README.md

---
title: Textilindo AI Assistant
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
sdk_version: "4.0.0"
app_file: app.py
pinned: false
license: mit
short_description: AI Assistant for Textilindo textile company
---

# 🤖 Textilindo AI Assistant

An intelligent AI assistant for Textilindo textile company with advanced training capabilities, built with FastAPI and Hugging Face Transformers.

## ✨ Features

- **Intelligent Chat Interface**: Natural language conversations in Indonesian
- **Company Knowledge**: Trained on Textilindo's specific information
- **Model Training**: Train custom models with your data
- **Fast Response**: Optimized for quick customer service
- **Mobile Friendly**: Responsive web interface
- **API Ready**: RESTful API for integration

## 🚀 Quick Start

### Chat Interface
Visit the main page to start chatting with the AI assistant. Ask questions about:
- Company location and hours
- Product information
- Ordering and shipping
- Sample requests
- Pricing and terms

### Training API

#### Start Training
```bash
curl -X POST "https://harismlnaslm-Textilindo-AI.hf.space/api/train/start" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "distilgpt2",
    "dataset_path": "data/lora_dataset_20250910_145055.jsonl",
    "config_path": "configs/training_config.yaml",
    "max_samples": 10,
    "epochs": 1,
    "batch_size": 1,
    "learning_rate": 5e-5
  }'
```

#### Check Training Status
```bash
curl "https://harismlnaslm-Textilindo-AI.hf.space/api/train/status"
```

#### Test Trained Model
```bash
curl -X POST "https://harismlnaslm-Textilindo-AI.hf.space/api/train/test"
```

#### Get Training Data Info
```bash
curl "https://harismlnaslm-Textilindo-AI.hf.space/api/train/data"
```

#### Check GPU Availability
```bash
curl "https://harismlnaslm-Textilindo-AI.hf.space/api/train/gpu"
```

## 🛠️ Technical Details

### Architecture
- **Framework**: FastAPI with Uvicorn
- **AI Model**: Llama 3.1 8B Instruct (via Hugging Face)
- **Training**: PyTorch with Transformers
- **Language**: Indonesian (Bahasa Indonesia)
- **Deployment**: Docker on Hugging Face Spaces

### API Endpoints

#### Chat Endpoints
- `GET /` - Main chat interface
- `POST /chat` - Chat API endpoint
- `GET /health` - Health check
- `GET /info` - Application information

#### Training Endpoints
- `POST /api/train/start` - Start model training
- `GET /api/train/status` - Check training progress
- `GET /api/train/data` - Get training data information
- `GET /api/train/gpu` - Check GPU availability
- `POST /api/train/test` - Test trained model

### Environment Variables
Set these in your space settings:

```bash
# Required: Hugging Face API Key
HUGGINGFACE_API_KEY=your_api_key_here

# Optional: Model selection
DEFAULT_MODEL=meta-llama/Llama-3.1-8B-Instruct
```

## 📞 Support

For technical issues:
1. Check the `/health` endpoint
2. Review space logs
3. Verify environment variables
4. Test with mock responses

---

*Built with ❤️ for Textilindo customers*