# Local Testing Guide

Before deploying to Hugging Face Spaces, you may want to test the application locally. This guide provides instructions for local testing.

## Prerequisites

- CUDA-capable GPU with at least 8GB VRAM
- Python 3.8+
- pip or conda package manager

## Steps for Local Testing

1. **Install Dependencies**

```bash
pip install -r image_descriptor_requirements.txt
```

2. **Run in UI Mode**

```bash
python app.py
```

This will start the Gradio UI on http://localhost:7860. You can upload images and test the model.

3. **Run in API-only Mode**

```bash
FLASK_APP=image_descriptor.py flask run --host=0.0.0.0 --port=5000
```

This will start just the Flask API on http://localhost:5000.

4. **Test the Docker Container**

```bash
# Build the container
docker build -t image-descriptor .

# Run the container
docker run -p 7860:7860 --gpus all image-descriptor
```

The application will be available at http://localhost:7860.

## Testing the API

You can test the API using curl:

```bash
# Health check
curl http://localhost:5000/health

# Process an image
curl -X POST -F "image=@data_temp/page_2.png" http://localhost:5000/describe
```

## Troubleshooting

- **GPU Memory Issues**: If you encounter GPU memory errors, try reducing batch sizes or using a smaller model.
- **Model Download Issues**: If the model download fails, try downloading it manually from Hugging Face and place it in the `.cache/huggingface/transformers` directory.
- **Dependencies**: Make sure you have the correct CUDA version installed for your GPU.

## Next Steps

Once you've confirmed the application works locally, you can deploy it to Hugging Face Spaces following the instructions in the main README.md.