YouTube Transcript Summarizer built with Streamlit and IBM WatsonX Mistral AI Model

This project is a YouTube Transcript Summarizer built with Streamlit and IBM WatsonX Mistral AI Model. The application extracts the transcript from a given YouTube video URL, chunks the transcript into manageable pieces, and generates a concise summary.

Prerequisites

• Python 3.6 or later
• IBM WatsonX API credentials

Installation

0. Get watsonX API credentials

   We need an API Key and project ID to access the models present in watsonX. To get that, please follow below instructions,

   Get a WML API key from https://cloud.ibm.com/iam/apikeys

   Create a watsonX project in https://dataplatform.cloud.ibm.com/wx/home and associate it with WML service on cloud (Watson Machine Learning). Also get the project id of this project.

1. Clone the repository:

   git clone https://github.com/yourusername/youtube-transcript-summarizer.git
   cd youtube-transcript-summarizer

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required libraries:

   pip install streamlit re youtube_transcript_api pysbd python-dotenv ibm-watsonx

4. Create a .env file in the project directory and add your WatsonX API key and project ID:

   WATSONX_API_KEY=your_watsonx_api_key
   PROJECT_ID=your_project_id

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Open your web browser and go to http://localhost:8501.

3. Enter the YouTube URL of the video you want to summarize and click "Submit".

Project Structure

• app.py: The main application file containing the Streamlit UI and logic for summarizing YouTube transcripts.
• .env: Environment file containing API credentials (not included in the repository, must be created by the user).

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License.