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title: AgriPredict Analysis Service
emoji: 🌾
colorFrom: green
colorTo: blue
sdk: docker
app_file: main.py
pinned: false
---
# AgriPredict Analysis Service
A production-ready FastAPI service for agricultural demand forecasting with multiple ML models including CatBoost ensemble methods.
## Features
- **Multi-Model Forecasting**: SMA, WMA, Exponential Smoothing, ARIMA, and CatBoost
- **RESTful API**: Clean endpoints for health checks, model listing, and forecasting
- **Docker Ready**: Containerized for easy deployment
- **Hugging Face Spaces**: Configured for cloud deployment
- **Comprehensive Testing**: Built-in test suite for validation
- **Confidence Intervals**: Uncertainty quantification for predictions
- **Real-time Processing**: Asynchronous processing for high performance
## Quick Start
### 1. Install Dependencies
```bash
python run.py install
```
### 2. Run the Service
```bash
python run.py run
```
The API will be available at:
- **Local Service**: http://localhost:7860
- **Local Documentation**: http://localhost:7860/docs
- **Local Health Check**: http://localhost:7860/health
### Production (Hugging Face Spaces)
- **Live Service**: https://adsurkasur-agripredict-analysis.hf.space
- **Live Documentation**: https://adsurkasur-agripredict-analysis.hf.space/docs
- **Live Health Check**: https://adsurkasur-agripredict-analysis.hf.space/health
### 3. Test the Service
```bash
python run.py test
```
### 4. Train CatBoost Model (Optional)
```bash
python run.py train
```
## API Endpoints
### GET /health
Health check endpoint
```json
{
"status": "healthy",
"service": "AgriPredict Analysis Service",
"version": "1.0.0"
}
```
### GET /models
List available forecasting models
```json
{
"models": ["SMA", "WMA", "ES", "ARIMA", "CatBoost"]
}
```
### POST /forecast
Generate demand forecasts
**Request Body:**
```json
{
"historical_data": [
{
"date": "2023-01-01",
"demand": 100,
"price": 50.0,
"weather_temp": 25.0
}
],
"forecast_horizon": 7,
"models": ["SMA", "WMA", "ES"],
"confidence_level": 0.95
}
```
**Response:**
```json
{
"forecast_horizon": 7,
"models_used": ["SMA", "WMA", "ES"],
"forecast_dates": ["2023-01-08", "2023-01-09", ...],
"forecasts": {
"SMA": [105.2, 107.8, ...],
"WMA": [108.5, 110.2, ...],
"ES": [106.1, 108.9, ...]
}
}
```
"models": ["ensemble"],
"include_confidence": true,
"scenario": "realistic"
}
```
### List Models
```
GET /models
```
Returns list of available forecasting models.
## Models Available
1. **SMA** - Simple Moving Average (basic trend analysis)
2. **WMA** - Weighted Moving Average (recent data weighted more)
3. **ES** - Exponential Smoothing (seasonal trend analysis)
4. **ARIMA** - Statistical time series model
5. **CatBoost** - Machine learning model (gradient boosting)
## Usage
### Local Development
1. Install dependencies:
```bash
python run.py install
```
2. Run the service:
```bash
python run.py run
```
The API will be available at `http://localhost:7860`
### API Documentation
Once running, visit `http://localhost:7860/docs` for interactive API documentation.
### Testing
Test the service with the built-in test suite:
```bash
python run.py test
```
## Deployment
This service is designed to run on Hugging Face Spaces with the following configuration:
- **Runtime**: Python 3.10+
- **Framework**: FastAPI with Uvicorn
- **Container**: Docker-based deployment
- **Port**: 7860
- **GPU**: Not required (CPU-only ML models)
- **Memory**: 2GB minimum recommended
## Training the CatBoost Model
The CatBoost model includes a training script for artificial data:
```bash
python run.py train
```
For production use with real data:
1. Prepare your training dataset with features like:
- Historical demand and prices
- Date-based features (day of week, month, season)
- Lag features (previous days' data)
- Rolling statistics
- Weather data
2. Modify `train_catboost.py` to use your real dataset
3. Train the model and update the implementation in `models/forecast_models.py`
## Project Structure
```
analysis-service/
├── main.py # FastAPI application
├── models/
│ ├── forecast_models.py # Forecasting algorithms
│ └── data_processor.py # Data validation & processing
├── utils/
│ ├── config.py # Configuration management
│ └── logger.py # Logging setup
├── train_catboost.py # Model training script
├── test_service.py # API testing script
├── run.py # Development runner
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
└── README.md # This file
```
## Development Commands
- `python run.py install` - Install dependencies
- `python run.py run` - Start the service
- `python run.py test` - Test the running service
- `python run.py train` - Train CatBoost model
## Error Handling
The API includes comprehensive error handling:
- Input validation with Pydantic models
- Graceful error responses with appropriate HTTP status codes
- Detailed error messages for debugging
- Logging for monitoring and troubleshooting
## Contributing
1. Test locally before committing: `python run.py test`
2. Ensure all tests pass
3. Update documentation as needed
4. Follow the existing code style and structure
## License
MIT License - see LICENSE file for details.
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