Update app.py

app.py

@@ -48,34 +48,42 @@ transform = transforms.Compose([
 
 def predict(data):
     try:
-        # Expecting data to be a list
+        # Check if the data is a list and not empty
         if not isinstance(data, list) or len(data) == 0:
             return json.dumps({"error": "Input data should be a non-empty list."})
         
+        # Extract the image path
         image_input = data[0].get('path', None)
         if not image_input:
-            return json.dumps({"error": "No image provided."})
+            return json.dumps({"error": "No image path provided."})
 
         print(f"Received image input: {image_input}")
 
-        # Check if the input is a URL
-        if isinstance(image_input, str):
-            if image_input.startswith("http://") or image_input.startswith("https://"):
-                try:
-                    response = requests.get(image_input)
-                    response.raise_for_status()  # Check for HTTP errors
-                    image = Image.open(BytesIO(response.content))
-                    print(f"Fetched image from URL: {image}")
-                except Exception as e:
-                    print(f"Error fetching image from URL: {e}")
-                    return json.dumps({"error": f"Error fetching image from URL: {e}"})
-            else:
-                return json.dumps({"error": "Invalid URL format. Please provide a valid URL starting with 'http://' or 'https://'."})
+        # Handle URLs
+        if isinstance(image_input, str) and (image_input.startswith("http://") or image_input.startswith("https://")):
+            try:
+                response = requests.get(image_input)
+                response.raise_for_status()  # Check for HTTP errors
+                image = Image.open(BytesIO(response.content))
+                print(f"Fetched image from URL: {image}")
+            except Exception as e:
+                print(f"Error fetching image from URL: {e}")
+                return json.dumps({"error": f"Error fetching image from URL: {e}"})
+        
+        # Check if the image path is a valid local path
+        elif isinstance(image_input, str) and os.path.exists(image_input):
+            try:
+                image = Image.open(image_input)
+                print(f"Loaded image from local path: {image}")
+            except Exception as e:
+                return json.dumps({"error": f"Error loading image from local path: {e}"})
+        
+        else:
+            return json.dumps({"error": "Invalid image path. Ensure it's a valid URL or local path."})
 
-        # Apply transformations
+        # Apply the transformations and make prediction
         image = transform(image).unsqueeze(0)
         print(f"Transformed image tensor: {image.shape}")
-
         image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
 
         with torch.no_grad():
@@ -83,25 +91,15 @@ def predict(data):
             predicted_class = torch.argmax(outputs, dim=1).item()
             print(f"Prediction output: {outputs}, Predicted class: {predicted_class}")
 
+        # Return the result based on the predicted class
         if predicted_class == 0:
             return json.dumps({"result": "The photo you've sent is of fall army worm with problem ID 126."})
         elif predicted_class == 1:
             return json.dumps({"result": "The photo you've sent is of a healthy maize image."})
         else:
             return json.dumps({"error": "Unexpected class prediction."})
+    
     except Exception as e:
         print(f"Error processing image: {e}")
         return json.dumps({"error": f"Error processing image: {e}"})
 
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.JSON(label="Input JSON"),
-    outputs=gr.Textbox(label="Prediction Result"),
-    live=True,
-    title="Maize Anomaly Detection",
-    description="Upload an image of maize to detect anomalies like disease or pest infestation. You can provide local paths, URLs, or base64-encoded images."
-)
-
-# Launch the Gradio interface 
-iface.launch(share=True, show_error=True)