Update app.py

app.py

@@ -12,6 +12,7 @@ from fastapi.middleware.cors import CORSMiddleware
 
 # Load your trained model
 model = tf.keras.models.load_model('recyclebot.keras')
+model2 = tf.keras.models.load_model('78-76.keras')
 
 # Define class names for predictions (this should be the same as in your local code)
 CLASSES = ['Glass', 'Metal', 'Paperboard', 'Plastic-Polystyrene', 'Plastic-Regular']
@@ -55,10 +56,15 @@ def preprocess_image(image_file):
 async def predict(file: UploadFile = File(...)):   #async def predict(request: Request, file: UploadFile = File(...)):
     try:
         img_array = preprocess_image(file.file)  # Preprocess the image
-        predictions = model.predict(img_array)  # Get predictions
+        prediction1 = model.predict(img_array)  # Get predictions
+        prediction1 = model2.predict(img_array)  # Get predictions
+
+        weight_1 = 0.6
+        weight_2 = 0.4
         
+        final_preds =     final_preds = (weight_1 * prediction1 + weight_2 * prediction2)
         # Get the index of the highest probability class (like np.argmax on local machine)
-        predicted_class_idx = np.argmax(predictions, axis=1)[0]  # Get predicted class index
+        predicted_class_idx = np.argmax(final_preds, axis=1)[0]  # Get predicted class index
         
         # Map the predicted index to the class name (like final_class = CLASSES[np.argmax(final_preds)])
         predicted_class = CLASSES[predicted_class_idx]  # Convert to class name
@@ -72,7 +78,7 @@ async def predict(file: UploadFile = File(...)):   #async def predict(request: R
 
 @app.get("/working")
 async def working():
-    return JSONResponse(content={"Response": "Received"})
+    return JSONResponse(content={"Status": "Working"})