价格监控系统

┌─────────────────┐
│  Telegram Bot   │ ← User interaction
└────────┬────────┘
         │
    ┌────▼─────┐
    │   N8N    │ ← Workflow orchestration
    │ Workflow │
    └────┬─────┘
         │
    ┌────▼─────────────┐
    │  FastAPI Server  │ ← REST API
    └────┬─────────────┘
         │
    ┌────▼──────────────────────┐
    │   LangGraph AI Agents     │
    ├───────────────────────────┤
    │ • Price Monitor Agent     │ ← Web scraping & extraction
    │ • Analysis Agent          │ ← Trend detection & insights
    │ • Notification Agent      │ ← Report generation
    └────┬──────────────────────┘
         │
    ┌────▼─────┐
    │  SQLite  │ ← Data persistence
    └──────────┘

# Clone the repository
git clone https://github.com/yourusername/ai-price-monitor.git
cd ai-price-monitor

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your API keys

# Anthropic API Key (required)
ANTHROPIC_API_KEY=sk-ant-api03-xxxxx

# Database
DATABASE_PATH=price_monitor.db

# Monitoring Configuration
CHECK_INTERVAL_HOURS=1
PRICE_CHANGE_THRESHOLD=5.0  # Alert threshold %

# Notifications
EMAIL_RECIPIENT=your-email@example.com
ADMIN_TELEGRAM=your_telegram_chat_id

# Start FastAPI server
python main.py

# Server runs on http://localhost:8000
# API docs: http://localhost:8000/docs

# In a new terminal
ngrok http 8000

# Copy the ngrok URL (e.g., https://abc123.ngrok.io)
# Use this URL in N8N as API_BASE_URL

# List all products
curl http://localhost:8000/products

# Add a product
curl -X POST http://localhost:8000/add-product \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Samsung Galaxy A35 5G",
    "url": "https://www.amazon.fr/dp/B0CV4HLFPX",
    "selector": ".a-price-whole"
  }'

# Test scraping configuration
curl -X POST http://localhost:8000/test-scraping \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.amazon.fr/dp/B0CV4HLFPX",
    "selector": ".a-price-whole"
  }'

# Run monitoring
curl -X POST http://localhost:8000/monitor

# Get product statistics
curl http://localhost:8000/stats/Samsung%20Galaxy%20A35%205G

# View HTML report
open http://localhost:8000/report

User: "show all products"
Bot: 📊 Here are all monitored products:
     1. Samsung Galaxy A35 5G - €229.00
     2. GRIFEMA Spray Bottle - €8.99

User: "monitor Samsung Galaxy"
Bot: 🔍 Monitoring Complete!
     ✅ Scraped: 1 products
     💰 Opportunities: 1
     [Sends detailed report]

User: "price history of Samsung"
Bot: 📜 Price History: Samsung Galaxy A35 5G
     • €229.00 - Jan 14, 03:00 PM
     • €250.00 - Jan 14, 01:00 PM
     📉 Trend: -8.4%

User: "stats for Samsung Galaxy A35 5G"
Bot: 📈 Statistics for Samsung Galaxy A35 5G
     💰 Current: €229.00
     📉 Min: €229.00
     📊 Avg: €239.50
     📈 Max: €250.00

price-monitor/
├── main.py                      # FastAPI application
├── config.py                    # Configuration settings
├── requirements.txt             # Python dependencies
├── .env.example                 # Environment template
├── n8n_workflow_price_monitor.json  # N8N workflow
├── insert_test_price.py         # Test data helper 
├── agents/
│   ├── __init__.py
│   ├── price_monitor.py         # Web scraping & price extraction
│   ├── analysis.py              # Trend analysis & opportunity detection
│   └── notification.py          # Report generation & alerts
│
├── database/
│   ├── __init__.py
│   └── models.py                # SQLite database models
│
├── mcp/                         # Model Context Protocol (optional)
│   ├── __init__.py
│   └── clients.py
│
└── reports/                     # Generated reports
    ├── latest_report.html
    ├── latest_report.json
    └── report_*.html

CREATE TABLE products (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    name TEXT NOT NULL,
    url TEXT NOT NULL UNIQUE,
    selector TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE price_history (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    product_id INTEGER NOT NULL,
    price REAL NOT NULL,
    currency TEXT DEFAULT 'EUR',
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    metadata TEXT,
    FOREIGN KEY (product_id) REFERENCES products (id)
);

CREATE TABLE alerts (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    product_id INTEGER NOT NULL,
    old_price REAL NOT NULL,
    new_price REAL NOT NULL,
    change_percent REAL NOT NULL,
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    notified BOOLEAN DEFAULT 0,
    FOREIGN KEY (product_id) REFERENCES products (id)
);

# Access database
sqlite3 price_monitor.db

# View all products
SELECT * FROM products;

# View recent price history
SELECT p.name, ph.price, ph.timestamp 
FROM products p 
LEFT JOIN price_history ph ON p.id = ph.product_id 
ORDER BY ph.timestamp DESC 
LIMIT 10;

# Insert test price
INSERT INTO price_history (product_id, price, timestamp) 
VALUES (1, 199.99, datetime('now'));

# Start the server
python main.py

# In another terminal, run tests
curl -X POST http://localhost:8000/monitor

# Check the report
curl http://localhost:8000/report

# Verify database
python insert_test_price.py