Create app.py

app.py

@@ -0,0 +1,701 @@
+import os
+import gradio as gr
+import base64
+import io
+import json
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+from typing import List, Dict, Any, Tuple
+import requests
+from sentence_transformers import SentenceTransformer
+import faiss
+from groq import Groq
+import tempfile
+import re
+
+class FoodRAGApplication:
+    def __init__(self):
+        """Initialize the Food Recognition RAG application"""
+        # Initialize Groq client for vision and text
+        self.groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+        
+        # Initialize embedding model for nutrition database
+        self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+        
+        # Initialize FAISS index for nutrition knowledge
+        self.dimension = 384
+        self.nutrition_index = faiss.IndexFlatIP(self.dimension)
+        
+        # Comprehensive nutrition database
+        self.nutrition_database = self._create_nutrition_database()
+        self.nutrition_embeddings = []
+        self.is_nutrition_indexed = False
+        
+        # Build nutrition index
+        self._build_nutrition_index()
+    
+    def _create_nutrition_database(self) -> List[Dict]:
+        """Create a comprehensive nutrition database for common foods"""
+        nutrition_db = [
+            # Fruits
+            {
+                "food_name": "Apple",
+                "category": "Fruit",
+                "calories_per_100g": 52,
+                "carbs": 14,
+                "fiber": 2.4,
+                "sugar": 10,
+                "protein": 0.3,
+                "fat": 0.2,
+                "vitamin_c": 4.6,
+                "potassium": 107,
+                "origin": "Central Asia",
+                "season": "Fall",
+                "health_benefits": "Rich in antioxidants, supports heart health, aids digestion",
+                "description": "Crisp, sweet fruit high in fiber and vitamin C"
+            },
+            {
+                "food_name": "Banana",
+                "category": "Fruit",
+                "calories_per_100g": 89,
+                "carbs": 23,
+                "fiber": 2.6,
+                "sugar": 12,
+                "protein": 1.1,
+                "fat": 0.3,
+                "vitamin_c": 8.7,
+                "potassium": 358,
+                "origin": "Southeast Asia",
+                "season": "Year-round",
+                "health_benefits": "High in potassium, supports muscle function, quick energy source",
+                "description": "Tropical fruit rich in potassium and natural sugars"
+            },
+            {
+                "food_name": "Orange",
+                "category": "Fruit",
+                "calories_per_100g": 47,
+                "carbs": 12,
+                "fiber": 2.4,
+                "sugar": 9,
+                "protein": 0.9,
+                "fat": 0.1,
+                "vitamin_c": 53.2,
+                "potassium": 181,
+                "origin": "China",
+                "season": "Winter",
+                "health_benefits": "Excellent source of vitamin C, boosts immunity, supports skin health",
+                "description": "Citrus fruit packed with vitamin C and folate"
+            },
+            {
+                "food_name": "Strawberry",
+                "category": "Fruit",
+                "calories_per_100g": 32,
+                "carbs": 8,
+                "fiber": 2,
+                "sugar": 4.9,
+                "protein": 0.7,
+                "fat": 0.3,
+                "vitamin_c": 58.8,
+                "potassium": 153,
+                "origin": "Europe and North America",
+                "season": "Spring-Summer",
+                "health_benefits": "High in antioxidants, supports brain health, anti-inflammatory",
+                "description": "Sweet berry rich in vitamin C and antioxidants"
+            },
+            {
+                "food_name": "Grapes",
+                "category": "Fruit",
+                "calories_per_100g": 62,
+                "carbs": 16,
+                "fiber": 0.9,
+                "sugar": 16,
+                "protein": 0.6,
+                "fat": 0.2,
+                "vitamin_c": 3.2,
+                "potassium": 191,
+                "origin": "Middle East",
+                "season": "Late summer-Fall",
+                "health_benefits": "Contains resveratrol, supports heart health, antioxidant properties",
+                "description": "Sweet fruit rich in natural sugars and antioxidants"
+            },
+            
+            # Vegetables
+            {
+                "food_name": "Carrot",
+                "category": "Vegetable",
+                "calories_per_100g": 41,
+                "carbs": 10,
+                "fiber": 2.8,
+                "sugar": 4.7,
+                "protein": 0.9,
+                "fat": 0.2,
+                "vitamin_c": 5.9,
+                "potassium": 320,
+                "vitamin_a": 835,
+                "origin": "Afghanistan",
+                "season": "Fall-Winter",
+                "health_benefits": "High in beta-carotene, supports eye health, immune function",
+                "description": "Root vegetable rich in beta-carotene and fiber"
+            },
+            {
+                "food_name": "Broccoli",
+                "category": "Vegetable",
+                "calories_per_100g": 34,
+                "carbs": 7,
+                "fiber": 2.6,
+                "sugar": 1.5,
+                "protein": 2.8,
+                "fat": 0.4,
+                "vitamin_c": 89.2,
+                "potassium": 316,
+                "origin": "Mediterranean",
+                "season": "Fall-Spring",
+                "health_benefits": "High in vitamin C and K, supports bone health, cancer-fighting compounds",
+                "description": "Cruciferous vegetable packed with vitamins and minerals"
+            },
+            {
+                "food_name": "Spinach",
+                "category": "Vegetable",
+                "calories_per_100g": 23,
+                "carbs": 3.6,
+                "fiber": 2.2,
+                "sugar": 0.4,
+                "protein": 2.9,
+                "fat": 0.4,
+                "vitamin_c": 28.1,
+                "potassium": 558,
+                "iron": 2.7,
+                "origin": "Persia",
+                "season": "Spring-Fall",
+                "health_benefits": "High in iron and folate, supports blood health, rich in antioxidants",
+                "description": "Leafy green vegetable high in iron and vitamins"
+            },
+            {
+                "food_name": "Tomato",
+                "category": "Vegetable",
+                "calories_per_100g": 18,
+                "carbs": 3.9,
+                "fiber": 1.2,
+                "sugar": 2.6,
+                "protein": 0.9,
+                "fat": 0.2,
+                "vitamin_c": 13.7,
+                "potassium": 237,
+                "origin": "South America",
+                "season": "Summer",
+                "health_benefits": "Rich in lycopene, supports heart health, anti-cancer properties",
+                "description": "Versatile fruit-vegetable rich in lycopene and vitamin C"
+            },
+            {
+                "food_name": "Bell Pepper",
+                "category": "Vegetable",
+                "calories_per_100g": 31,
+                "carbs": 7,
+                "fiber": 2.5,
+                "sugar": 4.2,
+                "protein": 1,
+                "fat": 0.3,
+                "vitamin_c": 127.7,
+                "potassium": 211,
+                "origin": "Central America",
+                "season": "Summer-Fall",
+                "health_benefits": "Extremely high in vitamin C, supports immune system, antioxidant rich",
+                "description": "Colorful vegetable with exceptional vitamin C content"
+            },
+            
+            # Nuts and Seeds
+            {
+                "food_name": "Almonds",
+                "category": "Nut",
+                "calories_per_100g": 579,
+                "carbs": 22,
+                "fiber": 12.5,
+                "sugar": 4.4,
+                "protein": 21,
+                "fat": 50,
+                "vitamin_c": 0,
+                "potassium": 733,
+                "origin": "Middle East",
+                "season": "Late summer",
+                "health_benefits": "High in healthy fats, supports heart health, good protein source",
+                "description": "Tree nut rich in healthy fats, protein, and vitamin E"
+            },
+            {
+                "food_name": "Avocado",
+                "category": "Fruit",
+                "calories_per_100g": 160,
+                "carbs": 9,
+                "fiber": 7,
+                "sugar": 0.7,
+                "protein": 2,
+                "fat": 15,
+                "vitamin_c": 10,
+                "potassium": 485,
+                "origin": "South Central Mexico",
+                "season": "Year-round",
+                "health_benefits": "Rich in monounsaturated fats, supports heart health, nutrient dense",
+                "description": "Creamy fruit high in healthy fats and fiber"
+            }
+        ]
+        return nutrition_db
+    
+    def _build_nutrition_index(self):
+        """Build FAISS index for nutrition database"""
+        try:
+            # Create text descriptions for embedding
+            nutrition_texts = []
+            for food in self.nutrition_database:
+                text = f"{food['food_name']} {food['category']} {food['description']} {food['health_benefits']} {food['origin']}"
+                nutrition_texts.append(text)
+            
+            # Create embeddings
+            embeddings = self.embedding_model.encode(nutrition_texts)
+            faiss.normalize_L2(embeddings)
+            
+            # Add to index
+            self.nutrition_index.add(embeddings)
+            self.nutrition_embeddings = embeddings
+            self.is_nutrition_indexed = True
+            
+        except Exception as e:
+            print(f"Error building nutrition index: {e}")
+    
+    def encode_image_to_base64(self, image: Image.Image) -> str:
+        """Convert PIL Image to base64 string"""
+        buffered = io.BytesIO()
+        image.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        return f"data:image/jpeg;base64,{img_str}"
+    
+    def identify_food_with_groq(self, image: Image.Image) -> str:
+        """Use Groq vision model to identify food in image"""
+        try:
+            # Convert image to base64
+            base64_image = self.encode_image_to_base64(image)
+            
+            # Call Groq vision API
+            completion = self.groq_client.chat.completions.create(
+                model="llama-3.2-11b-vision-preview",
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": "Identify the food item(s) in this image. Provide the name of the food, whether it's a fruit, vegetable, or other category. Be specific and concise. If there are multiple food items, list them all."
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": base64_image
+                                }
+                            }
+                        ]
+                    }
+                ],
+                temperature=0.1,
+                max_completion_tokens=512,
+                top_p=1,
+                stream=False,
+                stop=None,
+            )
+            
+            return completion.choices[0].message.content
+            
+        except Exception as e:
+            return f"Error identifying food: {str(e)}"
+    
+    def search_nutrition_info(self, food_identification: str, top_k: int = 3) -> List[Dict]:
+        """Search nutrition database for relevant food information"""
+        if not self.is_nutrition_indexed:
+            return []
+        
+        try:
+            # Create query embedding
+            query_embedding = self.embedding_model.encode([food_identification])
+            faiss.normalize_L2(query_embedding)
+            
+            # Search in nutrition index
+            scores, indices = self.nutrition_index.search(query_embedding, top_k)
+            
+            results = []
+            for score, idx in zip(scores[0], indices[0]):
+                if idx < len(self.nutrition_database):
+                    food_info = self.nutrition_database[idx].copy()
+                    food_info['similarity_score'] = float(score)
+                    results.append(food_info)
+            
+            return results
+            
+        except Exception as e:
+            print(f"Nutrition search error: {e}")
+            return []
+    
+    def generate_nutrition_response(self, food_identification: str, nutrition_matches: List[Dict]) -> str:
+        """Generate comprehensive nutrition response using Groq"""
+        try:
+            # Prepare nutrition context
+            context = ""
+            for i, food in enumerate(nutrition_matches):
+                context += f"""
+Food {i+1}: {food['food_name']} ({food['category']})
+- Calories per 100g: {food['calories_per_100g']}
+- Carbohydrates: {food['carbs']}g
+- Protein: {food['protein']}g
+- Fat: {food['fat']}g
+- Fiber: {food['fiber']}g
+- Vitamin C: {food['vitamin_c']}mg
+- Potassium: {food['potassium']}mg
+- Origin: {food['origin']}
+- Season: {food['season']}
+- Health Benefits: {food['health_benefits']}
+- Description: {food['description']}
+"""
+            
+            # Create comprehensive prompt
+            prompt = f"""Based on the food identification: "{food_identification}" and the following nutrition database information, provide a comprehensive answer about the nutritional content and other relevant information.
+
+Nutrition Database:
+{context}
+
+Please provide:
+1. Nutritional breakdown (calories, macronutrients, key vitamins/minerals)
+2. Health benefits
+3. Origin and seasonal information
+4. Any interesting facts about the food
+5. Serving size recommendations
+
+Make the response informative, engaging, and well-structured. If the identified food matches closely with the database, use that information. If not, provide general nutritional guidance based on the food type identified.
+"""
+
+            # Call Groq for response generation
+            completion = self.groq_client.chat.completions.create(
+                model="llama-3.3-70b-versatile",
+                messages=[
+                    {
+                        "role": "system",
+                        "content": "You are a nutrition expert providing detailed, accurate information about foods. Always cite specific nutritional values when available and give practical health advice."
+                    },
+                    {
+                        "role": "user",
+                        "content": prompt
+                    }
+                ],
+                temperature=0.3,
+                max_completion_tokens=1000,
+                top_p=1,
+                stream=False,
+            )
+            
+            return completion.choices[0].message.content
+            
+        except Exception as e:
+            return f"Error generating nutrition response: {str(e)}"
+    
+    def process_food_image(self, image: Image.Image) -> Tuple[str, str, str]:
+        """Main function to process food image and return nutrition information"""
+        if image is None:
+            return "Please upload an image of food.", "", ""
+        
+        try:
+            # Step 1: Identify food using vision model
+            food_identification = self.identify_food_with_groq(image)
+            
+            # Step 2: Search nutrition database
+            nutrition_matches = self.search_nutrition_info(food_identification)
+            
+            # Step 3: Generate comprehensive response
+            nutrition_response = self.generate_nutrition_response(food_identification, nutrition_matches)
+            
+            # Step 4: Create detailed breakdown
+            breakdown = "🔍 **Food Identification:**\n"
+            breakdown += f"{food_identification}\n\n"
+            
+            if nutrition_matches:
+                breakdown += "📊 **Matching Nutrition Data:**\n"
+                for i, food in enumerate(nutrition_matches[:2]):
+                    breakdown += f"**{food['food_name']}** ({food['category']})\n"
+                    breakdown += f"• Calories: {food['calories_per_100g']} per 100g\n"
+                    breakdown += f"• Similarity Score: {food['similarity_score']:.3f}\n\n"
+            
+            return nutrition_response, breakdown, food_identification
+            
+        except Exception as e:
+            return f"Error processing image: {str(e)}", "", ""
+
+# Initialize the application
+food_app = FoodRAGApplication()
+
+def create_food_pattern_background():
+    """Create an attractive food pattern background"""
+    # Create a large canvas
+    width, height = 1200, 800
+    background = Image.new('RGB', (width, height), '#f8f9fa')
+    draw = ImageDraw.Draw(background)
+    
+    # Food emojis and colors
+    food_items = ['🍎', '🍊', '🍌', '🥕', '🥬', '🍇', '🍓', '🥑', '🍅', '🥒']
+    colors = ['#ff6b6b', '#4ecdc4', '#45b7d1', '#96ceb4', '#feca57', '#ff9ff3', '#54a0ff', '#5f27cd']
+    
+    # Create pattern
+    for i in range(0, width, 100):
+        for j in range(0, height, 100):
+            # Add subtle circles
+            circle_color = colors[(i//100 + j//100) % len(colors)]
+            draw.ellipse([i+20, j+20, i+80, j+80], fill=circle_color + '20')
+    
+    return background
+
+# Custom CSS with food theme
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap');
+
+.gradio-container {
+    font-family: 'Poppins', sans-serif !important;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    min-height: 100vh;
+}
+
+.main-header {
+    text-align: center;
+    background: linear-gradient(135deg, #ff9a56, #ff6b95);
+    color: white;
+    padding: 3rem 2rem;
+    border-radius: 20px;
+    margin: 1rem;
+    box-shadow: 0 10px 30px rgba(0, 0, 0, 0.3);
+    position: relative;
+    overflow: hidden;
+}
+
+.main-header::before {
+    content: '🍎🥕🍊🥬🍇🍓';
+    position: absolute;
+    top: -10px;
+    right: -10px;
+    font-size: 2rem;
+    opacity: 0.2;
+    animation: float 3s ease-in-out infinite;
+}
+
+@keyframes float {
+    0%, 100% { transform: translateY(0px) rotate(0deg); }
+    50% { transform: translateY(-10px) rotate(5deg); }
+}
+
+.food-upload-area {
+    background: linear-gradient(145deg, #ffffff, #f0f0f0);
+    border: 3px dashed #ff9a56;
+    border-radius: 20px;
+    padding: 2rem;
+    text-align: center;
+    box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.1);
+    transition: all 0.3s ease;
+}
+
+.food-upload-area:hover {
+    border-color: #ff6b95;
+    transform: translateY(-2px);
+    box-shadow: 0 8px 25px rgba(255, 154, 86, 0.3);
+}
+
+.nutrition-panel {
+    background: linear-gradient(145deg, #ffffff, #f8f9ff);
+    border-radius: 15px;
+    padding: 1.5rem;
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
+    border-left: 4px solid #ff9a56;
+}
+
+.btn-analyze {
+    background: linear-gradient(135deg, #ff9a56, #ff6b95) !important;
+    border: none !important;
+    color: white !important;
+    font-weight: 600 !important;
+    padding: 12px 30px !important;
+    border-radius: 25px !important;
+    font-size: 16px !important;
+    transition: all 0.3s ease !important;
+    box-shadow: 0 4px 15px rgba(255, 154, 86, 0.4) !important;
+}
+
+.btn-analyze:hover {
+    transform: translateY(-2px) !important;
+    box-shadow: 0 6px 20px rgba(255, 154, 86, 0.6) !important;
+}
+
+.food-facts {
+    background: linear-gradient(145deg, #e8f5e8, #f0fff0);
+    border-radius: 15px;
+    padding: 1.5rem;
+    margin-top: 1rem;
+    border-left: 4px solid #4ecdc4;
+}
+
+.nutrition-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+    gap: 1rem;
+    margin-top: 1rem;
+}
+
+.nutrient-card {
+    background: white;
+    padding: 1rem;
+    border-radius: 10px;
+    text-align: center;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+    transition: transform 0.2s ease;
+}
+
+.nutrient-card:hover {
+    transform: translateY(-3px);
+}
+
+/* Custom scrollbar */
+::-webkit-scrollbar {
+    width: 8px;
+}
+
+::-webkit-scrollbar-track {
+    background: #f1f1f1;
+    border-radius: 10px;
+}
+
+::-webkit-scrollbar-thumb {
+    background: linear-gradient(135deg, #ff9a56, #ff6b95);
+    border-radius: 10px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: linear-gradient(135deg, #ff6b95, #ff9a56);
+}
+"""
+
+def create_interface():
+    """Create the Gradio interface"""
+    with gr.Blocks(css=custom_css, title="🍎 AI Food Nutritionist", theme=gr.themes.Soft()) as interface:
+        
+        # Header with food theme
+        gr.HTML("""
+        <div class="main-header">
+            <h1 style="font-size: 2.5rem; margin: 0; font-weight: 700;">🍎 AI Food Nutritionist</h1>
+            <p style="font-size: 1.2rem; margin: 10px 0 0 0; opacity: 0.9;">Upload food images and discover nutritional insights with AI-powered analysis!</p>
+        </div>
+        """)
+        
+        with gr.Row():
+            # Left panel - Image upload
+            with gr.Column(scale=1):
+                gr.HTML("<div style='text-align: center; padding: 1rem;'><h2 style='color: #333; margin-bottom: 1rem;'>📸 Upload Food Image</h2></div>")
+                
+                image_input = gr.Image(
+                    label="Food Image",
+                    type="pil",
+                    height=400,
+                    elem_classes=["food-upload-area"]
+                )
+                
+                analyze_btn = gr.Button(
+                    "🔍 Analyze Nutrition", 
+                    variant="primary",
+                    size="lg",
+                    elem_classes=["btn-analyze"]
+                )
+                
+                # Quick facts panel
+                gr.HTML("""
+                <div class="food-facts">
+                    <h3 style="color: #2c3e50; margin-top: 0;">💡 Did you know?</h3>
+                    <ul style="color: #555; line-height: 1.6;">
+                        <li>🥕 Carrots contain beta-carotene for eye health</li>
+                        <li>🍌 Bananas are rich in potassium for heart health</li>
+                        <li>🥬 Leafy greens provide folate and iron</li>
+                        <li>🍓 Berries are packed with antioxidants</li>
+                    </ul>
+                </div>
+                """)
+            
+            # Right panel - Results
+            with gr.Column(scale=2):
+                gr.HTML("<div style='text-align: center; padding: 1rem;'><h2 style='color: #333; margin-bottom: 1rem;'>📊 Nutrition Analysis</h2></div>")
+                
+                with gr.Tabs():
+                    with gr.TabItem("🍽️ Detailed Analysis"):
+                        nutrition_output = gr.Textbox(
+                            label="AI Nutrition Analysis",
+                            lines=15,
+                            interactive=False,
+                            placeholder="Upload a food image and click 'Analyze Nutrition' to get detailed nutritional information...",
+                            elem_classes=["nutrition-panel"]
+                        )
+                    
+                    with gr.TabItem("🔍 Food Identification"):
+                        identification_output = gr.Textbox(
+                            label="Food Identification Details",
+                            lines=10,
+                            interactive=False,
+                            placeholder="Food identification details will appear here...",
+                            elem_classes=["nutrition-panel"]
+                        )
+                    
+                    with gr.TabItem("📈 Quick Stats"):
+                        stats_output = gr.Textbox(
+                            label="Quick Nutritional Breakdown",
+                            lines=10,
+                            interactive=False,
+                            placeholder="Quick nutritional statistics will appear here...",
+                            elem_classes=["nutrition-panel"]
+                        )
+        
+        # Example images section
+        gr.HTML("""
+        <div style="margin-top: 2rem; text-align: center; padding: 2rem; background: rgba(255, 255, 255, 0.1); border-radius: 15px; backdrop-filter: blur(10px);">
+            <h3 style="color: white; margin-bottom: 1rem;">🌟 Try These Examples</h3>
+            <p style="color: white; opacity: 0.9;">Upload images of fruits, vegetables, nuts, or other foods to get instant nutritional analysis!</p>
+            <div style="margin-top: 1rem; font-size: 2rem;">
+                🍎 🥕 🍌 🥬 🍇 🍓 🥑 🍅 🥒 🌽
+            </div>
+        </div>
+        """)
+        
+        # Processing status
+        status_display = gr.HTML(visible=False)
+        
+        # Event handler
+        def analyze_food_wrapper(image):
+            if image is None:
+                return "Please upload an image first! 📸", "", ""
+            
+            # Show processing status
+            status_html = """
+            <div style="text-align: center; padding: 1rem; background: #e3f2fd; border-radius: 10px; margin: 1rem;">
+                <h3 style="color: #1976d2;">🔄 Processing your food image...</h3>
+                <p>AI is analyzing the nutritional content. Please wait...</p>
+            </div>
+            """
+            
+            try:
+                nutrition_info, breakdown, identification = food_app.process_food_image(image)
+                return nutrition_info, breakdown, identification
+            except Exception as e:
+                error_msg = f"❌ Error analyzing image: {str(e)}"
+                return error_msg, "", ""
+        
+        analyze_btn.click(
+            fn=analyze_food_wrapper,
+            inputs=[image_input],
+            outputs=[nutrition_output, stats_output, identification_output]
+        )
+    
+    return interface
+
+# Launch the application
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True
+    )