Update app.py

app.py

@@ -1,16 +1,30 @@
 import gradio as gr
+from PIL import Image
+import requests
 
 # Load the pre-trained model from Hugging Face
 model = gr.load("models/dima806/indian_food_image_detection")
 
+def classify_image(image):
+    # Convert PIL image to the format expected by the model
+    image = Image.fromarray(image)
+    result = model(image)
+    
+    # Parse the result to the format expected by Gradio's label component
+    predictions = result[0]["labels"]  # Adjust this line based on the actual output structure
+    formatted_result = {pred["label"]: pred["score"] for pred in predictions}
+    
+    return formatted_result
+
 # Create a Gradio interface with the custom title
 iface = gr.Interface(
-    fn=model, 
-    inputs=gr.Image(type="filepath"), 
-    outputs="label", 
+    fn=classify_image, 
+    inputs=gr.Image(type="numpy"), 
+    outputs=gr.Label(), 
     title="CAPSTONE",
-    description="Upload an image of Indian food to detect what it is."
+    description="Upload an image of Indian food to detect what it is.",
+    live=True  # Enable live processing
 )
 
 # Launch the Gradio interface
-iface.launch()
+iface.launch(share=True)