app.py

import os
import logging
from typing import Dict, List, Optional
from PIL import Image
from transformers import ViTFeatureExtractor, ViTForImageClassification
import requests
import gradio as gr

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class NutritionAnalyzer:
    """Main class handling food recognition and nutrition analysis"""
    
    def __init__(self):
        self.model_name = "google/vit-base-patch16-224"
        self.feature_extractor = None
        self.model = None
        self.api_key = os.getenv('NUTRITION_API_KEY', 'Your API Key')
        
    def initialize_models(self):
        """Initialize vision transformer model and feature extractor"""
        try:
            self.feature_extractor = ViTFeatureExtractor.from_pretrained(self.model_name)
            self.model = ViTForImageClassification.from_pretrained(self.model_name)
            logger.info("Models initialized successfully")
        except Exception as e:
            logger.error(f"Model initialization failed: {str(e)}")
            raise

    def identify_food(self, image_path: str) -> str:
        """Identify food item from image using Vision Transformer
        
        Args:
            image_path: Path to the input image file
            
        Returns:
            str: Detected food item name
        """
        try:
            image = Image.open(image_path)
            inputs = self.feature_extractor(images=image, return_tensors="pt")
            outputs = self.model(**inputs)
            return self.model.config.id2label[outputs.logits.argmax(-1).item()].split(',')[0]
        except Exception as e:
            logger.error(f"Food identification error: {str(e)}")
            raise

class NutritionAPIHandler:
    """Handles nutrition data retrieval from API"""
    
    BASE_URL = "https://api.api-ninjas.com/v1/nutrition"
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.session = requests.Session()
        self.session.headers.update({'X-Api-Key': self.api_key})

    def get_nutrition_data(self, food_name: str) -> Optional[Dict]:
        """Fetch nutrition data from API
        
        Args:
            food_name: Name of food item to query
            
        Returns:
            Optional[Dict]: Nutrition data or None if error occurs
        """
        try:
            response = self.session.get(self.BASE_URL, params={'query': food_name})
            response.raise_for_status()
            return response.json()[0] if response.json() else None
        except Exception as e:
            logger.error(f"API Error: {str(e)}")
            return None

class NutritionFormatter:
    """Formats nutrition data into visual representations"""
    
    @staticmethod
    def create_nutrition_table(data: Dict) -> str:
        """Generate HTML table with nutrition facts
        
        Args:
            data: Dictionary containing nutrition data
            
        Returns:
            str: Formatted HTML table
        """
        if not data:
            return "<p>No nutrition data available</p>"
            
        return f"""
        <div class="nutrition-container">
            <h3>Nutrition Facts for {data.get('name', 'Unknown Food')}</h3>
            <div class="macros">
                <div class="macro calories">
                    <h4>Calories</h4>
                    <div class="value">{data.get('calories', 0)}</div>
                </div>
                <div class="macro protein">
                    <h4>Protein (g)</h4>
                    <div class="value">{data.get('protein_g', 0)}</div>
                </div>
                <div class="macro carbs">
                    <h4>Carbs (g)</h4>
                    <div class="value">{data.get('carbohydrates_total_g', 0)}</div>
                </div>
                <div class="macro fat">
                    <h4>Fat (g)</h4>
                    <div class="value">{data.get('fat_total_g', 0)}</div>
                </div>
            </div>
            <table>
                <tr><th>Nutrient</th><th>Amount</th><th>Daily Value%</th></tr>
                {NutritionFormatter._create_table_rows(data)}
            </table>
        </div>
        """
    
    @staticmethod
    def _create_table_rows(data: Dict) -> str:
        nutrients = [
            ('Saturated Fat (g)', 'fat_saturated_g', 20),
            ('Fiber (g)', 'fiber_g', 25),
            ('Sugar (g)', 'sugar_g', 50),
            ('Sodium (mg)', 'sodium_mg', 2300),
            ('Potassium (mg)', 'potassium_mg', 4700),
            ('Cholesterol (mg)', 'cholesterol_mg', 300)
        ]
        rows = []
        for name, key, dv in nutrients:
            value = data.get(key, 0)
            dv_percent = (value / dv) * 100 if dv else 0
            rows.append(
                f"<tr><td>{name}</td><td>{value}</td>"
                f"<td>{dv_percent:.1f}%</td></tr>"
            )
        return ''.join(rows)

class NutritionAnalyzerApp:
    """Gradio application for nutrition analysis"""
    
    def __init__(self):
        self.analyzer = NutritionAnalyzer()
        self.analyzer.initialize_models()
        self.api_handler = NutritionAPIHandler(self.analyzer.api_key)
        self.example_images = self._load_example_images()
        
    def _load_example_images(self):
        example_paths = [
            "examples/apple.jpg",
            "examples/pizza.jpg",
            "examples/salad.jpg"
        ]
        valid_examples = []
        for path in example_paths:
            if os.path.exists(path):
                valid_examples.append([path])
            else:
                logger.warning(f"Example image not found: {path}")
        return valid_examples if valid_examples else None    
    def analyze_image(self, image_path: str) -> str:
        """Full processing pipeline for image analysis
        
        Args:
            image_path: Path to input image file
            
        Returns:
            str: Formatted nutrition information
        """
        try:
            food_item = self.analyzer.identify_food(image_path)
            nutrition_data = self.api_handler.get_nutrition_data(food_item)
            return self._handle_output(food_item, nutrition_data)
        except Exception as e:
            logger.error(f"Processing error: {str(e)}")
            return "<p class='error'>Error processing request. Please try again.</p>"

    def _handle_output(self, food_item: str, data: Optional[Dict]) -> str:
        if not data:
            return f"<p>No nutrition data found for {food_item}</p>"
        return NutritionFormatter.create_nutrition_table(data)

# Initialize and run Gradio app
if __name__ == "__main__":
    app = NutritionAnalyzerApp()
    
    css = """
    .nutrition-container { max-width: 600px; margin: 20px auto; }
    .macros { display: grid; grid-template-columns: repeat(4, 1fr); gap: 10px; margin: 20px 0; }
    .macro { padding: 15px; border-radius: 8px; text-align: center; }
    .calories { background: #ffd70033; }
    .protein { background: #00ff0033; }
    .carbs { background: #0000ff33; }
    .fat { background: #ff000033; }
    .value { font-size: 1.5em; font-weight: bold; }
    table { width: 100%; margin-top: 20px; }
    th, td { padding: 10px; text-align: left; }
    .error { color: red; font-weight: bold; }
    """
    
    # Update the Gradio interface section in the __main__ block:
with gr.Blocks(css=css) as demo:
    gr.Markdown("# 🍎 AI Nutrition Analyzer")
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="filepath", label="Upload Food Image")
            submit_btn = gr.Button("Analyze Nutrition")
        with gr.Column():
            output = gr.HTML(label="Nutrition Analysis")
    
    
    submit_btn.click(
        fn=app.analyze_image,
        inputs=image_input,
        outputs=output
    )

    demo.launch()