readme update

README.md

@@ -1,4 +1,3 @@
-# IndicTrans2 Language Server
 ---
 title: IndicTrans2 Translator API
 emoji: 🌐
@@ -9,9 +8,9 @@ sdk_version: "1.0"
 app_file: app.py
 pinned: false
 tags:
+  - translation
   - fastapi
   - docker
-  - translation
   - indic
   - multilingual
 license: apache-2.0
@@ -19,379 +18,16 @@ license: apache-2.0
 
 # IndicTrans2 Translator API
 
-A multilingual FastAPI translation backend powered by AI4Bharat's IndicTrans2.
-A high-performance FastAPI-based translation server powered by [IndicTrans2](https://github.com/AI4Bharat/IndicTrans2) models for seamless translation between 22 scheduled Indian languages and English.
-
-## 🌟 Features
-
-- **Multilingual Support**: Translate between 12 major Indian languages and English
-- **High-Quality Translations**: Powered by state-of-the-art IndicTrans2 models
-- **REST API**: Simple HTTP API for easy integration
-- **GPU Acceleration**: CUDA support for faster inference
-- **Memory Optimization**: Efficient model loading and GPU memory management
-- **Graceful Shutdown**: Proper cleanup of resources on server termination
-
-## 🚀 Quick Start
-
-### Prerequisites
-
-- Python 3.7+
-- CUDA-compatible GPU (recommended)
-- At least 8GB GPU memory for optimal performance
-
-### Installation
-
-1. **Clone the repository**
-   ```bash
-   git clone https://github.com/AI4Bharat/IndicTrans2
-   cd IndicTrans2
-   ```
-
-2. **Install dependencies**
-   ```bash
-   # Install IndicTrans2 dependencies
-   source install.sh
-   
-   # Install additional requirements for the server
-   pip install fastapi uvicorn torch transformers
-   ```
-
-3. **Install IndicTransToolkit**
-   ```bash
-   cd huggingface_interface/IndicTransToolkit
-   pip install -e .
-   cd ../..
-   ```
-
-4. **Run the server**
-   ```bash
-   python lang_server.py
-   ```
-
-The server will start on `http://0.0.0.0:9000`
+🌐 A multilingual translation API powered by AI4Bharat's IndicTrans2.
+Supports English ↔ Indian languages (Hindi, Kannada, Tamil, etc.)
 
-## 📋 Supported Languages
+## 🛠 API Endpoint
 
-The server supports translation between the following languages:
-
-| Language | Code | Script |
-|----------|------|--------|
-| English | `eng_Latn` | Latin |
-| Bengali | `ben_Beng` | Bengali |
-| Punjabi | `pan_Guru` | Gurmukhi |
-| Assamese | `asm_Beng` | Bengali |
-| Konkani | `gom_Deva` | Devanagari |
-| Gujarati | `guj_Gujr` | Gujarati |
-| Hindi | `hin_Deva` | Devanagari |
-| Kannada | `kan_Knda` | Kannada |
-| Malayalam | `mal_Mlym` | Malayalam |
-| Odia | `ory_Orya` | Odia |
-| Tamil | `tam_Taml` | Tamil |
-| Telugu | `tel_Telu` | Telugu |
-
-## 🔧 API Usage
-
-### Translation Endpoint
-
-**POST** `/language-server/translate`
-
-#### Request Body
+**POST** `/api/v1/translate`
 
 ```json
 {
-    "input_sentence": "Hello, how are you?",
-    "source_lan": "eng_Latn",
-    "target_lang": "hin_Deva"
-}
-```
-
-#### Response
-
-```json
-{
-    "translation": "नमस्ते, आप कैसे हैं?"
-}
-```
-
-#### Error Response
-
-```json
-{
-    "message": "Not a valid dialect"
-}
-```
-
-### Example Usage
-
-#### cURL
-```bash
-curl -X POST "http://localhost:9000/language-server/translate" \
-     -H "Content-Type: application/json" \
-     -d '{
-       "input_sentence": "Good morning!",
-       "source_lan": "eng_Latn",
-       "target_lang": "hin_Deva"
-     }'
-```
-
-#### Python
-```python
-import requests
-
-url = "http://localhost:9000/language-server/translate"
-data = {
-    "input_sentence": "Good morning!",
-    "source_lan": "eng_Latn",
-    "target_lang": "hin_Deva"
-}
-
-response = requests.post(url, json=data)
-print(response.json())
-```
-
-#### JavaScript
-```javascript
-const response = await fetch('http://localhost:9000/language-server/translate', {
-    method: 'POST',
-    headers: {
-        'Content-Type': 'application/json',
-    },
-    body: JSON.stringify({
-        input_sentence: 'Good morning!',
-        source_lan: 'eng_Latn',
-        target_lang: 'hin_Deva'
-    })
-});
-
-const result = await response.json();
-console.log(result);
-```
-
-## ⚡ Performance Optimization
-
-The server is optimized for production use with several performance features:
-
-### Model Configuration
-- **Distilled Models**: Uses 200M parameter distilled models for faster inference
-- **Memory Efficient**: Automatic GPU memory cleanup after each request
-- **Batch Processing**: Supports batch translation for multiple sentences
-
-### Recommended Settings for Speed
-To optimize performance, you can modify the following in `lang_server.py`:
-
-```python
-# Enable quantization for faster inference
-quantization = "4-bit"  # or "8-bit"
-
-# Reduce generation parameters for speed
-max_length = 128        # Reduced from 256
-num_beams = 1          # Greedy decoding for fastest results
-```
-
-## 🏗️ Architecture
-
-The server uses a dual-model architecture:
-
-1. **English → Indic Model**: `ai4bharat/indictrans2-en-indic-dist-200M`
-2. **Indic → English Model**: `ai4bharat/indictrans2-indic-en-dist-200M`
-
-The appropriate model is automatically selected based on the target language:
-- If target is English (`eng_Latn`): Uses Indic→English model
-- If target is any Indic language: Uses English→Indic model
-
-## 🔧 Configuration
-
-### Environment Variables
-
-| Variable | Default | Description |
-|----------|---------|-------------|
-| `BATCH_SIZE` | `4` | Batch size for translation |
-| `DEVICE` | `cuda` | Device for model inference |
-
-### Model Selection
-
-You can switch between different model variants by modifying the checkpoint directories:
-
-```python
-# For base models (higher quality, slower)
-en_indic_ckpt_dir = "ai4bharat/indictrans2-en-indic-1B"
-
-# For distilled models (faster, good quality)
-en_indic_ckpt_dir = "ai4bharat/indictrans2-en-indic-dist-200M"
-```
-
-## 🐳 Docker Deployment
-
-Create a `Dockerfile`:
-
-```dockerfile
-FROM nvidia/cuda:11.8-devel-ubuntu20.04
-
-# Install Python and dependencies
-RUN apt-get update && apt-get install -y python3 python3-pip git
-WORKDIR /app
-
-# Clone and setup IndicTrans2
-RUN git clone https://github.com/AI4Bharat/IndicTrans2 .
-RUN source install.sh
-RUN pip install fastapi uvicorn
-
-# Install IndicTransToolkit
-WORKDIR /app/huggingface_interface/IndicTransToolkit
-RUN pip install -e .
-WORKDIR /app
-
-# Copy your server file
-COPY lang_server.py .
-
-# Expose port
-EXPOSE 9000
-
-# Run the server
-CMD ["python3", "lang_server.py"]
-```
-
-Build and run:
-```bash
-docker build -t indictrans2-server .
-docker run --gpus all -p 9000:9000 indictrans2-server
-```
-
-## 📊 Benchmarks
-
-The IndicTrans2 models achieve state-of-the-art performance on various benchmarks:
-
-- **FLORES-22**: Comprehensive evaluation across 22 languages
-- **IN22**: New benchmark with 1024 sentences across multiple domains
-- **chrF++**: Primary evaluation metric for translation quality
-
-For detailed benchmark results, refer to the [IndicTrans2 paper](https://arxiv.org/abs/2305.16307).
-
-## 🛠️ Development
-
-### Running in Development Mode
-
-```bash
-# Install development dependencies
-pip install fastapi[all] uvicorn[standard]
-
-# Run with auto-reload
-uvicorn lang_server:app --host 0.0.0.0 --port 9000 --reload
-```
-
-### Testing
-
-```bash
-# Test the translation endpoint
-python -c "
-import requests
-response = requests.post('http://localhost:9000/language-server/translate', 
-                        json={'input_sentence': 'Hello', 'source_lan': 'eng_Latn', 'target_lang': 'hin_Deva'})
-print(response.json())
-"
-```
-
-## 🚦 Production Deployment
-
-### Using Gunicorn
-
-```bash
-pip install gunicorn
-
-# Run with multiple workers
-gunicorn lang_server:app -w 4 -k uvicorn.workers.UvicornWorker -b 0.0.0.0:9000
-```
-
-### Nginx Configuration
-
-```nginx
-server {
-    listen 80;
-    server_name your-domain.com;
-
-    location / {
-        proxy_pass http://127.0.0.1:9000;
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-    }
+  "input_sentence": "Hello",
+  "source_lan": "eng_Latn",
+  "target_lang": "hin_Deva"
 }
-```
-
-## 🔍 Troubleshooting
-
-### Common Issues
-
-1. **CUDA Out of Memory**
-   - Reduce `BATCH_SIZE` in the code
-   - Enable quantization: `quantization = "4-bit"`
-   - Use smaller model variant
-
-2. **Slow Performance**
-   - Ensure GPU is available and being used
-   - Enable quantization for faster inference
-   - Reduce `max_length` and `num_beams` parameters
-
-3. **Model Loading Issues**
-   - Check internet connection for model downloading
-   - Verify sufficient disk space (models are ~2GB each)
-   - Ensure proper CUDA installation
-
-### Monitoring
-
-```python
-# Add to your server for monitoring
-import psutil
-import GPUtil
-
-@app.get("/health")
-def health_check():
-    gpu = GPUtil.getGPUs()[0] if GPUtil.getGPUs() else None
-    return {
-        "status": "healthy",
-        "gpu_memory": f"{gpu.memoryUsed}/{gpu.memoryTotal}MB" if gpu else "No GPU",
-        "cpu_percent": psutil.cpu_percent(),
-        "memory_percent": psutil.virtual_memory().percent
-    }
-```
-
-## 📄 License
-
-This project uses the IndicTrans2 models which are released under the MIT License. See the [LICENSE](https://github.com/AI4Bharat/IndicTrans2/blob/main/LICENSE) file for details.
-
-## 🤝 Contributing
-
-1. Fork the repository
-2. Create your feature branch (`git checkout -b feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add some amazing feature'`)
-4. Push to the branch (`git push origin feature/amazing-feature`)
-5. Open a Pull Request
-
-## 🙏 Acknowledgments
-
-- [AI4Bharat](https://ai4bharat.iitm.ac.in/) for the IndicTrans2 models
-- [Hugging Face](https://huggingface.co/) for model hosting and transformers library
-- [FastAPI](https://fastapi.tiangolo.com/) for the excellent web framework
-
-## 📚 Citation
-
-If you use this server in your research, please cite the IndicTrans2 paper:
-
-```bibtex
-@article{gala2023indictrans,
-    title={IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
-    author={Jay Gala and Pranjal A Chitale and A K Raghavan and Varun Gumma and Sumanth Doddapaneni and Aswanth Kumar M and Janki Atul Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M Khapra and Raj Dabre and Anoop Kunchukuttan},
-    journal={Transactions on Machine Learning Research},
-    issn={2835-8856},
-    year={2023},
-    url={https://openreview.net/forum?id=vfT4YuzAYA},
-}
-```
-
-## 🔗 Links
-
-- [IndicTrans2 GitHub](https://github.com/AI4Bharat/IndicTrans2)
-- [IndicTrans2 Paper](https://arxiv.org/abs/2305.16307)
-- [AI4Bharat Website](https://ai4bharat.iitm.ac.in/)
-- [Demo](https://models.ai4bharat.org/#/nmt/v2)
-- [Colab Notebook](https://colab.research.google.com/github/AI4Bharat/IndicTrans2/blob/main/huggingface_interface/colab_inference.ipynb)
-