Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import json
 import torch
 from torch import nn
 from torchvision import models, transforms
@@ -48,7 +49,7 @@ transform = transforms.Compose([
 def predict(image):
     try:
         print(f"Received image input: {image}")
-        
+
         # Check if the input is a PIL Image type
         if isinstance(image, Image.Image):
             print(f"Image is already loaded as PIL Image: {image}")
@@ -61,7 +62,7 @@ def predict(image):
                     print(f"Decoded base64 image: {image}")
                 except Exception as e:
                     print(f"Error decoding base64 image: {e}")
-                    return f"Error decoding base64 image: {e}"
+                    return json.dumps({"error": f"Error decoding base64 image: {e}"})
 
             # Try to fetch the image from a URL
             elif isinstance(image, str) and image.startswith("http"):
@@ -71,7 +72,7 @@ def predict(image):
                     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}"
+                    return json.dumps({"error": f"Error fetching image from URL: {e}"})
 
             # Try to load the image from a local file path
             elif isinstance(image, str) and os.path.isfile(image):
@@ -80,12 +81,12 @@ def predict(image):
                     print(f"Loaded image from local path: {image}")
                 except Exception as e:
                     print(f"Error loading image from local path: {e}")
-                    return f"Error loading image from local path: {e}"
+                    return json.dumps({"error": f"Error loading image from local path: {e}"})
 
             # Validate that the image is correctly loaded
             if not isinstance(image, Image.Image):
                 print("Invalid image format received.")
-                return "Invalid image format received."
+                return json.dumps({"error": "Invalid image format received."})
 
         # Apply transformations
         image = transform(image).unsqueeze(0)
@@ -99,19 +100,19 @@ def predict(image):
             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."
+            return json.dumps({"result": "The photo you've sent is of fall army worm with problem ID 126."})
         elif predicted_class == 1:
-            return "The photo you've sent is of a healthy maize image."
+            return json.dumps({"result": "The photo you've sent is of a healthy maize image."})
         else:
-            return "Unexpected class prediction."
+            return json.dumps({"error": "Unexpected class prediction."})
     except Exception as e:
         print(f"Error processing image: {e}")
-        return 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.Image(type="pil", label="Upload an image or provide a URL or local path"),  # Input: Image, URL, or Local Path
+    inputs=gr.Image(type="pil", label="Upload an image or provide a URL or local path"),
     outputs=gr.Textbox(label="Prediction Result"),
     live=True,
     title="Maize Anomaly Detection",