04-2022 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + ## Lessons in User Research from Donald Lobo There is a famous quote attributed to Edison: “I have not failed, not once.  I’ve discovered ten thousand ways that don’t work.” This quote was his defense for the space for experimentation even if it doesn’t get you results right away. @@ -31,3 +35,5 @@ I was expressing my frustration/aspiration with Donald Lobo in a meeting earlier * I asked if Project Tech4Dev ever had to convince their partners that something that they are facing is a problem that can be fixed. Lobo said that for smaller things convincing is easy. But when it comes to the platforms they build, they don't try to convince the partner. If they have to convince then the partner isn't a good partner for the user-study. I believe that in the last point, Uli will vary from Tech4Dev's project since we're also trying to show new possibilities in online moderation and advocacy. So, there is some 'convincing' involved which introduces some tension in the user-research process. But for the most part, I found the pointed and specific advice something all social impact projects would find helpful. + + \ No newline at end of file diff --git a/uli-website/src/pages/blog/uli-privacy.mdx b/uli-website/src/pages/blog/uli-privacy.mdx index d6ec7f04..6f9b5dad 100644 --- a/uli-website/src/pages/blog/uli-privacy.mdx +++ b/uli-website/src/pages/blog/uli-privacy.mdx @@ -6,6 +6,10 @@ project: date: 15-07-2022 --- +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + # Privacy Preserving Features of Uli - Authors: Denny George and Sudeep Duggal \* @@ -36,3 +40,5 @@ If you find any flaws in our approach, have suggestions for improvements or spot [1] This code was prototyped in python and then ported to run on web browsers using the pyodide project. Code can be studied [here](https://github.com/tattle-made/OGBV/commit/35ce9e15e230e7a2aa247b7fc448bd733a831a85). [2] There is currently an [open issue](https://github.com/tattle-made/OGBV/issues/141) on our github repository tracking progress on this. Feel free to participate there if you wish to get further clarification or have any input on how we could achieve this functionality locally. [3] The code for this service can be audited [here](https://github.com/tattle-made/OGBV/tree/main/ogbv-ml-rest). + + \ No newline at end of file diff --git a/uli-website/src/pages/faq.mdx b/uli-website/src/pages/faq.mdx index a2d19274..56daf6ed 100644 --- a/uli-website/src/pages/faq.mdx +++ b/uli-website/src/pages/faq.mdx @@ -1,5 +1,8 @@ +import ContentPageShell from "../components/molecules/ContentPageShell.jsx" import { Box } from "grommet"; + + # FAQ @@ -91,3 +94,5 @@ Since the ML model is a work in progress, we understand that it can feel clunky ## You have the custom slur feature, why was ML needed at all for this tool? We wanted to develop this model as a proof of concept which can be used to demand responsible algorithmic designs that take into account the concerns of the communities and make a case for more investment from social media companies in non-English languages. + + \ No newline at end of file diff --git a/uli-website/src/pages/hi/about.mdx b/uli-website/src/pages/hi/about.mdx index 666278f4..277cc376 100644 --- a/uli-website/src/pages/hi/about.mdx +++ b/uli-website/src/pages/hi/about.mdx @@ -1,3 +1,7 @@ +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + ## उली क्या है? ऑनलाइन दुनिया को नेविगेट करना तेजी से ऑफ़लाइन दुनिया का प्रतिबिंब बन गया है: सभी लिंग और यौन अल्पसंख्यकों के खिलाफ लक्षित लिंग आधारित हिंसा अब आम बात है। उली, तमिल भाषा में जिसका अर्थ है छेनी, ऑनलाइन लिंग-आधारित हिंसा से सबसे अधिक प्रभावित यूज़र्स को लड़ने की कुछ क्षमता प्रदान करता है। उली हम में से प्रत्येक को इकट्ठा होने और अपने सोशल मीडिया प्लेटफार्म के अनुभव को नियंत्रित करने के लिए आमंत्रित करता है। उली के साथ, हम एक जिम्मेदार और सक्रिय तकनीक की मांग करने का एक आधार तैयार कर रहे हैं । @@ -5,3 +9,5 @@ यह प्लगइन विभिन्न समूहों, संगठनों और आंदोलनों के सामूहिक प्रयासों से प्रेरित हुआ हैं जो हम में से प्रत्येक को सशक्त बनाने का प्रयास करते हैं। इसका जन्म जाति, धर्म, लिंग आधारित हिंसा के खिलाफ संघर्ष में लगे पत्रकारों, कार्यकर्ताओं, सामुदायिक प्रभावकों और लेखकों के सामूहिक श्रम से हुआ है । इस प्लगइन के विकास में उनका योगदान हमें आपत्तिजनक शब्दों/वाक्यांशों को फ़िल्टर करने, मदद के लिए हमारे दोस्तों को कॉल करने या ऑनलाइन बातचीत शुरू करने, और समस्याग्रस्त ट्वीट्स को संग्रहीत करने में मदद करता है। यह प्लगइन इस बात का चिन्ह है कि हम अपने ऑनलाइन अनुभव को खुद नियंत्रित कर सकते है। उली एक साधारण उपकरण है, एक छेनी, जो किसी को अपना एक कमरा बनाने में मदद करता है या एक आंगन जहां लोग इकट्ठा हो सकते हैं, अपना एक कांथा बुन सकते हैं, आपस में कहानियां साझा कर सकते हैं, और उस भविष्य की कलपना कर सकते हैं जिसे हम सभी देखना चाहते हैं। + + diff --git a/uli-website/src/pages/hi/faq.mdx b/uli-website/src/pages/hi/faq.mdx index 90eaaba3..30c9233e 100644 --- a/uli-website/src/pages/hi/faq.mdx +++ b/uli-website/src/pages/hi/faq.mdx @@ -1,5 +1,8 @@ +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" import { Box } from "grommet"; + + # सामान्यतः पूछे जाने वाले प्रश्न: @@ -85,3 +88,5 @@ import { Box } from "grommet"; ## हम इस परियोजना में कैसे योगदान कर सकते हैं ? कई तरह से। उली एक ओपन सोर्स प्रोजेक्ट है। इसलिए यदि आपके पास कोड या दस्तावेज़ीकरण का योगदान करने का समय है, तो कृपया टैटल के स्लैक चैनल पर जाएं या हमे ईमेल भेजें । आप GitHub पर रिपॉजिटरी को स्पॉंसर करके भी आर्थिक रूप से सहायता कर सकते हैं। + + \ No newline at end of file diff --git a/uli-website/src/pages/privacy-policy.mdx b/uli-website/src/pages/privacy-policy.mdx index c2630560..c38c6e69 100644 --- a/uli-website/src/pages/privacy-policy.mdx +++ b/uli-website/src/pages/privacy-policy.mdx @@ -1,3 +1,7 @@ +import ContentPageShell from "../components/molecules/ContentPageShell.jsx" + + + # Privacy Policy ## Chrome Extension: @@ -24,3 +28,5 @@ On resetting your account, the data for the account is deleted within a month. O ## Website Analytics: This website, like all Tattle websites, uses [Plausible Analytics](https://plausible.io/), a GDPR compliant open source web analytics library. Plausible is a privacy first analytical tool that does not use cookies and does not collect any personal or personally identifiable information. + + \ No newline at end of file diff --git a/uli-website/src/pages/research.mdx b/uli-website/src/pages/research.mdx index cfaca8a6..ab8c822f 100644 --- a/uli-website/src/pages/research.mdx +++ b/uli-website/src/pages/research.mdx @@ -1,3 +1,7 @@ +import ContentPageShell from "../components/molecules/ContentPageShell.jsx" + + + # Research 1. Mehta, T., & Chakraborty, S. (2023). Visuais Para Tecnologia Emancipatória: Um Estudo de Caso Sobre a Cocriação de uma Linguagem Visual Para Combater a Violência de Género Online no Twitter Indiano. Vista, (11), e023007. [https://doi.org/10.21814/vista.4133](https://doi.org/10.21814/vista.4133) @@ -13,3 +17,5 @@ 2. [How Desi Troll Armies have built a Coded Lexicon of Slurs](https://timesofindia.indiatimes.com/india/how-desi-troll-armies-have-built-a-coded-language-of-abuse/articleshow/89406114.cms) 3. [Facebook’s language problem. Rest of World. Newsletter](https://restofworld.org/2021/newsletter-south-asia-facebooks-language-problem/) 4. [Putting hate speech on mute: An Indian project helps social media users filter out abuse to feel safe online](https://akademie.dw.com/en/putting-hate-speech-on-mute-an-indian-project-helps-social-media-users-filter-out-abuse-to-feel-safe-online/a-65636500) + + \ No newline at end of file diff --git a/uli-website/src/pages/resources.mdx b/uli-website/src/pages/resources.mdx index c17debf2..b0936b0a 100644 --- a/uli-website/src/pages/resources.mdx +++ b/uli-website/src/pages/resources.mdx @@ -1,3 +1,7 @@ +import ContentPageShell from "../components/molecules/ContentPageShell.jsx" + + + # Resources 1. Understand, Report, Archive and Mitigate online gender and sexual abuse. @@ -11,3 +15,5 @@ 3. The Crowdsourced List of Slurs in Hindi, Tamil and Indian English: - [On GitHub](https://github.com/tattle-made/OGBV/blob/main/browser-extension/plugin/scripts/slur-list.txt) + + \ No newline at end of file diff --git a/uli-website/src/pages/ta/about.mdx b/uli-website/src/pages/ta/about.mdx index 0a34cec2..9efb7a7f 100644 --- a/uli-website/src/pages/ta/about.mdx +++ b/uli-website/src/pages/ta/about.mdx @@ -1,3 +1,7 @@ +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" + + + ## உளி பற்றி ஆன்லைன் உலகில் வழிசெலுத்துவது, ஆஃப்லைன் உலகத்தைப் பிரதிபலிப்பதாக மாறியுள்ளது: அனைத்து பாலினம் மற்றும் பாலியல் சிறுபான்மையினருக்கு எதிரான பாலினம் சார்ந்த வன்முறை இப்போது பொதுவானது. உளி (ஒரு வகையான சுத்தியல்) என்பது ஆன்லைன் பாலினம் சார்ந்த வன்முறை / ஆன்லைன் பாலின அடிப்படையிலான வன்முறையால் பாதிக்கப்பட்ட பயனர்களுக்கு ஒரு விதமான அதிகாரத்தை ஒப்படைக்கும் எங்கள் முயற்சியாகும். உளி நம் ஒவ்வொருவரையும் சமூக வலைதளங்களில் ஒன்றுகூடி நம்முடைய அனுபவத்தைக் கட்டுப்படுத்த அழைக்கிறது. உளி மூலம், அதிகாரம் உள்ளவர்களிடம் இருந்து அதிக பொறுப்பான மற்றும் செயலூக்கமுள்ள தொழில்நுட்பத்தைக் கோருவதற்கான அடித்தளத்தை நாங்கள் அமைத்துள்ளோம். @@ -5,3 +9,5 @@ இந்த பிளகின், நம் ஒவ்வொருவருக்கும் அதிகாரம் அளிக்க பாடுபடும் பல்வேறு குழுக்கள், அமைப்புகள் மற்றும் இயக்கங்களின் முயற்சிகளுக்கு ஒரு அடையாளமாகவும் இருக்கிறது. இணையத்திலும் ஆஃப்லைனிலும் பின்னிப்பிணைந்த ஜாதி, மதம், பாலினம் மற்றும் பாலியல் அடிப்படையிலான வன்முறைகளுக்கு எதிரான போராட்டத்தில் ஈடுபட்ட பத்திரிகையாளர்கள், ஆர்வலர்கள், சமூகத்தின் செல்வாக்கு செலுத்துபவர்கள் மற்றும் எழுத்தாளர்களின் கூட்டு உழைப்பில் இருந்து பிறந்தது. புண்படுத்தும் வார்த்தைகள்/சொற்றொடர்களை வடிகட்டவும், உதவிக்கு எங்கள் நண்பர்களை அழைக்கவும் அல்லது ஆன்லைன் உரையாடல்களை தொடங்கவும், பிரச்சனைக்குரிய ட்வீட்களை காப்பகப்படுத்தவும் அனுமதிக்கும் இந்த பிளகினின் வளர்ச்சிக்கு அவர்கள் பங்களித்துள்ளனர். இந்த பிளகின், இடைவெளிகளை மீட்டெடுக்கும் மற்றும் எங்கள் ஆன்லைன் அனுபவத்தின் கட்டுப்பாட்டை எடுப்பதற்கான எங்களின் திறனை நினைவூட்டுகிறது. உளி ஒரு தயாரிப்பு அல்ல, இது ஒரு எளிய கருவியாகும், இது ஒருவருக்கு சொந்தமாக ஒரு அறையை அல்லது தனி இடங்களை உருவாக்க அனுமதிக்கிறது, அங்கு மக்கள் ஒன்று கூடலாம், கதைகளைப் பகிர்ந்து கொள்ளலாம் மற்றும் நாம் அனைவரும் பார்க்க விரும்பும் எதிர்காலத்தைப் பற்றி சிந்திக்கலாம். + + \ No newline at end of file diff --git a/uli-website/src/pages/ta/faq.mdx b/uli-website/src/pages/ta/faq.mdx index 61fc7f34..842a4776 100644 --- a/uli-website/src/pages/ta/faq.mdx +++ b/uli-website/src/pages/ta/faq.mdx @@ -1,5 +1,8 @@ +import ContentPageShell from "../../components/molecules/ContentPageShell.jsx" import { Box } from "grommet"; + + # அடிக்கடி கேட்கப்படும் கேள்விகள் @@ -94,3 +97,5 @@ ML மாடல் இன்னும் மேம்படுத்தப்ப ## உங்களிடம் தனிப்பட்ட அவதூறு மதிப்பீடு அம்சம் உள்ளது, இந்த கருவிக்கு ஏன் ML தேவைப்பட்டது? சமூகப் பிரச்சனைகளைக் கணக்கில் எடுத்துக்கொண்டு, ஆங்கிலம் அல்லாத மொழிகளில் சமூக ஊடக நிறுவனங்களிடமிருந்து அதிக முதலீட்டிற்கு ஒரு வழக்கை உருவாக்கும் பொறுப்பான வழிமுறை வடிவமைப்புகளைக் கோருவதற்குப் பயன்படுத்தக்கூடிய அமைப்பை ஆதாரமாகக் கொண்டு இந்த மாதிரியை உருவாக்க நாங்கள் விரும்பினோம். + + \ No newline at end of file diff --git a/uli-website/src/pages/team-2021.mdx b/uli-website/src/pages/team-2021.mdx index 82aad784..922ea06e 100644 --- a/uli-website/src/pages/team-2021.mdx +++ b/uli-website/src/pages/team-2021.mdx @@ -1,5 +1,8 @@ +import ContentPageShell from "../components/molecules/ContentPageShell.jsx" import MultiLingualAbout from "../components/atoms/MultiLingualAbout"; + + ## The pilot The pilot Uli tool was created by The [Centre for Internet and Society (CIS)](https://cis-india.org/) and [Tattle Civic Tech](https://tattle.co.in/) with support from the Digital Society Challenge grant. It was as much a pilot of a tool as it was of a process. You can read more about the process here: [https://tattle.co.in/products/ogbv](https://tattle.co.in/products/ogbv) @@ -38,3 +41,5 @@ The dataset of nearly 24000 tweets that drives the abuse redaction feature was a **Dharini Priscilla** : Gender Consultant/Researcher **[@iThamizhi](https://twitter.com/iThamizhi)** **Grace Banu** : Trans Rights Now Collective + + \ No newline at end of file