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README.md

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----
-title: Text Correction API
-emoji: 🔧
-colorFrom: blue
-colorTo: purple
-sdk: docker
-sdk_version: 1.0.0
-app_file: app.py
-pinned: false
----
-
-# Text Correction API Server
-
-This is the server-side API for text correction using your trained model.
-
-## 📝 License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-## 🚀 Setup
-
-### 1. Install Dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-### 2. Set Model Path
-
-Make sure your trained model is in the `gpu_base_model2` directory, or set the `MODEL_PATH` environment variable:
-
-```bash
-export MODEL_PATH="./gpu_base_model2"
-```
-
-### 3. Run the Server
-
-#### Local Development:
-```bash
-python main.py
-```
-
-Or using uvicorn directly:
-```bash
-uvicorn main:app --reload --host 0.0.0.0 --port 8000
-```
-
-The API will be available at: `http://localhost:8000`
-
-### 4. Test the API
-
-```bash
-# Health check
-curl http://localhost:8000/health
-
-# Correct text
-curl -X POST http://localhost:8000/correct \
-  -H "Content-Type: application/json" \
-  -d '{"text": "helo wrld this is a test"}'
-```
-
-## 📡 API Endpoints
-
-### GET `/health`
-Check if the API and model are ready.
-
-**Response:**
-```json
-{
-  "status": "healthy",
-  "model_loaded": true,
-  "device": "cuda"
-}
-```
-
-### POST `/correct`
-Correct text using the trained model.
-
-**Request:**
-```json
-{
-  "text": "helo wrld this is a test"
-}
-```
-
-**Response:**
-```json
-{
-  "corrected_text": "hello world this is a test",
-  "processing_time": 0.45
-}
-```
-
-## 🌐 Deployment Options
-
-### Option 1: Hugging Face Spaces (Free) - Recommended
-
-1. **Create a new Space** at https://huggingface.co/new-space
-   - Name: `your-username-text-correction`
-   - SDK: Docker
-   - License: **MIT** (or Apache 2.0)
-   - Click "Create Space"
-
-2. **Upload files:**
-   - Upload all files from this directory
-   - Upload your `gpu_base_model2/` folder
-
-3. **Your API will be live at:**
-   ```
-   https://your-username-text-correction.hf.space/correct
-   ```
-
-### Option 2: Render (Free tier available)
-
-1. Create a new Web Service
-2. Connect your GitHub repository
-3. Set build command: `pip install -r requirements.txt`
-4. Set start command: `uvicorn main:app --host 0.0.0.0 --port $PORT`
-5. Deploy
-
-### Option 3: Railway (Free tier available)
-
-1. Create a new project
-2. Add a service from GitHub
-3. Railway will auto-detect the Python app
-4. Set environment variable `MODEL_PATH` if needed
-5. Deploy
-
-### Option 4: AWS/GCP/Azure
-For production deployments with more control.
-
-## ⚙️ Environment Variables
-
-- `MODEL_PATH`: Path to your trained model (default: `./gpu_base_model2`)
-- `PORT`: Server port (default: `8000`)
-
-## 🔒 Security Notes
-
-⚠️ **Important for Production:**
-1. Add authentication to your API endpoints
-2. Set proper CORS origins (not `*`)
-3. Add rate limiting
-4. Use HTTPS
-5. Keep your API key secure
-
-## 🐛 Troubleshooting
-
-### Model not loading
-- Check that `gpu_base_model2` directory exists
-- Verify all model files are present
-- Check console logs for specific errors
-
-### Out of memory
-- Reduce `max_length` in the generate function
-- Use smaller batch sizes
-- Consider using CPU instead of GPU
-
-### Slow inference
-- Use GPU if available
-- Reduce `num_beams` parameter
-- Use quantization for faster inference
-
-## 📊 Usage
-
-This API is designed to be called from an iOS app for correcting OCR text. The typical flow is:
-
-1. User takes/selects an image
-2. OCR extracts text from the image
-3. Extracted text is sent to this API
-4. API corrects the text using the trained model
-5. Corrected text is returned to the app
-
-## 🤝 Contributing
-
-This is a private project for text correction. For questions or issues, please contact the project owner.