Update app.py

app.py

@@ -39,6 +39,9 @@ transform = transforms.Compose([
 # Function to predict from image content
 def predict_from_image(image):
     try:
+        # Log the image processing
+        print(f"Processing image: {image}")
+        
         # Ensure the image is a PIL Image
         if not isinstance(image, Image.Image):
             raise ValueError("Invalid image format received. Please provide a valid image.")
@@ -53,41 +56,51 @@ def predict_from_image(image):
 
         # Interpret the result
         if predicted_class == 0:
-            return {"status": "success", "result": "Fall army worm detected (Problem ID: 126)."}
+            return {"result": "The photo is of fall army worm with problem ID 126."}
         elif predicted_class == 1:
-            return {"status": "success", "result": "Healthy maize image detected."}
+            return {"result": "The photo is of a healthy maize image."}
         else:
-            return {"status": "error", "message": "Unexpected class prediction."}
+            return {"error": "Unexpected class prediction."}
+
     except Exception as e:
-        return {"status": "error", "message": f"Error during prediction: {str(e)}"}
+        print(f"Error during image processing: {e}")
+        return {"error": str(e)}
 
 # Function to predict from URL
 def predict_from_url(url):
     try:
-        if not url.startswith(("http://", "https://")):
-            raise ValueError("Invalid URL format. Please provide a valid image URL.")
-        
+        # Fetch the image from the URL
         response = requests.get(url)
         response.raise_for_status()  # Ensure the request was successful
         image = Image.open(BytesIO(response.content))
+        print(f"Fetched image from URL: {url}")
         return predict_from_image(image)
     except Exception as e:
-        return {"status": "error", "message": f"Failed to process the URL: {str(e)}"}
+        print(f"Error during URL processing: {e}")
+        return {"error": f"Failed to process the URL: {str(e)}"}
 
-# Combined prediction function for Gradio
-def combined_predict(image, url):
-    if image and url:
-        return {"status": "error", "message": "Provide either an image or a URL, not both."}
-    elif image:
-        return predict_from_image(image)
-    elif url:
-        return predict_from_url(url)
-    else:
-        return {"status": "error", "message": "No input provided. Please upload an image or provide a URL."}
+# Gradio interface with logging
+def predict(image, url):
+    try:
+        if image:
+            result = predict_from_image(image)
+        elif url:
+            result = predict_from_url(url)
+        else:
+            result = {"error": "No input provided. Please upload an image or provide a URL."}
+        
+        # Log the result
+        event_id = id(result)  # Generate a simple event ID for logging purposes
+        print(f"Event ID: {event_id}, Result: {result}")
+        return result
+
+    except Exception as e:
+        print(f"Error in prediction function: {e}")
+        return {"error": str(e)}
 
-# Gradio interface
+# Gradio interface setup
 iface = gr.Interface(
-    fn=combined_predict,
+    fn=predict,
     inputs=[
         gr.Image(type="pil", label="Upload an Image"),
         gr.Textbox(label="Or Enter an Image URL", placeholder="Provide a valid image URL"),
@@ -96,9 +109,6 @@ iface = gr.Interface(
     live=True,
     title="Maize Anomaly Detection",
     description="Upload an image or provide a URL to detect anomalies in maize crops.",
-    examples=[
-        [None, "https://example.com/sample-image.jpg"],  # Replace with a valid example URL
-    ]
 )
 
 # Launch the interface