Update app.py

app.py

@@ -54,37 +54,25 @@ def predict(image):
         if isinstance(image, Image.Image):
             print(f"Image is already loaded as PIL Image: {image}")
         else:
-            # Try to handle base64-encoded image
+            # Check if the input contains a base64-encoded string or URL
             if isinstance(image, dict) and image.get("data"):
-                try:
-                    image_data = base64.b64decode(image["data"])
+                image_data = image["data"]
+                if image_data.startswith("http"):  # URL case
+                    response = requests.get(image_data)
+                    image = Image.open(BytesIO(response.content))
+                    print(f"Fetched image from URL: {image}")
+                else:  # Base64-encoded image case
+                    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}"})
-
-            # Try to fetch the image from a URL
             elif isinstance(image, str) and image.startswith("http"):
-                try:
-                    response = requests.get(image)
-                    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 json.dumps({"error": f"Error fetching image from URL: {e}"})
-
-            # Try to load the image from a local file path
+                response = requests.get(image)
+                image = Image.open(BytesIO(response.content))
+                print(f"Fetched image from URL: {image}")
             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):
+                image = Image.open(image)
+                print(f"Loaded image from local path: {image}")
+            else:
                 print("Invalid image format received.")
                 return json.dumps({"error": "Invalid image format received."})
 
@@ -112,7 +100,7 @@ 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",
@@ -121,3 +109,4 @@ iface = gr.Interface(
 
 # Launch the Gradio interface 
 iface.launch(share=True, show_error=True)
+