Update app.py

app.py

@@ -48,13 +48,12 @@ transform = transforms.Compose([
 
 def predict(data):
     try:
-        # Expecting data to be a list
         if not isinstance(data, list) or len(data) == 0:
-            return json.dumps({"error": "Input data should be a non-empty list."})
+            return {"error": "Input data should be a non-empty list."}
         
         image_input = data[0].get('image', None)
         if not image_input:
-            return json.dumps({"error": "No image provided."})
+            return {"error": "No image provided."}
 
         print(f"Received image input: {image_input}")
 
@@ -70,7 +69,7 @@ def predict(data):
                     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}"})
+                    return {"error": f"Error fetching image from URL: {e}"}
             else:  # Assuming it is base64-encoded image data
                 try:
                     image_data = base64.b64decode(image_input)
@@ -78,7 +77,7 @@ def predict(data):
                     print(f"Decoded base64 image: {image}")
                 except Exception as e:
                     print(f"Error decoding base64 image: {e}")
-                    return json.dumps({"error": f"Error decoding base64 image: {e}"})
+                    return {"error": f"Error decoding base64 image: {e}"}
 
         # Apply transformations
         image = transform(image).unsqueeze(0)
@@ -92,20 +91,20 @@ def predict(data):
             print(f"Prediction output: {outputs}, Predicted class: {predicted_class}")
 
         if predicted_class == 0:
-            return json.dumps({"result": "The photo you've sent is of fall army worm with problem ID 126."})
+            return {"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."})
+            return {"result": "The photo you've sent is of a healthy maize image."}
         else:
-            return json.dumps({"error": "Unexpected class prediction."})
+            return {"error": "Unexpected class prediction."}
     except Exception as e:
         print(f"Error processing image: {e}")
-        return json.dumps({"error": f"Error processing image: {e}"})
+        return {"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"),
+    outputs=gr.JSON(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."