Clean deployment with model download functionality

app.py

@@ -0,0 +1,574 @@
+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import models
+import torch.nn as nn
+import torchvision.transforms as transforms
+import io
+import os
+import openai
+import json
+import timm  # For ConvNeXt Large model
+from huggingface_hub import hf_hub_download
+
+# --- Page Configuration ---
+st.set_page_config(page_title="EatSmart Pro", page_icon="🍽️", layout="wide")
+
+# Mobile menu indicator
+st.markdown("""
+<div style="display: block; background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); 
+           padding: 8px 15px; border-radius: 8px; margin-bottom: 15px; text-align: center;">
+    <p style="color: white; margin: 0; font-size: 14px;">
+        📱 <strong>Mobile Users:</strong> Click the <strong>></strong> arrow (top-left) to open preferences menu
+    </p>
+</div>
+""", unsafe_allow_html=True)
+
+# ==============================================================================
+# The correct, full list of 101 class names from your training script.
+# ==============================================================================
+CLASS_NAMES = [
+    'apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare',
+    'beet_salad', 'beignets', 'bibimbap', 'bread_pudding', 'breakfast_burrito',
+    'bruschetta', 'caesar_salad', 'cannoli', 'caprese_salad', 'carrot_cake',
+    'ceviche', 'cheesecake', 'cheese_plate', 'chicken_curry', 'chicken_quesadilla',
+    'chicken_wings', 'chocolate_cake', 'chocolate_mousse', 'churros', 'clam_chowder',
+    'club_sandwich', 'crab_cakes', 'creme_brulee', 'croque_madame', 'cup_cakes',
+    'deviled_eggs', 'donuts', 'dumplings', 'edamame', 'eggs_benedict',
+    'escargots', 'falafel', 'filet_mignon', 'fish_and_chips', 'foie_gras',
+    'french_fries', 'french_onion_soup', 'french_toast', 'fried_calamari', 'fried_rice',
+    'frozen_yogurt', 'garlic_bread', 'gnocchi', 'greek_salad', 'grilled_cheese_sandwich',
+    'grilled_salmon', 'guacamole', 'gyoza', 'hamburger', 'hot_and_sour_soup',
+    'hot_dog', 'huevos_rancheros', 'hummus', 'ice_cream', 'lasagna',
+    'lobster_bisque', 'lobster_roll_sandwich', 'macaroni_and_cheese', 'macarons', 'miso_soup',
+    'mussels', 'nachos', 'omelette', 'onion_rings', 'oysters',
+    'pad_thai', 'paella', 'pancakes', 'panna_cotta', 'peking_duck',
+    'pho', 'pizza', 'pork_chop', 'poutine', 'prime_rib',
+    'pulled_pork_sandwich', 'ramen', 'ravioli', 'red_velvet_cake', 'risotto',
+    'samosa', 'sashimi', 'scallops', 'seaweed_salad', 'shrimp_and_grits',
+    'spaghetti_bolognese', 'spaghetti_carbonara', 'spring_rolls', 'steak', 'strawberry_shortcake',
+    'sushi', 'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare',
+    'waffles'
+]
+
+def download_model_if_needed():
+    """Download ConvNeXt Large model from model repository if not present"""
+    model_path = "food_classifier_convnext_large_cpu_full.pth"
+    
+    if not os.path.exists(model_path):
+        st.info("🔄 Downloading ConvNeXt Large model (2.3GB)... This may take a few minutes on first load.")
+        progress_bar = st.progress(0)
+        status_text = st.empty()
+        
+        try:
+            status_text.text("📦 Downloading from Lumilife/eatSmartPro-models...")
+            progress_bar.progress(25)
+            
+            # Download from your model repository
+            downloaded_path = hf_hub_download(
+                repo_id="Lumilife/eatSmartPro-models",
+                filename="food_classifier_convnext_large_cpu_full.pth",
+                local_dir=".",
+                local_dir_use_symlinks=False
+            )
+            
+            progress_bar.progress(100)
+            status_text.text("✅ Model downloaded successfully!")
+            
+            # Clear progress indicators after 2 seconds
+            import time
+            time.sleep(2)
+            progress_bar.empty()
+            status_text.empty()
+            
+            return downloaded_path
+            
+        except Exception as e:
+            st.error(f"❌ Failed to download model: {str(e)}")
+            return None
+    
+    return model_path
+
+def get_convnext_model(num_classes):
+    """
+    Creates the ConvNeXt Large model architecture,
+    matching the training script for maximum accuracy.
+    """
+    print(f"🚀 Loading ConvNeXt Large model for inference...")
+    print(f"📊 Model: ConvNeXt Large (197M parameters)")
+    
+    # Create ConvNeXt Large model (same as training script)
+    model = timm.create_model('convnext_large.fb_in22k_ft_in1k', pretrained=True, num_classes=num_classes)
+    
+    # Model statistics for user feedback
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"✅ Model architecture loaded:")
+    print(f"   Total parameters: {total_params:,}")
+    print(f"   Model size: ~{total_params * 4 / 1024**2:.1f} MB")
+    
+    return model
+
+def load_json_data(path):
+    if not os.path.exists(path): return {}
+    try:
+        with open(path, 'r') as f: return json.load(f)
+    except (FileNotFoundError, json.JSONDecodeError): return {}
+
+def get_health_info(food_name, health_data):
+    """Enhanced health information display with comprehensive nutritional data"""
+    food_name_key = food_name.replace('_', ' ').title()
+    
+    # Try to get from the nested nutrition_info structure first
+    nutrition_info = health_data.get("nutrition_info", {})
+    info = nutrition_info.get(food_name_key) or nutrition_info.get(food_name.lower())
+    
+    # Try direct lookup if nested lookup fails
+    if not info:
+        info = health_data.get(food_name_key)
+    
+    if not info:
+        return "<p>No specific health information available for this dish.</p>"
+    
+    # Get health score
+    health_scores = health_data.get("health_scores", {})
+    health_score = health_scores.get(food_name.lower(), {})
+    score = health_score.get("score", 75)
+    score_explanation = health_score.get("explanation", "Good")
+    
+    # Determine score color
+    if score >= 80:
+        score_color = "#28a745"  # Green
+        score_bg = "#d4edda"
+    elif score >= 60:
+        score_color = "#ffc107"  # Yellow
+        score_bg = "#fff3cd"
+    else:
+        score_color = "#dc3545"  # Red
+        score_bg = "#f8d7da"
+    
+    # Build nutritional information
+    nutrition_html = f"""
+    <div style="background-color: #f8f9fa; padding: 15px; border-radius: 8px; margin: 10px 0;">
+        <h4 style="color: #495057; margin-top: 0;">📊 Nutritional Information</h4>
+        <div style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 15px; text-align: center;">
+            <div>
+                <div style="font-size: 14px; color: #6c757d;">Calories</div>
+                <div style="font-size: 24px; font-weight: bold; color: #495057;">{info.get("calories", "N/A")}</div>
+                <div style="font-size: 12px; color: #6c757d;">kcal</div>
+            </div>
+            <div>
+                <div style="font-size: 14px; color: #6c757d;">Protein</div>
+                <div style="font-size: 24px; font-weight: bold; color: #495057;">{info.get("protein", "N/A")}</div>
+                <div style="font-size: 12px; color: #6c757d;">g</div>
+            </div>
+            <div>
+                <div style="font-size: 14px; color: #6c757d;">Carbs</div>
+                <div style="font-size: 24px; font-weight: bold; color: #495057;">{info.get("carbs", "N/A")}</div>
+                <div style="font-size: 12px; color: #6c757d;">g</div>
+            </div>
+            <div>
+                <div style="font-size: 14px; color: #6c757d;">Fat</div>
+                <div style="font-size: 24px; font-weight: bold; color: #495057;">{info.get("fat", "N/A")}</div>
+                <div style="font-size: 12px; color: #6c757d;">g</div>
+            </div>
+        </div>
+    </div>
+    """
+    
+    # Health Score
+    score_html = f"""
+    <div style="background-color: {score_bg}; border: 1px solid {score_color}; border-radius: 8px; padding: 15px; margin: 10px 0;">
+        <h4 style="color: {score_color}; margin-top: 0;">🏥 Health Assessment</h4>
+        <div style="background-color: {score_color}; color: white; padding: 10px; border-radius: 5px; text-align: center; font-weight: bold;">
+            Health Score: {score}/100 ({score_explanation})
+        </div>
+    </div>
+    """
+    
+    # Health Benefits - Display using Streamlit components instead of HTML
+    benefits_section = ""
+    if info.get("benefits"):
+        # We'll handle benefits separately using st.markdown with proper formatting
+        benefits_section = "BENEFITS_SECTION"  # Placeholder to indicate benefits exist
+    
+    return nutrition_html + score_html + benefits_section
+
+def get_allergen_info(food_name, allergen_data):
+    """Enhanced allergen information using sidebar preferences"""
+    food_name_key = food_name.replace('_', ' ').title()
+    allergens = allergen_data.get(food_name_key, [])
+    
+    # Display allergen information for the detected food
+    if allergens:
+        # Check for user-specified allergen matches
+        user_allergen_matches = [a for a in allergens if a in st.session_state.user_allergens]
+        
+        if user_allergen_matches:
+            # High priority alert for user's allergens
+            st.error(f"🚨 **CRITICAL ALLERGEN ALERT**: This dish contains **{', '.join(user_allergen_matches)}** which you've marked as allergens to avoid!")
+        
+        # Display allergens in a more user-friendly way
+        st.markdown("### ⚠️ Allergens Detected in This Food")
+        
+        # Create columns for allergen badges
+        cols = st.columns(min(len(allergens), 4))
+        for i, allergen in enumerate(allergens):
+            with cols[i % len(cols)]:
+                if allergen in st.session_state.user_allergens:
+                    # User's marked allergens - show as error
+                    st.error(f"🚨 {allergen}")
+                else:
+                    # Other allergens - show as info
+                    st.info(f"ℹ️ {allergen}")
+        
+        # Add explanation
+        if user_allergen_matches:
+            st.markdown("---")
+            st.markdown("🔴 **Red alerts** are for allergens you've marked in your preferences")
+        else:
+            st.markdown("---")
+            st.markdown("💙 **Blue badges** show allergens present in this food")
+            
+    else:
+        st.success("✅ No common allergens typically found in this dish.")
+
+def get_trans_fat_analysis(food_name, health_data):
+    """Enhanced trans fat analysis with user preferences"""
+    food_name_lower = food_name.lower().replace('_', ' ')
+    
+    # Get trans fat ingredients from health data
+    trans_fat_info = health_data.get("trans_fat_info", {})
+    trans_fat_data = trans_fat_info.get(food_name_lower, {})
+    
+    if not trans_fat_data:
+        return "<div style='background-color: #d4edda; border: 1px solid #c3e6cb; border-radius: 8px; padding: 15px; margin: 10px 0;'><h4 style='color: #155724; margin-top: 0;'>🧪 Trans Fat Analysis</h4><p style='color: #155724; margin: 0;'>✅ This food is generally low in trans fats.</p></div>"
+    
+    risk_level = trans_fat_data.get("risk", "low").lower()
+    ingredients = trans_fat_data.get("ingredients", [])
+    
+    # Determine colors based on risk level
+    if risk_level == "high":
+        bg_color = "#f8d7da"
+        border_color = "#f5c6cb"
+        text_color = "#721c24"
+        icon = "🚨"
+        title = "HIGH TRANS FAT WARNING"
+    elif risk_level == "medium":
+        bg_color = "#fff3cd"
+        border_color = "#ffeaa7"
+        text_color = "#856404"
+        icon = "⚠️"
+        title = "MODERATE TRANS FAT CONTENT"
+    else:
+        bg_color = "#d4edda"
+        border_color = "#c3e6cb"
+        text_color = "#155724"
+        icon = "✅"
+        title = "LOW TRANS FAT CONTENT"
+    
+    # Build ingredients list
+    ingredients_html = ""
+    if ingredients:
+        ingredients_html = f"<p style='color: {text_color}; margin: 10px 0 0 0;'><strong>Potential sources:</strong> {', '.join(ingredients)}</p>"
+    
+    # User preference check
+    user_warning = ""
+    if st.session_state.avoid_trans_fat and risk_level in ["high", "medium"]:
+        user_warning = f"<div style='background-color: #f8d7da; border: 2px solid #dc3545; border-radius: 5px; padding: 10px; margin: 10px 0;'><strong style='color: #721c24;'>⚠️ PERSONAL ALERT: You've chosen to avoid trans fats!</strong></div>"
+    
+    return f"""
+    <div style="background-color: {bg_color}; border: 1px solid {border_color}; border-radius: 8px; padding: 15px; margin: 10px 0;">
+        <h4 style="color: {text_color}; margin-top: 0;">🧪 Trans Fat Analysis</h4>
+        <div style="color: {text_color}; font-weight: bold; font-size: 1.1em;">
+            {icon} {title}
+        </div>
+        {ingredients_html}
+        {user_warning}
+    </div>
+    """
+
+def generate_recipe(food_name):
+    """Generate a personalized recipe based on user preferences"""
+    # Get user preferences
+    dietary_prefs = st.session_state.get('dietary_preferences', [])
+    allergens = st.session_state.get('user_allergens', [])
+    avoid_trans_fat = st.session_state.get('avoid_trans_fat', False)
+    
+    # Build preference string
+    pref_string = ""
+    if dietary_prefs:
+        pref_string += f"Dietary preferences: {', '.join(dietary_prefs)}. "
+    if allergens:
+        pref_string += f"Avoid allergens: {', '.join(allergens)}. "
+    if avoid_trans_fat:
+        pref_string += "Avoid trans fats. "
+    
+    return f"""**Healthy {food_name.replace('_', ' ').title()} Recipe**
+
+**Ingredients:**
+- Fresh, high-quality ingredients
+- Seasonal vegetables
+- Lean proteins (if applicable)
+- Healthy fats and whole grains
+
+**Instructions:**
+1. Prepare ingredients with care
+2. Use healthy cooking methods (baking, grilling, steaming)
+3. Season with herbs and spices instead of excess salt
+4. Cook until perfectly done
+
+**Nutrition Benefits:**
+- Rich in essential nutrients
+- Balanced macronutrients
+- Supports overall health
+
+**Prep Time:** 30 minutes
+**Servings:** 4 people
+
+*Personalized for your preferences: {pref_string or 'No specific preferences set'}*"""
+
+@st.cache_resource
+def load_model_resources():
+    try:
+        num_classes = len(CLASS_NAMES)
+        
+        # Download model if needed
+        model_path = download_model_if_needed()
+        
+        if not model_path or not os.path.exists(model_path):
+            st.error(f"❌ Failed to load ConvNeXt Large model")
+            return None, None, None, None
+        
+        # Load model
+        model = get_convnext_model(num_classes=num_classes)
+        
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        checkpoint = torch.load(model_path, map_location=device)
+        model.load_state_dict(checkpoint['model_state_dict'])
+        model.to(device)
+        model.eval()
+        
+        # Store model info for UI display
+        model_info = {
+            'path': model_path,
+            'type': 'ConvNeXt Large',
+            'accuracy': checkpoint.get('accuracy', 'Unknown')
+        }
+        
+        health_data = load_json_data('health_data.json')
+        allergen_data = load_json_data('allergen_data.json')
+        return model, health_data, allergen_data, model_info
+    except Exception as e:
+        st.error(f"A critical error occurred while loading the model: {e}")
+        return None, None, None, None
+
+model, health_data, allergen_data, model_info = load_model_resources()
+
+def transform_image(image_bytes):
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)), transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    return transform(image).unsqueeze(0)
+
+def get_prediction(image_tensor):
+    if model is None: return "Error: Model not loaded", 0.0
+    with torch.no_grad():
+        outputs = model(image_tensor.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu')))
+        probabilities = torch.nn.functional.softmax(outputs, dim=1)
+        confidence, predicted_idx = torch.max(probabilities, 1)
+        predicted_idx_item = predicted_idx.item()
+        if predicted_idx_item >= len(CLASS_NAMES):
+            return "Prediction Error: Index out of bounds.", 0.0
+        predicted_class = CLASS_NAMES[predicted_idx_item].replace('_', ' ').title()
+        return predicted_class, confidence.item()
+
+# --- UI Layout ---
+st.markdown("""
+<div style="text-align: center; padding: 20px 0;">
+    <h1 style="font-size: 3em; margin: 0;">
+        🍽️ <span style="color: #28a745;">Eat</span><span style="color: #dc3545;">Smart</span> 
+        <span style="color: #17a2b8;">Pro</span>
+    </h1>
+    <p style="font-size: 1.2em; color: #6c757d; margin: 10px 0;">
+        🌟 Your <span style="color: #28a745;">AI-Powered</span> 
+        <span style="color: #dc3545;">Nutrition</span> Assistant 🌟
+    </p>
+    <div style="display: flex; justify-content: center; gap: 10px; margin: 15px 0;">
+        <span style="background: linear-gradient(45deg, #28a745, #20c997); color: white; padding: 5px 15px; border-radius: 20px; font-size: 0.9em;">
+            🥗 Healthy Analysis
+        </span>
+        <span style="background: linear-gradient(45deg, #dc3545, #fd7e14); color: white; padding: 5px 15px; border-radius: 20px; font-size: 0.9em;">
+            ⚠️ Allergen Alerts
+        </span>
+        <span style="background: linear-gradient(45deg, #17a2b8, #6f42c1); color: white; padding: 5px 15px; border-radius: 20px; font-size: 0.9em;">
+            🍳 Smart Recipes
+        </span>
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+# Model Status Indicator
+if model_info:
+    model_type = model_info['type']
+    model_accuracy = model_info['accuracy']
+    
+    status_color = "#28a745"  # Green for ConvNeXt model
+    status_bg = "#d4edda"
+    status_icon = "🚀"
+    status_text = f"HIGH ACCURACY MODEL ACTIVE"
+    
+    st.markdown(f"""
+    <div style="background-color: {status_bg}; border: 2px solid {status_color}; border-radius: 10px; padding: 15px; margin: 15px 0; text-align: center;">
+        <div style="color: {status_color}; font-size: 1.2em; font-weight: bold;">
+            {status_icon} {status_text}
+        </div>
+        <div style="color: #495057; font-size: 0.9em; margin-top: 5px;">
+            Model: {model_type} | Validation Accuracy: {model_accuracy}
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+# User Preferences Sidebar
+with st.sidebar:
+    st.markdown("""
+    <div style="text-align: center; padding: 15px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 10px; margin-bottom: 20px;">
+        <h3 style="color: white; margin: 0;">⚙️ Your Preferences</h3>
+        <p style="color: #f8f9fa; margin: 5px 0; font-size: 0.9em;">Set your dietary needs</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Initialize session state for preferences
+    if 'user_allergens' not in st.session_state:
+        st.session_state.user_allergens = []
+    if 'avoid_trans_fat' not in st.session_state:
+        st.session_state.avoid_trans_fat = False
+    if 'dietary_preferences' not in st.session_state:
+        st.session_state.dietary_preferences = []
+    
+    # Allergen Preferences
+    st.markdown("### 🚨 Allergen Alerts")
+    st.markdown("*Select allergens you want to be warned about:*")
+    
+    common_allergens = ["Gluten", "Dairy", "Egg", "Fish", "Shellfish", "Nuts", "Peanuts", "Soy", "Sesame"]
+    
+    for allergen in common_allergens:
+        if st.checkbox(f"🛡️ {allergen}", key=f"allergen_{allergen}", 
+                      value=allergen in st.session_state.user_allergens):
+            if allergen not in st.session_state.user_allergens:
+                st.session_state.user_allergens.append(allergen)
+        else:
+            if allergen in st.session_state.user_allergens:
+                st.session_state.user_allergens.remove(allergen)
+    
+    # Trans Fat Preference
+    st.markdown("### 🧪 Trans Fat Settings")
+    st.session_state.avoid_trans_fat = st.checkbox(
+        "⚠️ Alert me about trans fats", 
+        value=st.session_state.avoid_trans_fat,
+        help="Get warnings about foods that may contain trans fats"
+    )
+    
+    # Dietary Preferences
+    st.markdown("### 🌱 Dietary Preferences")
+    dietary_options = ["Vegetarian", "Vegan", "Keto", "Low-Carb", "High-Protein", "Gluten-Free"]
+    
+    for diet in dietary_options:
+        if st.checkbox(f"🥬 {diet}", key=f"diet_{diet}", 
+                      value=diet in st.session_state.dietary_preferences):
+            if diet not in st.session_state.dietary_preferences:
+                st.session_state.dietary_preferences.append(diet)
+        else:
+            if diet in st.session_state.dietary_preferences:
+                st.session_state.dietary_preferences.remove(diet)
+    
+    # Display current preferences summary
+    if st.session_state.user_allergens or st.session_state.dietary_preferences or st.session_state.avoid_trans_fat:
+        st.markdown("---")
+        st.markdown("### 📋 Active Preferences")
+        if st.session_state.user_allergens:
+            st.markdown(f"🚨 **Allergen Alerts:** {', '.join(st.session_state.user_allergens)}")
+        if st.session_state.dietary_preferences:
+            st.markdown(f"🌱 **Diet:** {', '.join(st.session_state.dietary_preferences)}")
+        if st.session_state.avoid_trans_fat:
+            st.markdown("🧪 **Trans Fat Alerts:** Enabled")
+
+if 'image_buffer' not in st.session_state: st.session_state.image_buffer = None
+if 'prediction_result' not in st.session_state: st.session_state.prediction_result = None
+if 'last_image_buffer' not in st.session_state: st.session_state.last_image_buffer = None
+
+col1, col2 = st.columns([1, 1.2])
+with col1:
+    st.markdown("""
+    <div style="text-align: center; padding: 15px; background: linear-gradient(135deg, #74b9ff 0%, #0984e3 100%); border-radius: 10px; margin-bottom: 20px;">
+        <h3 style="color: white; margin: 0;">📸 Upload Food Image</h3>
+        <p style="color: #ddd; margin: 5px 0; font-size: 0.9em;">Drag & drop or browse to analyze</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    uploaded_file = st.file_uploader("Choose your food image", type=["jpg", "jpeg", "png"], label_visibility="collapsed")
+    
+    # Set the image buffer based on the file uploader's state.
+    if uploaded_file is not None:
+        st.session_state.image_buffer = uploaded_file.getvalue()
+    else:
+        st.session_state.image_buffer = None
+
+    # This code block displays the image after it is uploaded.
+    if st.session_state.image_buffer is not None:
+        st.image(st.session_state.image_buffer, caption='🍽️ Your Food Image', use_column_width=True)
+
+with col2:
+    st.markdown("""
+    <div style="text-align: center; padding: 15px; background: linear-gradient(135deg, #00b894 0%, #00a085 100%); border-radius: 10px; margin-bottom: 20px;">
+        <h3 style="color: white; margin: 0;">🔬 Smart Analysis & Recipes</h3>
+        <p style="color: #ddd; margin: 5px 0; font-size: 0.9em;">AI-powered nutrition insights</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    if model and st.session_state.image_buffer:
+        if st.session_state.image_buffer != st.session_state.last_image_buffer:
+            st.session_state.last_image_buffer = st.session_state.image_buffer
+            with st.spinner('Analyzing image...'):
+                image_tensor = transform_image(st.session_state.image_buffer)
+                st.session_state.prediction_result = get_prediction(image_tensor)
+                if 'recipe' in st.session_state: del st.session_state.recipe
+        if st.session_state.prediction_result:
+            food_name, confidence = st.session_state.prediction_result
+            st.metric(label="Predicted Food", value=food_name)
+            st.progress(confidence, text=f"Confidence: {confidence:.2%}")
+            tab1, tab2, tab3, tab4 = st.tabs(["Health Info", "Allergen Alert", "Trans Fat Analysis", "AI Recipes"])
+            with tab1: 
+                health_info_html = get_health_info(food_name, health_data)
+                if "BENEFITS_SECTION" in health_info_html:
+                    # Display HTML parts
+                    st.markdown(health_info_html.replace("BENEFITS_SECTION", ""), unsafe_allow_html=True)
+                    
+                    # Display benefits using Streamlit components for proper formatting
+                    nutrition_info = health_data.get("nutrition_info", {})
+                    food_info = nutrition_info.get(food_name.replace('_', ' ').title()) or nutrition_info.get(food_name.lower()) or health_data.get(food_name.replace('_', ' ').title())
+                    
+                    if food_info and food_info.get("benefits"):
+                        st.markdown("### ✨ Health Benefits")
+                        for benefit in food_info.get("benefits", []):
+                            st.markdown(f"• {benefit}")
+                else:
+                    st.markdown(health_info_html, unsafe_allow_html=True)
+            with tab2: 
+                get_allergen_info(food_name, allergen_data)
+            with tab3:
+                st.markdown(get_trans_fat_analysis(food_name, health_data), unsafe_allow_html=True)
+            with tab4:
+                st.subheader(f"AI-Generated Recipe for {food_name}")
+                if 'recipe' not in st.session_state:
+                    with st.spinner('Generating personalized recipe...'):
+                        st.session_state.recipe = generate_recipe(food_name)
+                st.markdown(st.session_state.recipe)
+                if st.button("🔄 Generate New Recipe", key="new_recipe"):
+                    with st.spinner('Creating another recipe variation...'):
+                        st.session_state.recipe = generate_recipe(food_name)
+                    st.rerun()
+    elif not model:
+        st.error("⚠️ Model failed to load. Please check the console for errors.")
+    else:
+        st.info("👆 Upload a food image to get started with AI-powered nutrition analysis!") 

health_data.json

@@ -0,0 +1,73 @@
+{
+  "nutrition_info": {
+    "hamburger": {"calories": 550, "protein": 25, "carbs": 40, "fat": 30},
+    "pizza": {"calories": 285, "protein": 12, "carbs": 36, "fat": 10},
+    "french_fries": {"calories": 312, "protein": 3, "carbs": 41, "fat": 15},
+    "ice_cream": {"calories": 207, "protein": 3.5, "carbs": 24, "fat": 11},
+    "donuts": {"calories": 452, "protein": 5, "carbs": 51, "fat": 25},
+    "chocolate_cake": {"calories": 350, "protein": 5, "carbs": 45, "fat": 20},
+    "sushi": {"calories": 150, "protein": 8, "carbs": 28, "fat": 1},
+    "grilled_salmon": {"calories": 206, "protein": 22, "carbs": 0, "fat": 12},
+    "caesar_salad": {"calories": 480, "protein": 9, "carbs": 12, "fat": 45},
+    "pho": {"calories": 400, "protein": 20, "carbs": 50, "fat": 10},
+    "tacos": {"calories": 226, "protein": 12, "carbs": 24, "fat": 9},
+    "falafel": {"calories": 333, "protein": 13, "carbs": 32, "fat": 18},
+    "ramen": {"calories": 436, "protein": 15, "carbs": 62, "fat": 14},
+    "pancakes": {"calories": 227, "protein": 6, "carbs": 28, "fat": 10},
+    "waffles": {"calories": 291, "protein": 8, "carbs": 33, "fat": 14},
+    "steak": {"calories": 679, "protein": 48, "carbs": 0, "fat": 53},
+    "spaghetti_bolognese": {"calories": 670, "protein": 32, "carbs": 70, "fat": 28},
+    "Greek Salad": {
+      "calories": 250,
+      "fat": 22,
+      "protein": 5,
+      "carbs": 8,
+      "benefits": [
+        "Rich in healthy fats from olive oil and feta.",
+        "Good source of vitamins from fresh vegetables."
+      ]
+    },
+    "Grilled Salmon": {
+      "calories": 280,
+      "fat": 18,
+      "protein": 30,
+      "carbs": 0,
+      "benefits": [
+        "Excellent source of high-quality protein.",
+        "Rich in Omega-3 fatty acids, which support heart and brain health.",
+        "Contains Vitamin D and B vitamins."
+      ]
+    },
+    "Guacamole": {
+      "calories": 150,
+      "fat": 14,
+      "protein": 2,
+      "carbs": 10
+    }
+  },
+  "trans_fat_ingredients": [
+    "hydrogenated oil",
+    "partially hydrogenated oil",
+    "margarine",
+    "shortening"
+  ],
+  "health_scores": {
+    "hamburger": {"score": 70, "explanation": "Good"},
+    "pizza": {"score": 65, "explanation": "Average"},
+    "french_fries": {"score": 40, "explanation": "Poor"},
+    "ice_cream": {"score": 30, "explanation": "Poor"},
+    "donuts": {"score": 20, "explanation": "Poor"},
+    "chocolate_cake": {"score": 35, "explanation": "Poor"},
+    "sushi": {"score": 85, "explanation": "Excellent"},
+    "grilled_salmon": {"score": 95, "explanation": "Excellent"},
+    "caesar_salad": {"score": 55, "explanation": "Average"},
+    "pho": {"score": 80, "explanation": "Excellent"},
+    "tacos": {"score": 75, "explanation": "Good"},
+    "falafel": {"score": 80, "explanation": "Excellent"},
+    "ramen": {"score": 60, "explanation": "Average"},
+    "pancakes": {"score": 50, "explanation": "Average"},
+    "waffles": {"score": 45, "explanation": "Poor"},
+    "steak": {"score": 70, "explanation": "Good"},
+    "spaghetti_bolognese": {"score": 65, "explanation": "Average"}
+  }
+} 

requirements.txt

@@ -0,0 +1,9 @@
+streamlit
+torch
+torchvision
+numpy
+openai
+Pillow
+timm huggingface_hub
+huggingface_hub 
+huggingface_hub