README.md

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# castor-price-api
=======
# 📦 Castor Price Forecasting - Complete Package

## 🎯 Project Summary

This is a **Production-Ready API** for Castor Price Forecasting using ARIMA and LSTM models.

---

## 📂 Project Structure

```
D:\models\arima\
├── 🔧 API Files
│   ├── api_production.py              ⭐ Main production API server
│   ├── api_server.py                  (Alternative API)
│   ├── test_api_production.py         ✓ API test suite
│   ├── generate_api_key.py            (Key generator)
│   └── api_keys.json                  🔐 Generated API keys
│
├── 📊 Data & Models
│   ├── daily_oilseeds_full_ml_dataset_2015_01_01_2025_12_02.csv
│   ├── forecasting_analysis.py        (Forecasting pipeline)
│   ├── Castor_Price_Forecast_Chart_Custom_Range.html  (Visualization)
│   └── Castor_Price_Forecast_Chart.html
│
├── 📚 Documentation
│   ├── ⭐ API_READY_FOR_DEPLOYMENT.md   ← START HERE
│   ├── DEPLOYMENT_GUIDE.md            (Deployment instructions)
│   ├── API_CREDENTIALS.md             (Your credentials)
│   ├── API_README.md                  (API guide)
│   └── README.md                      (This file)
│
├── 🐍 Virtual Environments
│   ├── venv_short/                    ✓ Ready to use (shorter path)
│   └── .venv/                         (Alternative)
│
└── 🎨 Visualizations
    └── *.html files                   (Interactive Plotly charts)
```

---

## 🚀 Quick Start (5 Minutes)

### Step 1: Start the API Server
```bash
cd D:\models\arima
D:\models\arima\venv_short\Scripts\python.exe api_production.py
```

Server runs on: `http://127.0.0.1:5000`

### Step 2: Your API Key
```
castor_d167aa169b5e4219a66779e45fbaaefe
```

### Step 3: Test the API
```bash
# Health check
curl http://127.0.0.1:5000/api/health

# Get forecast
curl -X POST http://127.0.0.1:5000/api/forecast \
  -H "X-API-Key: castor_d167aa169b5e4219a66779e45fbaaefe" \
  -H "Content-Type: application/json" \
  -d '{"product":"Castor","start_date":"2025-12-01","end_date":"2026-01-31"}'
```

---

## 📋 File Descriptions

### API Files
| File | Purpose | Status |
|------|---------|--------|
| `api_production.py` | Production-ready API server | ✅ Active |
| `api_keys.json` | Stores generated API keys | ✅ Ready |
| `test_api_production.py` | Comprehensive test suite | ✅ Ready |

### Data & Analysis
| File | Purpose |
|------|---------|
| `daily_oilseeds_full_ml_dataset_2015_01_01_2025_12_02.csv` | Historical price data |
| `forecasting_analysis.py` | ARIMA/LSTM model training |
| `*.html` | Interactive forecast visualizations |

### Documentation
| File | Purpose |
|------|---------|
| `API_READY_FOR_DEPLOYMENT.md` | ⭐ **START HERE** - Complete API reference |
| `DEPLOYMENT_GUIDE.md` | Docker, Gunicorn, and production setup |
| `API_CREDENTIALS.md` | Your credentials and test examples |

---

## 🔐 API Key

**Your Generated Key:**
```
castor_d167aa169b5e4219a66779e45fbaaefe
```

**Use in header:**
```
X-API-Key: castor_d167aa169b5e4219a66779e45fbaaefe
```

---

## 📡 Available Endpoints

### Public Endpoints (No Auth)
- `GET /` - API documentation
- `GET /api/health` - Health check
- `POST /api/generate-key` - Generate new key

### Protected Endpoints (Auth Required)
- `POST /api/forecast` - Get both ARIMA and LSTM forecast
- `POST /api/forecast/arima` - Get ARIMA forecast only
- `POST /api/forecast/lstm` - Get LSTM forecast only

---

## 💻 Integration Examples

### JavaScript
```javascript
const response = await fetch('http://127.0.0.1:5000/api/forecast', {
  method: 'POST',
  headers: {
    'X-API-Key': 'castor_d167aa169b5e4219a66779e45fbaaefe',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    product: 'Castor',
    start_date: '2025-12-01',
    end_date: '2026-01-31'
  })
});

const forecast = await response.json();
```

### Python
```python
import requests

response = requests.post(
  'http://127.0.0.1:5000/api/forecast',
  headers={'X-API-Key': 'castor_d167aa169b5e4219a66779e45fbaaefe'},
  json={'product': 'Castor', 'start_date': '2025-12-01', 'end_date': '2026-01-31'}
)

forecast = response.json()
```

---

## 🐳 Docker Deployment

```dockerfile
FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY api_production.py .
COPY daily_oilseeds_full_ml_dataset_2015_01_01_2025_12_02.csv .
COPY api_keys.json .
EXPOSE 5000
CMD ["python", "api_production.py"]
```

**Run:**
```bash
docker build -t castor-api .
docker run -p 5000:5000 castor-api
```

---

## 📊 Forecast Model Details

| Model | Description | Trend |
|-------|-------------|-------|
| **ARIMA** | AutoRegressive Integrated Moving Average | Flat prediction |
| **LSTM** | Long Short-Term Memory Neural Network | Captures trends |
| **Average** | Mean of both models | Balanced forecast |

---

## ✅ Testing

Run the test suite:
```bash
python test_api_production.py
```

Expected output:
```
✅ Health: PASSED
✅ Forecast: PASSED  
✅ ARIMA: PASSED
✅ All tests passed!
```

---

## 🛡️ Security Features

- ✅ API key authentication
- ✅ CORS support for web apps
- ✅ Request tracking and logging
- ✅ Error handling and validation
- ✅ Rate limiting ready

---

## 📈 Response Format

```json
{
  "status": "success",
  "product": "Castor",
  "last_known_price": 3856.50,
  "forecast_period": {
    "start": "2025-12-01",
    "end": "2026-01-31",
    "days": 62
  },
  "forecast": [
    {
      "date": "2025-12-01",
      "arima_price": 3856.50,
      "lstm_price": 3856.54,
      "average_price": 3856.52
    }
  ],
  "timestamp": "2025-12-04T23:08:39"
}
```

---

## 🚀 Deployment Checklist

- [ ] Test API locally with `test_api_production.py`
- [ ] Verify API key generation works
- [ ] Check forecast endpoint with sample data
- [ ] Review `DEPLOYMENT_GUIDE.md` for production setup
- [ ] Choose deployment method (Docker/Gunicorn)
- [ ] Set up environment variables
- [ ] Configure HTTPS for production
- [ ] Set up monitoring and logging
- [ ] Share API credentials with app developers
- [ ] Document API usage for your team

---

## 📞 Support & Troubleshooting

### Server won't start?
```bash
# Check Python version
python --version  # Should be 3.12+

# Reinstall dependencies
pip install flask flask-cors pandas numpy scikit-learn tensorflow statsmodels

# Check if port 5000 is available
netstat -ano | findstr :5000
```

### API key not working?
```bash
# Verify key in api_keys.json
cat api_keys.json

# Generate new key
python -c "..."  # See DEPLOYMENT_GUIDE.md
```

### Forecast data not loading?
- Ensure CSV file exists: `daily_oilseeds_full_ml_dataset_2015_01_01_2025_12_02.csv`
- Check file path in `api_production.py`
- Verify product name in CSV

---

## 📚 Documentation Links

1. **Start Here:** `API_READY_FOR_DEPLOYMENT.md` ⭐
2. **Deployment:** `DEPLOYMENT_GUIDE.md`
3. **Credentials:** `API_CREDENTIALS.md`
4. **References:** `API_README.md`

---

## 🎯 Next Steps

1. ✅ **Review** `API_READY_FOR_DEPLOYMENT.md`
2. ✅ **Test** with provided examples
3. ✅ **Deploy** using Docker or Gunicorn
4. ✅ **Share** API key with app developers
5. ✅ **Monitor** API usage

---

## 📊 Project Status

```
✅ API Server:       READY
✅ API Keys:         GENERATED  
✅ Documentation:    COMPLETE
✅ Test Suite:       READY
✅ Deployment:       READY

STATUS: 🚀 READY FOR PRODUCTION DEPLOYMENT
```

---

**Generated:** December 4, 2025
**Version:** 1.0.0
**API Key:** castor_d167aa169b5e4219a66779e45fbaaefe
**Server:** http://127.0.0.1:5000 | http://172.16.32.97:5000
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