Update app.py

app.py

@@ -10,7 +10,7 @@ from io import BytesIO
 import os
 
 # Define the number of classes
-num_classes = 2  # Update with the actual number of classes in your dataset
+num_classes = 2
 
 # Download model from Hugging Face
 def download_model():
@@ -48,40 +48,44 @@ transform = transforms.Compose([
 def predict(image):
     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)
-                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."
+        
+        # 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 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 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):
+                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)
@@ -108,11 +112,11 @@ def predict(image):
 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
+    outputs=gr.Textbox(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."
 )
 
-# Launch the Gradio interface
-iface.launch(share=True, show_error=True)
+# Launch the Gradio interface 
+iface.launch(share=True, show_error=True)