added grading function

.firebaserc

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+{
+  "projects": {
+    "default": "gradeease-57107"
+  }
+}

.gitignore

@@ -23,3 +23,9 @@ dist-ssr
 *.sln
 *.sw?
 .env
+
+api/firebase-SA.json
+api/gcloud-SA.json
+api/openAPI
+functions/firebase-SA.json
+functions/gcloud.SA.json

api/main.py

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+from typing import Union
+
+from fastapi import FastAPI
+
+#from readtrial import get_all_docs
+from openAPI import main as openapimain
+
+app = FastAPI()
+
+
+@app.get("/read_firebase_data")
+def read_firebase_data(param1: str, param2: str):
+    # Call the function from readtrial.py to read data from Firebase
+    #res=get_all_docs("trial1")
+    #return res
+    openapimain(param1,param2)
+    res="something"
+    return res

api/openAPI.py

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+import openai
+import re
+from firebase_admin import firestore
+from read_from_db import main as readFromDb
+
+# qnpaper_id='5'
+# anspaper_id='ans002'
+# doc_list=readFromDb(qnpaper_id,anspaper_id)
+
+# API_KEY = open("openAPI", "r").read().strip()
+# client = openai.OpenAI(api_key=API_KEY)
+
+def extract_numbers(text):
+    # Use regular expression to find all numeric parts
+    numbers = re.findall(r'\d+', text)
+    # Convert the list of strings to integers
+    numbers = [int(num) for num in numbers]
+    return numbers
+
+def update_documents(collection_name, qnpaper_id, anspaper_id, update_data, qn_no, db):
+    # Get a reference to the documents that match the query criteria
+    docs_ref = db.collection(collection_name).where("qnpaper_id", "==", qnpaper_id).where("anspaper_id", "==", anspaper_id).where("question_no", "==", qn_no).stream()
+
+    # Iterate over the documents and update each one
+    for doc in docs_ref:
+        doc_ref = db.collection(collection_name).document(doc.id)
+        doc_ref.update(update_data)
+
+def main(qnpaper_id,anspaper_id):
+    # qnpaper_id='5'
+    # anspaper_id='ans002'
+
+    doc_list=readFromDb(qnpaper_id,anspaper_id)
+
+    API_KEY = open("openAPI", "r").read().strip()
+    client = openai.OpenAI(api_key=API_KEY)
+
+    response = client.chat.completions.create(model = "gpt-3.5-turbo",
+    messages = [{"role": "user","content":"Ignore all previous instructions. Assume you are a high school teacher who is going to grade the exam answer papers of your students. Problem=Analyze each answer with respect to the answer key, the question given and maximum marks. You have to grade the answers based on two factors: 1. Whether the content of the answer is aligned with what the question has asked and the keywords given in the answer key, 2. Whether the answer contains enough number of valid points corresponding to the maximum marks. Example, if the question is to 'write about the 3 types of rocks' for 6 marks, the student has to describe each of the three types of rocks with a minimum of 2 valid points. Another example, if the question is to 'differentiate between system software and application software for 3 marks, the student has to give a minimum of 3 valid points for each type of software in order to be awarded full marks. The output should only contain the marks awarded to that specific answer. Can you acknowledge that you have read my problem by saying READ and don't say anything else yet."}])
+
+    content = response.choices[0].message.content
+
+    print(content)
+
+    for doc_data in doc_list:
+        data=doc_data['doc_data']
+        qn=data['question']
+        qn_no=data['question_no']   
+        ans_key=data['answer_key']   
+        ans=data['student_answer']
+        print(ans)
+        max_marks=data['max_marks']
+
+
+        response = client.chat.completions.create(model = "gpt-3.5-turbo",
+        messages = [{"role": "user","content":f"Question= {qn},Student answer={ans},Answer key={ans_key},Maximum marks={max_marks}, How much marks will you give for this answer considering the question, the answer key and the maximun marks?Give full marks only if number of valid points equals the maximum marks. Otherwise reduce marks accordingly. Mention the score in number."}],
+    temperature=0.2,
+    max_tokens=100)
+
+        content = response.choices[0].message.content
+        print(content)
+        marks = extract_numbers(content)
+        obtained_marks=marks[0]
+        update_data={'obtained_marks':str(obtained_marks)} 
+        print(update_data)
+        # Write data to Firestore with the specified document ID
+        db=firestore.client()
+        #MENTION THE CORRECT COLLECTION NAME
+        update_documents('gradingtrial', qnpaper_id, anspaper_id, update_data, qn_no,db)
+
+
+
+if __name__ == "__main__":
+    main()
+

api/read_from_db.py

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+from firebase_admin import firestore
+from write_to_db import main as write2db
+
+# cred = credentials.Certificate('firebase-SA.json')
+# firebase_admin.initialize_app(cred)
+
+def get_all_docs(collectionName,qnpaper_id, anspaper_id):
+    db=firestore.client()
+    docs = (
+        db.collection(collectionName)
+        .where("qnpaper_id", "==", qnpaper_id)
+        .where("anspaper_id", "==", anspaper_id)
+        .stream()
+    )
+
+    documents_list=[]
+    for doc in docs:
+        doc_data=doc.to_dict()
+        doc_data['id'] = doc.id
+        doc_data['doc_data']=doc._data
+        documents_list.append(doc_data)
+    return documents_list
+
+def main(qnpaper_id,anspaper_id):
+
+    write2db(qnpaper_id,anspaper_id)
+
+    #MENTION THE CORRECT COLLECTION NAME
+    doc_list=get_all_docs("gradingtrial",qnpaper_id,anspaper_id)
+    print("Data read from DB")
+    return doc_list

api/textrecog.py

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+import os
+import firebase_admin
+from firebase_admin import storage
+from firebase_admin import firestore
+from google.cloud import vision_v1
+import cv2
+import numpy as np
+from firebase_admin import credentials
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = r'gcloud-SA.json'
+
+# Initialize the client with explicit credentials
+client = vision_v1.ImageAnnotatorClient()
+
+def download_image_from_storage(image_id):
+    bucket = storage.bucket()
+    #trial is the name of the folder in which thr images are uploaded. CHECK if its correct
+    blob = bucket.blob(f'trial/{image_id}.jpg')  # Replace 'jpg' with the actual image format
+    image_data = blob.download_as_bytes()
+    return image_data
+
+def preprocess_image(image_data):
+    # Convert byte data to numpy array for processing
+    np_array = np.frombuffer(image_data, np.uint8)
+    # Decode numpy array into an image
+    image = cv2.imdecode(np_array, cv2.IMREAD_GRAYSCALE)
+
+    # Preprocessing steps
+    # Noise reduction
+    denoised_image = cv2.fastNlMeansDenoising(image, None, h=10, templateWindowSize=7, searchWindowSize=21)
+
+    # Binarization
+    _, binary_image = cv2.threshold(denoised_image, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+
+    return binary_image
+
+def detect_text(image_data):
+    binary_image = preprocess_image(image_data)
+    image = vision_v1.Image(content=cv2.imencode('.jpg', binary_image)[1].tobytes())
+    response = client.text_detection(image=image)
+    return response.text_annotations[0].description if response.text_annotations else None
+
+def parse_answers(text):
+    answers = {}
+    current_question = None
+    lines = text.split('\n')
+    for line in lines:
+        # Check if line starts with a numeric value followed by a period
+        if line.strip() and line.strip()[0].isdigit() and '.' in line:
+            question_number, answer = line.strip().split('.', 1)
+            current_question = int(question_number)
+            answers[current_question] = answer.strip()
+        elif current_question is not None:
+            # If current_question is set, append line to existing answer
+            answers[current_question] += ' ' + line.strip()
+    return answers
+
+def retrieve_imgid(doc_id, key, collection_name):
+
+    # Access Firestore database
+    db = firestore.client()
+
+    # Get a reference to the document
+    doc_ref = db.collection(collection_name).document(doc_id)
+
+    # Get the document snapshot
+    doc = doc_ref.get()
+
+    # Check if the document exists
+    if doc.exists:
+        # Get the value corresponding to the key
+        value = doc.to_dict().get(key)
+        if value is not None:
+            print("Value for key '{}' in document '{}' in collection '{}': {}".format(key, doc_id, collection_name, value))
+            return value
+        else:
+            print("Key '{}' not found in document '{}' in collection '{}'.".format(key, doc_id, collection_name))
+            #following stmt can be removed
+            return value
+    else:
+        print("Document '{}' not found in collection '{}'.".format(doc_id, collection_name))
+        #following stmt can be removed
+        return value
+
+
+def process_text(id, input,collectionName):
+    image_id_with_extension=retrieve_imgid(id, input, collectionName)
+    image_id = image_id_with_extension.split('.')[0]
+    image_data = download_image_from_storage(image_id)
+    detected_text = detect_text(image_data)
+    parsed_text = parse_answers(detected_text)
+    return parsed_text
+
+
+def main(qnpaper_id,anspaper_id):
+
+    cred = credentials.Certificate('firebase-SA.json')
+    firebase_admin.initialize_app(cred,{
+        'storageBucket': 'gradeease-57107.appspot.com'
+    })
+
+
+    parsed_questions=process_text(qnpaper_id, "question_paper", "trial")
+    parsed_key=process_text(qnpaper_id, "answer_key", "trial")
+    parsed_answers=process_text(anspaper_id,"answer_paper", "anspapertrial")
+    print("*******Text Recognised...**********")
+    return parsed_questions, parsed_key, parsed_answers

api/write_to_db.py

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+from firebase_admin import firestore
+from textrecog import main as textrec
+
+def convert_to_dict_qn(questions,answerkey,answers, qnpaper_id, anspaper_id):
+
+    db=firestore.client()
+    # Iterate over each question
+    for key, value in questions.items():
+        formatted_set = {}
+        question, marks = value.rsplit('(', 1)
+        # Remove ' marks)' from the marks part to get only the numeric value
+        marks = marks.replace(' marks)', '')
+        # Create the dictionary
+        formatted_set["qnpaper_id"] = qnpaper_id
+        formatted_set["anspaper_id"] = anspaper_id
+        formatted_set["question_no"] = key
+        formatted_set["question"] = question
+        formatted_set["max_marks"] = marks   
+
+        formatted_set["answer_key"] = answerkey[key]
+
+        formatted_set["student_answer"] = answers[key]
+
+        # #for testing
+        # print("printing formatted set one by one")
+        # print("formatted_set: ", formatted_set)
+
+        #MENTION THE CORRECT COLLECTION NAME
+        doc_ref = db.collection("gradingtrial").document()
+        doc_ref.set(formatted_set)
+
+
+def main(qnpaper_id, anspaper_id):
+
+    parsed_questions,parsed_key,parsed_answers = textrec(qnpaper_id,anspaper_id)
+    convert_to_dict_qn(parsed_questions,parsed_key,parsed_answers, qnpaper_id, anspaper_id)
+    print("Written to DB..")
+
+
+# if __name__ == "__main__":
+#     main()

database.rules.json

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+{
+  "rules": {
+    ".read": false,
+    ".write": false
+  }
+}

firebase.json

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+{
+  "database": {
+    "rules": "database.rules.json"
+  },
+  "firestore": {
+    "rules": "firestore.rules",
+    "indexes": "firestore.indexes.json"
+  },
+  "functions": [
+    {
+      "source": "functions",
+      "codebase": "default",
+      "ignore": [
+        "venv",
+        ".git",
+        "firebase-debug.log",
+        "firebase-debug.*.log"
+      ]
+    }
+  ],
+  "hosting": {
+    "public": "public",
+    "ignore": [
+      "firebase.json",
+      "**/.*",
+      "**/node_modules/**"
+    ],
+    "rewrites": [
+      {
+        "source": "**",
+        "destination": "/index.html"
+      }
+    ]
+  },
+  "storage": {
+    "rules": "storage.rules"
+  },
+  "emulators": {
+    "auth": {
+      "port": 9099
+    },
+    "functions": {
+      "port": 5001
+    },
+    "firestore": {
+      "port": 8080
+    },
+    "database": {
+      "port": 9000
+    },
+    "hosting": {
+      "port": 5000
+    },
+    "pubsub": {
+      "port": 8085
+    },
+    "storage": {
+      "port": 9199
+    },
+    "eventarc": {
+      "port": 9299
+    },
+    "ui": {
+      "enabled": true
+    },
+    "singleProjectMode": true
+  },
+  "remoteconfig": {
+    "template": "remoteconfig.template.json"
+  },
+  "extensions": {}
+}

firestore.indexes.json

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+{
+  "indexes": [],
+  "fieldOverrides": []
+}

firestore.rules

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+rules_version = '2';
+
+service cloud.firestore {
+  match /databases/{database}/documents {
+    match /{document=**} {
+      allow read, write: if true;
+    }
+  }
+}