This project is an MVP (Minimum Viable Product) for Logos AI, a Telegram bot that receives audio files (in MP3 format), transcribes the content, and returns a summary with keywords using the OpenAI API.

• Receive audio files in MP3 format via Telegram.
• Transcribe the audio content using Whisper.
• Summarize the transcribed text and extract keywords using the OpenAI API.
• Return the summary directly to the user on Telegram.

• Python 3.9+
• Telegram Bot API (via python-telegram-bot)
• OpenAI API (openai)
• Whisper (OpenAI) for transcription
• dotenv for managing environment variables

logos_ai_mvp/
├── accesses/
│   └── secrets.env           # API keys for Telegram and OpenAI
├── audio_processing/
│   └── transcriber.py        # Audio transcription function
├── summarization/
│   └── summarizer.py         # Function to summarize the transcribed text
├── utils/
│   └── downloader.py         # Function to download audio files from Telegram
├── audio_files/              # Directory where received audio files are stored
├── main.py                   # Main file to run the bot
├── README.md                 # This documentation file
├── requirements.txt          # Project dependencies

• Python 3.9+
• FFmpeg (required for transcription with Whisper)
  • On Windows: Download here and add to PATH.
  • On Linux: sudo apt-get install ffmpeg
  • On Mac: brew install ffmpeg

git clone https://github.com/your-username/logos_ai_mvp.git
cd logos_ai_mvp

python -m venv .venv
source .venv/bin/activate  # Linux/Mac
.venv\Scripts\activate     # Windows

pip install -r requirements.txt

Create the accesses/secrets.env file with the following variables:

OPENAI_API_KEY=your_openai_api_key
TELEGRAM_TOKEN=your_telegram_token

python main.py

1. On Telegram, send the /start command to the bot.
2. Send an audio file in MP3 format.
3. The bot will return the transcribed text, the summary, and the keywords.

Do not share your API keys publicly. The .env file is configured to protect these sensitive details.

• Add support for different languages during transcription.
• Implement options for short, medium, and long summaries.
• Persist transcriptions and summaries in a local database.

Contributions are welcome! Feel free to open issues and submit pull requests.

This project is licensed under the MIT License.