README.md

---
title: KARTE - 音声カルテ分析
emoji: 🎯
colorFrom: red
colorTo: pink
sdk: docker
app_port: 7860
pinned: false
---

# KARTE - Audio Analysis for Medical Records

KARTE is a powerful application that generates medical records and customer service analysis from audio data. It combines advanced speech recognition with natural language processing to provide comprehensive insights.

## Features

- 🎤 Audio Transcription: Converts audio files to text using OpenAI's Whisper model
- 📊 Style Analysis: Evaluates customer service style and communication quality
- 🔄 Flow Analysis: Analyzes conversation flow and structure
- 📝 Medical Record Generation: Creates structured medical records from conversations
- 🔒 Secure Authentication: Basic auth protection for sensitive data
- 📥 Export Functionality: Download analysis reports in JSON format

## Technology Stack

- Streamlit: Web application framework
- OpenAI Whisper: Speech-to-text transcription
- Groq: Large language model for analysis
- Python: Core programming language
- Docker: Containerization

## Environment Variables

Required environment variables:
- `BASIC_AUTH_USERNAME`: Username for basic authentication
- `BASIC_AUTH_PASSWORD`: Password for basic authentication
- `OPENAI_API_KEY`: OpenAI API key for transcription
- `GROQ_API_KEY`: Groq API key for analysis

## Usage

1. Access the application through Hugging Face Spaces
2. Log in using the provided credentials
3. Upload an audio file (supported formats: MP3, WAV, M4A)
4. Wait for transcription and analysis
5. Review the generated insights and medical record
6. Download the complete analysis report

## Deploying to Hugging Face Spaces

### Option 1: Using Streamlit SDK (Recommended)

1. Make sure your README.md has the correct metadata at the top:
   ```
   ---
   title: KARTE - 音声カルテ分析
   emoji: 🎯
   colorFrom: red
   colorTo: pink
   sdk: streamlit
   sdk_version: 1.32.2
   app_file: main.py
   pinned: false
   ---
   ```

2. Set up your environment variables in Hugging Face Spaces:
   - Go to your Space settings
   - Add the following secrets:
     - `OPENAI_API_KEY`
     - `GROQ_API_KEY`
     - `BASIC_AUTH_USERNAME`
     - `BASIC_AUTH_PASSWORD`

3. Push your code to the Hugging Face repository:
   ```bash
   git add .
   git commit -m "Deploy Streamlit app"
   git push
   ```

### Option 2: Using Docker (Advanced)

1. Update your README.md metadata to use Docker:
   ```
   ---
   title: KARTE - 音声カルテ分析
   emoji: 🎯
   colorFrom: red
   colorTo: pink
   sdk: docker
   pinned: false
   ---
   ```

2. Make sure your Dockerfile is properly configured:
   - Uses port 7860
   - Sets up a non-root user
   - Installs all dependencies including ffmpeg
   - Properly sets environment variables

3. Set up your environment variables in Hugging Face Spaces as in Option 1

4. Push your code to the Hugging Face repository:
   ```bash
   git add .
   git commit -m "Deploy Docker app"
   git push
   ```

### Troubleshooting

If you encounter issues with deployment:

1. Start with the diagnostic app:
   - Change the Dockerfile to use `test_app.py` instead of `main.py`
   - This will help identify environment and dependency issues

2. Check the Spaces logs for errors:
   - Go to your Space → "Logs" tab
   - Look for any error messages related to missing dependencies or environment variables

3. Common issues:
   - Missing environment variables
   - Missing system dependencies (like ffmpeg)
   - Incorrect port configuration
   - Memory or resource limitations

## Development

To run locally:

1. Clone the repository
2. Install dependencies: `pip install -r requirements.txt`
3. Set up environment variables
4. Run the application: `streamlit run main.py`

## License

This project is licensed under the MIT License.