Update app.py

app.py

@@ -46,8 +46,7 @@ transform = transforms.Compose([
 
 def predict(image):
     try:
-        # Debugging: Print the received image
-        print(f"Received image: {image}")
+        print(f"Received image input: {image}")
 
         # Check if the input contains a base64-encoded string
         if isinstance(image, dict) and image.get("data"):
@@ -56,6 +55,7 @@ def predict(image):
                 image = Image.open(BytesIO(image_data))
                 print(f"Decoded base64 image: {image}")
             except Exception as e:
+                print(f"Error decoding base64 image: {e}")
                 return f"Error decoding base64 image: {e}"
 
         # Check if the input is a URL
@@ -65,15 +65,24 @@ def predict(image):
                 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 f"Error fetching image from URL: {e}"
 
+        # Validate that the image is correctly loaded
+        if not isinstance(image, Image.Image):
+            print("Invalid image format received.")
+            return "Invalid image format received."
+
         # Apply transformations
         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():
             outputs = model(image)
             predicted_class = torch.argmax(outputs, dim=1).item()
+            print(f"Prediction output: {outputs}, Predicted class: {predicted_class}")
 
         if predicted_class == 0:
             return "The photo you've sent is of fall army worm with problem ID 126."
@@ -82,17 +91,18 @@ def predict(image):
         else:
             return "Unexpected class prediction."
     except Exception as e:
+        print(f"Error processing image: {e}")
         return f"Error processing image: {e}"
 
 # Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Textbox(),
+    inputs=gr.Image(type="pil", label="Upload an image or provide a URL"),  # Input: Image or URL
+    outputs=gr.Textbox(label="Prediction Result"),  # Output: Predicted class
     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)
+iface.launch(share=True, show_error=True)