Upload gradio_app.py

interface/gradio_app.py

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+"""
+Gradio interface for FoodViT
+Provides a web interface for food classification
+"""
+
+import gradio as gr
+import sys
+import os
+from PIL import Image
+import numpy as np
+import random
+
+# Add parent directory to path for imports
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from config import GRADIO_CONFIG, CLASS_CONFIG
+from utils.predictor import predictor
+
+SAMPLES_DIR = "assets/samples"
+def get_random_examples(n=3):
+    files = [os.path.join(SAMPLES_DIR, f) for f in os.listdir(SAMPLES_DIR)
+             if f.lower().endswith((".png", ".jpg", ".jpeg", ".bmp", ".gif"))]
+    return [[f] for f in random.sample(files, min(n, len(files)))] if files else []
+
+def classify_food(image):
+    """
+    Classify food in the uploaded image
+    
+    Args:
+        image: PIL Image object from Gradio
+        
+    Returns:
+        tuple: (predicted_class, confidence, detailed_results)
+    """
+    if image is None:
+        return "No image uploaded", 0.0, "Please upload an image to classify."
+    
+    try:
+        # Make prediction
+        result = predictor.predict(image)
+        
+        if not result.get("success", False):
+            return "Error", 0.0, f"Prediction failed: {result.get('error', 'Unknown error')}"
+        
+        # Extract results
+        predicted_class = result["class"]
+        confidence = result["confidence"]
+        
+        # Create detailed results string
+        detailed_results = f"**Predicted Class:** {predicted_class.title()}\n\n"
+        detailed_results += f"**Confidence:** {confidence:.2%}\n\n"
+        detailed_results += "**All Class Probabilities:**\n"
+        
+        for class_name, prob in result["probabilities"].items():
+            detailed_results += f"- {class_name.title()}: {prob:.2%}\n"
+        
+        return predicted_class.title(), confidence, detailed_results
+        
+    except Exception as e:
+        return "Error", 0.0, f"An error occurred: {str(e)}"
+
+def create_interface():
+    """Create and configure the Gradio interface"""
+    
+    # Initialize predictor
+    if not predictor.initialize():
+        raise RuntimeError("Failed to initialize predictor")
+    
+    # Create interface
+    with gr.Blocks(
+        title=GRADIO_CONFIG["title"],
+        theme=gr.themes.Soft()
+    ) as interface:
+        
+        gr.Markdown(f"# {GRADIO_CONFIG['title']}")
+        gr.Markdown(GRADIO_CONFIG["description"])
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                # Input section
+                gr.Markdown("## Upload Image")
+                input_image = gr.Image(
+                    type="pil",
+                    label="Upload a food image",
+                    height=300
+                )
+                
+                classify_btn = gr.Button(
+                    "Classify Food",
+                    variant="primary",
+                    size="lg"
+                )
+                
+                # Example images
+                gr.Markdown("## Example Images")
+                gr.Examples(
+                    examples=get_random_examples(3),
+                    inputs=input_image,
+                    label="Try these examples"
+                )
+            
+            with gr.Column(scale=1):
+                # Output section
+                gr.Markdown("## Results")
+                
+                predicted_class = gr.Textbox(
+                    label="Predicted Food Class",
+                    interactive=False
+                )
+                
+                confidence_score = gr.Slider(
+                    minimum=0,
+                    maximum=1,
+                    value=0,
+                    label="Confidence Score",
+                    interactive=False
+                )
+                
+                detailed_results = gr.Markdown(
+                    label="Detailed Results",
+                    value="Upload an image and click 'Classify Food' to see results."
+                )
+        
+        # Model information
+        with gr.Accordion("Model Information", open=False):
+            model_info = predictor.get_model_info()
+            if "error" not in model_info:
+                info_text = f"""
+                **Device:** {model_info['device']}
+                **Total Parameters:** {model_info['total_parameters']:,}
+                **Number of Classes:** {model_info['num_classes']}
+                **Classes:** {', '.join(model_info['class_names'])}
+                """
+            else:
+                info_text = f"Error loading model info: {model_info['error']}"
+            
+            gr.Markdown(info_text)
+        
+        # Connect button to function
+        classify_btn.click(
+            fn=classify_food,
+            inputs=input_image,
+            outputs=[predicted_class, confidence_score, detailed_results]
+        )
+        
+        # Auto-classify when image is uploaded
+        input_image.change(
+            fn=classify_food,
+            inputs=input_image,
+            outputs=[predicted_class, confidence_score, detailed_results]
+        )
+    
+    return interface
+
+def launch_interface():
+    """Launch the Gradio interface"""
+    interface = create_interface()
+    # Launch with default configuration for Hugging Face Spaces
+    interface.launch(ssr_mode=False)
+
+if __name__ == "__main__":
+    launch_interface()