Delete interface/gradio_app.py

interface/gradio_app.py

@@ -1,168 +0,0 @@
-"""
-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 configuration
-    interface.launch(
-        server_name="127.0.0.1",
-        server_port=7860,
-        share=False,
-        show_error=True
-    )
-
-if __name__ == "__main__":
-    launch_interface()