Update app.py

app.py

@@ -46,27 +46,21 @@ transform = transforms.Compose([
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
 ])
 
-def predict(image):
+def predict(data):
     try:
-        print(f"Received image input: {image}")
+        # 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."})
+        
+        image_input = data[0].get('path', None)
+        if not image_input:
+            return json.dumps({"error": "No image provided."})
 
-        # Check if the input is a PIL Image type
-        if isinstance(image, Image.Image):
-            print(f"Image is already loaded as PIL Image: {image}")
-        else:
-            # Try to handle base64-encoded image
-            if isinstance(image, dict) and image.get("data"):
-                try:
-                    image_data = base64.b64decode(image["data"])
-                    image = Image.open(BytesIO(image_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}"})
+        print(f"Received image input: {image_input}")
 
         # Check if the input is a URL
         if isinstance(image_input, str):
-           if image_input.startswith("http://") or image_input.startswith("https://"):
+            if image_input.startswith("http://") or image_input.startswith("https://"):
                 try:
                     response = requests.get(image_input)
                     response.raise_for_status()  # Check for HTTP errors
@@ -75,22 +69,8 @@ def predict(image):
                 except Exception as e:
                     print(f"Error fetching image from URL: {e}")
                     return json.dumps({"error": f"Error fetching image from URL: {e}"})
-        else:
-            return json.dumps({"error": "Invalid URL format. Please provide a valid URL starting with 'http://' or 'https://'."})
-
-            # Try to load the image from a local file path
-            elif isinstance(image, str) and os.path.isfile(image):
-                try:
-                    image = Image.open(image)
-                    print(f"Loaded image from local path: {image}")
-                except Exception as e:
-                    print(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 json.dumps({"error": "Invalid image format received."})
+            else:
+                return json.dumps({"error": "Invalid URL format. Please provide a valid URL starting with 'http://' or 'https://'."})
 
         # Apply transformations
         image = transform(image).unsqueeze(0)
@@ -116,7 +96,7 @@ def predict(image):
 # 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"),
+    inputs=gr.JSON(label="Input JSON"),
     outputs=gr.Textbox(label="Prediction Result"),
     live=True,
     title="Maize Anomaly Detection",