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138 lines
3.8 KiB
Markdown
138 lines
3.8 KiB
Markdown
# YouTube Summarizer by TCSenpai
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[](https://justforfunnoreally.dev)
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YouTube Summarizer is a Streamlit-based web application that allows users to generate summaries of YouTube videos using AI-powered language models and optionally Whisper for transcription.
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- [YouTube Summarizer by TCSenpai](#youtube-summarizer-by-tcsenpai)
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- [Features](#features)
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- [Installation](#installation)
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- [Usage](#usage)
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- [Global Installation](#global-installation)
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- [Run with the included binary](#run-with-the-included-binary)
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- [Dependencies](#dependencies)
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- [Project Structure](#project-structure)
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- [Contributing](#contributing)
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- [License](#license)
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- [Credits](#credits)
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## Features
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- Supports multiple YouTube frontends (e.g. YouTube, Invidious, etc.)
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- Fetch and cache YouTube video transcripts
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- Summarize video content using Ollama AI models
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- Display video information (title and channel)
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- Customizable Ollama URL and model selection
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- Fallback to Whisper for transcription if no transcript is found
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- Customizable Whisper URL and model selection
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- Optional force Whisper transcription
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## Installation
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1. Clone the repository:
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```
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git clone https://github.com/yourusername/youtube-summarizer.git
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cd youtube-summarizer
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```
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2. Install the required dependencies:
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2a. Using pip:
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```
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pip install -r requirements.txt
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```
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2b. Using conda:
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```
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conda env create -f environment.yml
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```
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Note: You might need to install `conda` first.
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3. Set up environment variables:
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Create a `.env` file in the root directory and add the following:
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```
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YOUTUBE_API_KEY=your_youtube_api_key
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OLLAMA_MODEL=default_model_name
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WHISPER_URL=http://localhost:8000/
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WHISPER_MODEL=Systran/faster-whisper-large-v3
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PASTEBIN_API_KEY=your_pastebin_api_key
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```
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- Note: you can copy the `env.example` file to `.env` and modify the values.
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- Important: the `WHISPER_URL` should point to the whisper server you want to use. You can leave it as it is if you are not planning on using Whisper.
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- Important: the `PASTEBIN_API_KEY` is optional, but if you want to use it, you need to get one from [Pastebin](https://pastebin.com/doc_api).
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## Usage
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1. Run the Streamlit app:
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```
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streamlit run src/main.py
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```
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2. Open your web browser and navigate to the provided local URL (usually `http://localhost:8501`).
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3. Enter a YouTube video URL in the input field.
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4. (Optional) Customize the Ollama URL and select a different AI model.
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5. (Optional) Customize the Whisper URL and select a different Whisper model.
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6. Click the "Summarize" button to generate a summary of the video.
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## Global Installation
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You can install the application globally on your system by running the following command:
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```
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sudo ./install.sh
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```
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This will create a new command `youlama` that you can use to run the application.
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## Run with the included binary
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You can also run the application with the included binary:
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```
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./youlama
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```
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## Dependencies
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- Streamlit
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- Pytube
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- Ollama
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- YouTube Data API
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- Python-dotenv
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- pytubefix
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- Gradio
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## Project Structure
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- `src/main.py`: Main Streamlit application
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- `src/ollama_client.py`: Ollama API client for model interaction
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- `src/video_info.py`: YouTube API integration for video information
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- `src/whisper_module.py`: Whisper API client for transcription
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- `src/yt_audiophile.py`: Audio downloader for YouTube videos
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- `transcript_cache/`: Directory for caching video transcripts
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- `downloads/`: Directory for downloaded audio files, might be empty
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## Contributing
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Contributions are welcome! Please feel free to submit a Pull Request.
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## License
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WTFPL License
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## Credits
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Icon: "https://www.flaticon.com/free-icons/subtitles" by Freepik - Flaticon
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