Update app.py

app.py

@@ -6,7 +6,6 @@ from torchvision import models, transforms
 from huggingface_hub import hf_hub_download
 from PIL import Image
 import requests
-import os
 from io import BytesIO
 
 # Define the number of classes
@@ -39,39 +38,56 @@ transform = transforms.Compose([
 
 # Function to predict from image content
 def predict_from_image(image):
-    # Ensure the image is a PIL Image
-    if not isinstance(image, Image.Image):
-        raise ValueError("Invalid image format received. Please provide a valid image.")
+    try:
+        # Ensure the image is a PIL Image
+        if not isinstance(image, Image.Image):
+            raise ValueError("Invalid image format received. Please provide a valid image.")
 
-    # Apply transformations
-    image_tensor = transform(image).unsqueeze(0)
+        # Apply transformations
+        image_tensor = transform(image).unsqueeze(0)
 
-    # Predict
-    with torch.no_grad():
-        outputs = model(image_tensor)
-        predicted_class = torch.argmax(outputs, dim=1).item()
+        # Predict
+        with torch.no_grad():
+            outputs = model(image_tensor)
+            predicted_class = torch.argmax(outputs, dim=1).item()
 
-    # Interpret the result
-    if predicted_class == 0:
-        return {"result": "The photo is of fall army worm with problem ID 126."}
-    elif predicted_class == 1:
-        return {"result": "The photo is of a healthy maize image."}
-    else:
-        return {"error": "Unexpected class prediction."}
+        # Interpret the result
+        if predicted_class == 0:
+            return {"status": "success", "result": "Fall army worm detected (Problem ID: 126)."}
+        elif predicted_class == 1:
+            return {"status": "success", "result": "Healthy maize image detected."}
+        else:
+            return {"status": "error", "message": "Unexpected class prediction."}
+    except Exception as e:
+        return {"status": "error", "message": f"Error during prediction: {str(e)}"}
 
 # Function to predict from URL
 def predict_from_url(url):
     try:
+        if not url.startswith(("http://", "https://")):
+            raise ValueError("Invalid URL format. Please provide a valid image URL.")
+        
         response = requests.get(url)
         response.raise_for_status()  # Ensure the request was successful
         image = Image.open(BytesIO(response.content))
         return predict_from_image(image)
     except Exception as e:
-        return {"error": f"Failed to process the URL: {str(e)}"}
+        return {"status": "error", "message": f"Failed to process the URL: {str(e)}"}
+
+# Combined prediction function for Gradio
+def combined_predict(image, url):
+    if image and url:
+        return {"status": "error", "message": "Provide either an image or a URL, not both."}
+    elif image:
+        return predict_from_image(image)
+    elif url:
+        return predict_from_url(url)
+    else:
+        return {"status": "error", "message": "No input provided. Please upload an image or provide a URL."}
 
 # Gradio interface
 iface = gr.Interface(
-    fn=lambda image, url: predict_from_image(image) if image else predict_from_url(url),
+    fn=combined_predict,
     inputs=[
         gr.Image(type="pil", label="Upload an Image"),
         gr.Textbox(label="Or Enter an Image URL", placeholder="Provide a valid image URL"),
@@ -80,6 +96,9 @@ iface = gr.Interface(
     live=True,
     title="Maize Anomaly Detection",
     description="Upload an image or provide a URL to detect anomalies in maize crops.",
+    examples=[
+        [None, "https://example.com/sample-image.jpg"],  # Replace with a valid example URL
+    ]
 )
 
 # Launch the interface