Update app.py

app.py

@@ -45,70 +45,53 @@ transform = transforms.Compose([
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
 ])
 
-def predict(image):
+def process_image(image_input):
     try:
-        print(f"Received image input: {image}")
-
-        # Check if the input contains a base64-encoded string
-        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 f"Error decoding base64 image: {e}"
-
-        # Check if the input is a URL
-        elif isinstance(image, str) and image.startswith("http"):
-            try:
-                response = requests.get(image)
+        # Process the image input (URL, local file, or base64)
+        if isinstance(image_input, dict):
+            # Check if the input contains a URL
+            if image_input.get("url"):
+                image_url = image_input["url"]
+                response = requests.get(image_url)
                 image = Image.open(BytesIO(response.content))
-                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}"
-
-        # Check if the input is 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 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."
-
-        # Apply transformations
-        image = transform(image).unsqueeze(0)
-        print(f"Transformed image tensor: {image.shape}")
-
-        image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
-
-        with torch.no_grad():
-            outputs = model(image)
-            predicted_class = torch.argmax(outputs, dim=1).item()
-            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."
-        elif predicted_class == 1:
-            return "The photo you've sent is of a healthy maize image."
+            # Check if the input contains a file path
+            elif image_input.get("path"):
+                image_path = image_input["path"]
+                image = Image.open(image_path)
+            # Handle base64 if it's included
+            elif image_input.get("data"):
+                image_data = base64.b64decode(image_input["data"])
+                image = Image.open(BytesIO(image_data))
+            else:
+                return "Invalid input data format. Please provide a URL or path."
+
+            # Apply transformations
+            image = transform(image).unsqueeze(0)
+            image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
+
+            # Make the prediction
+            with torch.no_grad():
+                outputs = model(image)
+                predicted_class = torch.argmax(outputs, dim=1).item()
+
+            # Return prediction result
+            if predicted_class == 0:
+                return "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."
+            else:
+                return "Unexpected class prediction."
         else:
-            return "Unexpected class prediction."
+            return "Invalid input. Please provide a dictionary with 'url' or 'path'."
     except Exception as e:
         print(f"Error processing image: {e}")
         return 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
-    outputs=gr.Textbox(label="Prediction Result"),  # Output: Predicted class
+    fn=process_image,
+    inputs=gr.JSON(label="Upload an image (URL or Local Path)"),  # Input: JSON to handle URL or path
+    outputs=gr.Textbox(label="Prediction Result"),  # Output: 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."