Feature : Upload Accepted Predictions

.github/workflows/backend_build.yml

@@ -35,14 +35,6 @@ jobs:
         with:
           python-version: 3.8
 
-      - name: Get my current working dir
-        run: pwd
-
-      - name: Test env vars for python
-        env:
-          TESTING_TOKEN: ${{ secrets.TESTING_TOKEN }}
-        run: python -c "import os; print(os.environ['TESTING_TOKEN'])"
-
       - name: Clone Ramp
         run: git clone https://github.com/kshitijrajsharma/ramp-code-fAIr.git ramp-code
 
@@ -92,7 +84,6 @@ jobs:
       - name: Create env
         run: |
           cd backend/
-          mv sample_env .env
           export DATABASE_URL=postgis://admin:password@localhost:5432/ai
           export RAMP_HOME="/home/runner/work/fAIr/fAIr"
           export TRAINING_WORKSPACE="/home/runner/work/fAIr/fAIr/backend/training"
@@ -120,6 +111,9 @@ jobs:
       - name: Run migrations
         env:
           TESTING_TOKEN: ${{ secrets.TESTING_TOKEN }}
+          OSM_CLIENT_ID: ${{ secrets.OSM_CLIENT_ID }}
+          OSM_CLIENT_SECRET: ${{ secrets.OSM_CLIENT_SECRET }}
+          OSM_SECRET_KEY: ${{ secrets.OSM_SECRET_KEY }}
         run: |
           cd backend/
           python manage.py makemigrations
@@ -134,7 +128,7 @@ jobs:
           TESTING_TOKEN: ${{ secrets.TESTING_TOKEN }}
           OSM_CLIENT_ID: ${{ secrets.OSM_CLIENT_ID }}
           OSM_CLIENT_SECRET: ${{ secrets.OSM_CLIENT_SECRET }}
-          OSM_SECRET_KEY: ""
+          OSM_SECRET_KEY: ${{ secrets.OSM_SECRET_KEY }}
 
         run : |
           cd backend/

backend/Dockerfile.API

@@ -0,0 +1,62 @@
+## docker build -t fair-api -f Dockerfile.API . 
+
+## For Development:
+
+## docker run --env-file .env --rm -p 8000:8000 -v $(pwd):/app/code --name fair-api-container fair-api
+
+## in order to access localhost port to your system from docker env you might wanna use : host.docker.internal or ip 
+
+FROM python:3.11-slim-bookworm AS build
+
+RUN apt-get update && apt-get --no-install-recommends -y install \
+    build-essential \
+    libpq-dev \
+    libgdal-dev \
+    python3-dev \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+ENV CPLUS_INCLUDE_PATH=/usr/include/gdal
+ENV C_INCLUDE_PATH=/usr/include/gdal
+
+WORKDIR /app
+
+RUN python3 -m venv /app/venv
+
+RUN /app/venv/bin/pip install --no-cache-dir --upgrade pip setuptools wheel
+
+COPY api-requirements.txt api-requirements.txt ./
+
+RUN /app/venv/bin/pip install --no-cache-dir -r api-requirements.txt
+
+WORKDIR /app/code
+COPY aiproject /app/code/aiproject
+COPY core /app/code/core
+COPY login /app/code/login
+COPY manage.py /app/code/manage.py
+COPY tests /app/code/tests
+
+FROM python:3.11-slim-bookworm
+
+RUN apt-get update && apt-get --no-install-recommends -y install \
+    libgdal-dev \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+ENV CPLUS_INCLUDE_PATH=/usr/include/gdal
+ENV C_INCLUDE_PATH=/usr/include/gdal
+
+WORKDIR /app
+
+COPY --from=build /app/venv /app/venv
+
+COPY --from=build /app/code /app/code
+ENV PATH="/app/venv/bin:$PATH"
+COPY api-entrypoint.sh ./api-entrypoint-lock.sh
+RUN chmod +x ./api-entrypoint-lock.sh
+
+WORKDIR /app/code
+
+EXPOSE 8000
+
+ENTRYPOINT ["/app/api-entrypoint-lock.sh"]
+
+CMD ["python", "manage.py", "runserver", "0.0.0.0:8000"]

backend/aiproject/settings.py

@@ -56,7 +56,9 @@
 # Limiter
 EPOCHS_LIMIT = env("EPOCHS_LIMIT", default=30)
 BATCH_SIZE_LIMIT = env("BATCH_SIZE_LIMIT", default=8)
-TRAINING_WORKSPACE_DOWNLOAD_LIMIT = env("TRAINING_WORKSPACE_DOWNLOAD_LIMIT", default=200)
+TRAINING_WORKSPACE_DOWNLOAD_LIMIT = env(
+    "TRAINING_WORKSPACE_DOWNLOAD_LIMIT", default=200
+)
 
 
 # Application definition
@@ -98,7 +100,7 @@
 
 CORS_ORIGIN_WHITELIST = ALLOWED_ORIGINS
 
-CORS_ORIGIN_ALLOW_ALL = env("CORS_ORIGIN_ALLOW_ALL", default= False)
+CORS_ORIGIN_ALLOW_ALL = env("CORS_ORIGIN_ALLOW_ALL", default=False)
 
 REST_FRAMEWORK = {
     "DEFAULT_SCHEMA_CLASS": "rest_framework.schemas.coreapi.AutoSchema",
@@ -205,10 +207,16 @@
     }
 }
 # get ramp home and set it to environ
-RAMP_HOME = env("RAMP_HOME")
-os.environ["RAMP_HOME"] = RAMP_HOME
+RAMP_HOME = env("RAMP_HOME",default=None)
+if RAMP_HOME:
+    os.environ["RAMP_HOME"] = RAMP_HOME
 
 # training workspace
 TRAINING_WORKSPACE = env(
     "TRAINING_WORKSPACE", default=os.path.join(os.getcwd(), "training")
 )
+
+ENABLE_PREDICTION_API = env("ENABLE_PREDICTION_API", default=False)
+
+
+TEST_RUNNER = 'tests.test_runners.NoDestroyTestRunner'

backend/api-entrypoint.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+set -e 
+echo "Applying database migrations..."
+python manage.py makemigrations login core
+python manage.py migrate
+echo "Starting Django server..."
+exec "$@"

backend/api-requirements.txt

@@ -1,6 +1,6 @@
 django==4.1.4
 # gdal==3.6.2
-psycopg2
+psycopg2==2.9.9
 djangorestframework==3.14.0
 djangorestframework-gis==1.0
 dj-database-url==1.2.0
@@ -20,4 +20,8 @@ geojson2osm==0.0.1
 osmconflator==0.0.11
 orthogonalizer==0.0.4
 fairpredictor==0.0.26
-tflite-runtime==2.14.0
+
+rasterio==1.3.8
+numpy<2.0.0
+
+

backend/core/models.py

@@ -2,6 +2,7 @@
 from django.contrib.postgres.fields import ArrayField
 from django.core.validators import MaxValueValidator, MinValueValidator
 from django.db import models
+
 from login.models import OsmUser
 
 # Create your models here.
@@ -133,3 +134,14 @@ class FeedbackLabel(models.Model):
 
     geom = geomodels.PolygonField(srid=4326)
     created_at = models.DateTimeField(auto_now_add=True)
+
+
+class ApprovedPredictions(models.Model):
+    training = models.ForeignKey(Training, to_field="id", on_delete=models.DO_NOTHING)
+    config = models.JSONField(
+        null=True, blank=True
+    )  ### Config meant to be kept for vectorization config / zoom config , to know what user is using for the most of the time
+    geom = geomodels.GeometryField(
+        srid=4326
+    )  ## Making this geometry field to support point/line prediction later on
+    approved_at = models.DateTimeField(auto_now_add=True)

backend/core/serializers.py

@@ -1,10 +1,11 @@
 from django.conf import settings
-from login.models import OsmUser
 from rest_framework import serializers
 from rest_framework_gis.serializers import (
     GeoFeatureModelSerializer,  # this will be used if we used to serialize as geojson
 )
 
+from login.models import OsmUser
+
 from .models import *
 
 # from .tasks import train_model
@@ -113,6 +114,13 @@ class Meta:
         # read_only_fields = ("created_at", "osm_id")
 
 
+class ApprovedPredictionsSerializer(GeoFeatureModelSerializer):
+    class Meta:
+        model = ApprovedPredictions
+        geo_field = "geom"
+        fields = "__all__"
+
+
 class FeedbackLabelSerializer(GeoFeatureModelSerializer):
     class Meta:
         model = FeedbackLabel

backend/core/tasks.py

@@ -7,8 +7,13 @@
 import traceback
 from shutil import rmtree
 
-
 from celery import shared_task
+from django.conf import settings
+from django.contrib.gis.db.models.aggregates import Extent
+from django.contrib.gis.geos import GEOSGeometry
+from django.shortcuts import get_object_or_404
+from django.utils import timezone
+
 from core.models import AOI, Feedback, FeedbackAOI, FeedbackLabel, Label, Training
 from core.serializers import (
     AOISerializer,
@@ -18,12 +23,6 @@
     LabelFileSerializer,
 )
 from core.utils import bbox, is_dir_empty
-from django.conf import settings
-from django.contrib.gis.db.models.aggregates import Extent
-from django.contrib.gis.geos import GEOSGeometry
-from django.shortcuts import get_object_or_404
-from django.utils import timezone
-from predictor import download_imagery, get_start_end_download_coords
 
 logger = logging.getLogger(__name__)
 
@@ -81,17 +80,20 @@ def train_model(
     input_contact_spacing=8,
     input_boundary_width=3,
 ):
-    #importing them here so that it won't be necessary when sending tasks ( api only)
+    # importing them here so that it won't be necessary when sending tasks ( api only)
     import hot_fair_utilities
     import ramp.utils
     import tensorflow as tf
     from hot_fair_utilities import preprocess, train
     from hot_fair_utilities.training import run_feedback
+    from predictor import download_imagery, get_start_end_download_coords
 
     training_instance = get_object_or_404(Training, id=training_id)
     training_instance.status = "RUNNING"
     training_instance.started_at = timezone.now()
     training_instance.save()
+    if settings.RAMP_HOME is None:
+        raise ValueError("Ramp Home is not configured")
 
     try:
         ## -----------IMAGE DOWNLOADER---------

backend/core/urls.py

@@ -1,11 +1,12 @@
+from django.conf import settings
 from django.conf.urls import include
 from django.urls import path
 from rest_framework import routers
 
 # now import the views.py file into this code
-from .views import (
+from .views import (  # APIStatus,
     AOIViewSet,
-    # APIStatus,
+    ApprovedPredictionsViewSet,
     ConflateGeojson,
     DatasetViewSet,
     FeedbackAOIViewset,
@@ -16,7 +17,6 @@
     GenerateGpxView,
     LabelViewSet,
     ModelViewSet,
-    PredictionView,
     RawdataApiAOIView,
     RawdataApiFeedbackView,
     TrainingViewSet,
@@ -28,11 +28,15 @@
     run_task_status,
 )
 
+if settings.ENABLE_PREDICTION_API:
+    from .views import PredictionView
+
 # CURD Block
 router = routers.DefaultRouter()
 router.register(r"dataset", DatasetViewSet)
 router.register(r"aoi", AOIViewSet)
 router.register(r"label", LabelViewSet)
+router.register(r"approved-prediction", ApprovedPredictionsViewSet)
 router.register(r"training", TrainingViewSet)
 router.register(r"model", ModelViewSet)
 router.register(r"feedback", FeedbackViewset)
@@ -50,7 +54,6 @@
     # path("download/<int:dataset_id>/", download_training_data),
     path("training/status/<str:run_id>/", run_task_status),
     path("training/publish/<int:training_id>/", publish_training),
-    path("prediction/", PredictionView.as_view()),
     path("feedback/training/submit/", FeedbackView.as_view()),
     # path("status/", APIStatus.as_view()),
     path("geojson2osm/", geojson2osmconverter, name="geojson2osmconverter"),
@@ -65,3 +68,5 @@
     ),
     path("workspace/<path:lookup_dir>/", TrainingWorkspaceView.as_view()),
 ]
+if settings.ENABLE_PREDICTION_API:
+    urlpatterns.append(path("prediction/", PredictionView.as_view()))

backend/core/utils.py

@@ -63,7 +63,11 @@ def __init__(self, BASE_API_URL):
         self.BASE_API_URL = BASE_API_URL
 
     def request_snapshot(self, geometry):
-        headers = {"accept": "application/json", "Content-Type": "application/json"}
+        headers = {
+            "accept": "application/json",
+            "Content-Type": "application/json",
+            "Referer": "fAIr",
+        }
         # Lets start with buildings for now
         payload = {
             "geometry": json.loads(geometry),
@@ -124,9 +128,7 @@ def process_rawdata(file_download_url, aoi_id, feedback=False):
     """This will create temp directory , Downloads file from URL provided,
     Unzips it Finds a geojson file , Process it and finally removes
     processed Geojson file and downloaded zip file from Directory"""
-    headers = {
-        'Referer': 'https://fair-dev.hotosm.org/' # TODO : Use request uri 
-    }
+    headers = {"Referer": "https://fair-dev.hotosm.org/"}  # TODO : Use request uri
     r = requests.get(file_download_url, headers=headers)
     # Check whether the export path exists or not
     path = "temp/"
@@ -250,7 +252,7 @@ def process_geojson(geojson_file_path, aoi_id, feedback=False):
     )  # leave one cpu free always
     if feedback:
         FeedbackLabel.objects.filter(feedback_aoi__id=aoi_id).delete()
-    else : 
+    else:
         Label.objects.filter(aoi__id=aoi_id).delete()
     # max_workers = os.cpu_count()  # get total cpu count available on the