Update app.py

app.py

@@ -6,8 +6,6 @@ from torchvision import models, transforms
 from huggingface_hub import hf_hub_download
 from PIL import Image
 import requests
-import os
-import base64
 from io import BytesIO
 
 # Define the number of classes
@@ -46,17 +44,14 @@ transform = transforms.Compose([
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
 ])
 
-def predict(image):
+def predict_from_image(image):
     try:
-        print(f"Received image input: {image}")
-
         # Check if the input is a PIL Image type (Gradio automatically provides a PIL image)
         if not isinstance(image, Image.Image):
             return json.dumps({"error": "Invalid image format received. Please provide a valid image."})
 
         # Apply transformations to the image
         image = transform(image).unsqueeze(0)
-        print(f"Transformed image tensor: {image.shape}")
 
         # Move the image to the correct device
         image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
@@ -65,7 +60,6 @@ def predict(image):
         with torch.no_grad():
             outputs = model(image)
             predicted_class = torch.argmax(outputs, dim=1).item()
-            print(f"Prediction output: {outputs}, Predicted class: {predicted_class}")
 
         # Return the result based on the predicted class
         if predicted_class == 0:
@@ -76,18 +70,43 @@ def predict(image):
             return json.dumps({"error": "Unexpected class prediction."})
 
     except Exception as e:
-        print(f"Error processing image: {e}")
         return json.dumps({"error": f"Error processing image: {e}"})
 
+
+def predict_from_url(url):
+    try:
+        # Check if the URL is valid and try fetching the image
+        response = requests.get(url)
+        if response.status_code == 200:
+            img = Image.open(BytesIO(response.content))
+            # Call the predict function for the image
+            return predict_from_image(img)
+        else:
+            return json.dumps({"error": "Unable to fetch image from the URL."})
+    except Exception as e:
+        return json.dumps({"error": f"Error fetching image from URL: {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"),
+    fn=predict_from_image,
+    inputs=gr.Image(type="pil", label="Upload an image or provide a local path"),
     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 
+# Add another function for URL input
+url_iface = gr.Interface(
+    fn=predict_from_url,
+    inputs=gr.Textbox(label="Enter image URL"),
+    outputs=gr.Textbox(label="Prediction Result from URL"),
+    live=True,
+    title="Maize Anomaly Detection from URL",
+    description="Provide an image URL to detect anomalies like disease or pest infestation."
+)
+
+# Launch the Gradio interface
 iface.launch(share=True, show_error=True)
+url_iface.launch(share=True, show_error=True)