Your AI-Powered Smart Food Companion with Beautiful Modern Design

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✨ Features

🤖 AI Chat Assistant Powered by Google Gemini 2.0 Natural language queries • Product recommendations Persistent history • Add to cart from chat	🌤️ Weather Intelligence Location-based suggestions Weather-aware meals • Seasonal menus Automatic updates
👨‍🍳 Cooking Mode Step-by-step recipes Live timer • Ingredient checklists Progress tracking	📸 Food Scanner Deep Learning Model (EfficientNetB0) Camera & gallery • AI recognition • Confidence scores Top-5 predictions • Custom training
🛒 Smart Cart Real-time Firebase sync Quantity management • Price calculation Persistent across sessions	🔐 Secure Auth Firebase Authentication Profile management • Photo upload Address management

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🎯 App Workflow

graph LR
    A[🏠 Home Screen] --> B[🌤️ Weather Check]
    B --> C[🍽️ Food Suggestions]
    A --> D[🤖 AI Chat]
    D --> E[💬 Ask Questions]
    E --> F[🛒 Add to Cart]
    A --> G[📸 Food Scanner]
    G --> H[🔍 Scan Food]
    H --> I[📦 Product Details]
    I --> F
    C --> J[👨‍🍳 Cooking Mode]
    J --> K[⏱️ Timer & Steps]
    F --> L[💳 Checkout]
    L --> M[📍 Address & Payment]

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🚀 Quick Start

📱 Flutter Setup

# Clone repository
git clone https://github.com/khanak0509/CookVision.git
cd CookVision/food_app

# Install dependencies
flutter pub get

# Run the app
flutter run

🐍 Backend Setup

# Navigate to project directory
cd food_app

# Install Python dependencies
pip install -r requirements.txt

# Create .env file with your API key
echo "GOOGLE_API_KEY=your_gemini_api_key_here" > .env

# Start FastAPI server
python3 -m uvicorn main:app --reload

# Server will run on http://localhost:8000

🔥 Firebase Setup

1. Create project at Firebase Console
2. Enable these services:
   • ✅ Authentication (Email/Password)
   • ✅ Cloud Firestore
   • ✅ Cloud Storage
3. Download configuration files:
   • Android: google-services.json → android/app/
   • iOS: GoogleService-Info.plist → ios/Runner/
4. Run Firebase CLI: flutterfire configure
5. Update Firestore rules for security

🤖 Food Recognition Model Setup

# 1. Check if model is ready
python3 check_model.py

# 2. If model not found, train it
# Open train.ipynb in Jupyter or VS Code and run all cells
# Training takes 30-60 minutes

# 3. Verify model files exist
ls -lh food_recognition_model.h5 food_labels.json

# 4. Model is automatically loaded when backend starts
python3 -m uvicorn main:app --reload
# Look for: "✅ Food recognition model loaded"

📚 Detailed Guide: See FOOD_RECOGNITION_SETUP.md

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