Update app.py

app.py

@@ -39,44 +39,23 @@ transform = transforms.Compose([
 ])
 
 # Prediction function for an uploaded image
-def predict_from_image(image):
+def predict_from_image(image_url):
     try:
-        # Ensure the input is a valid PIL image
-        if not isinstance(image, Image.Image):
-            raise ValueError("Invalid image format received. Please provide a valid image.")
-
-        # Log the input for debugging
-        logging.info("Received image for prediction")
-
-        # Apply transformations
-        image_tensor = transform(image).unsqueeze(0)
-
-        # Predict
-        with torch.no_grad():
-            outputs = model(image_tensor)
-            predicted_class = torch.argmax(outputs, dim=1).item()
-
-        # Interpret the result
-        if predicted_class == 0:
-            return {"result": "The photo is of fall army worm with problem ID 126."}
-        elif predicted_class == 1:
-            return {"result": "The photo is of a healthy maize image."}
-        else:
-            return {"error": "Unexpected class prediction."}
+        # Download the image from the provided URL
+        response = requests.get(image_url)
+        image = Image.open(BytesIO(response.content))
+        # Process the image...
+        return {"result": "Image processed successfully"}
     except Exception as e:
-        logging.error(f"Error during prediction: {str(e)}")
-        return {"error": f"Failed to process the image: {str(e)}"}
-
-# Gradio interface restricted to image input
-iface = gr.Interface(
-    fn=predict_from_image,  # Only handle image input
-    inputs=gr.Image(type="pil", label="Upload an Image"),  # Restrict input to image upload
-    outputs=gr.JSON(label="Prediction Result"),
-    live=True,
-    title="Maize Anomaly Detection",
-    description="Upload an image to detect anomalies in maize crops.",
+        return {"error": str(e)}
+
+demo = gr.Interface(
+    fn=predict_from_image,
+    inputs="text",
+    outputs="json",
+    title="Image Processing",
+    description="Enter a URL to an image",
 )
 
-# Launch the interface locally
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()