Merge pull request #111 from DevMed22/Back-end

MCI Model API

backend/prediction/admin.py

@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.

backend/prediction/apps.py

@@ -0,0 +1,6 @@
+from django.apps import AppConfig
+
+
+class PredictionConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'prediction'

backend/prediction/models.py

@@ -0,0 +1,3 @@
+from django.db import models
+
+# Create your models here.

backend/prediction/serializers.py

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+# serializers.py
+from rest_framework import serializers
+
+class ImageUploadSerializer(serializers.Serializer):
+    image = serializers.ImageField()

backend/prediction/tests.py

@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.

backend/prediction/urls.py

@@ -0,0 +1,7 @@
+# urls.py
+from django.urls import path
+from .views import PredictionView
+
+urlpatterns = [
+    path('api/predict/', PredictionView.as_view(), name='predict'),
+]

backend/prediction/views.py

@@ -0,0 +1,37 @@
+# views.py
+from rest_framework.views import APIView
+from rest_framework.response import Response
+from PIL import Image
+import io
+import os
+from django.conf import settings
+from .serializers import ImageUploadSerializer
+import tensorflow as tf
+model_path = os.path.join(settings.BASE_DIR, 'MCI Model', 'mci-resnet.h5')
+
+class PredictionView(APIView):
+    def post(self, request):
+        # Load the serialized DL model from disk
+        model = tf.keras.models.load_model(model_path)
+
+        # Process the API request
+        serializer = ImageUploadSerializer(data=request.data)
+        serializer.is_valid(raise_exception=True)
+        image = serializer.validated_data['image']
+
+        # Pre-process the image
+        img = Image.open(image)
+        # Perform any necessary pre-processing on the image (e.g., resizing, normalization)
+
+        # Convert the image to the input format expected by the DL model
+        img = img.resize((224, 224))  # Example: Resize the image to 224x224 pixels
+        img = img.convert('RGB')  # Example: Convert to RGB color space
+        img = tf.keras.preprocessing.image.img_to_array(img)
+        img = tf.expand_dims(img, axis=0)
+
+        # Pass the pre-processed image to the DL model for prediction
+        result = model.predict(img)
+        # Format the result as a response
+
+        # Return the API response
+        return Response({'result': result})

backend/root/urls.py

@@ -7,7 +7,7 @@
 urlpatterns = [
     path('admin/', admin.site.urls),
     path('api/v1/', include('api.urls')),
-
+    path('prediction/api/v1/', include('prediction.urls')),
     path('', include('pages.urls')),
     path('accounts/', include('django.contrib.auth.urls')),
     path('accounts/', include('accounts.urls')),