YouTube Content Generator with Hugging Face's Gemma

Overview

This project is a YouTube Content Generator that uses Hugging Face's Gemma model to generate engaging YouTube video titles and scripts. The application is built using Streamlit for a user-friendly interface and leverages LangChain for conversational memory and prompt management.

Features

1. Content Generator

   • Input a topic and generate:
     • A concise YouTube video title.
     • A detailed YouTube video script.
   • Fetch relevant Wikipedia research to enrich the script.

2. Test API Mode

   • Test Hugging Face's Inference API with custom queries to understand the model's response capabilities.

3. Temperature Control

   • Adjustable temperature slider to control creativity levels in model outputs:
     • Low values (e.g., 0.1) for deterministic outputs.
     • High values (e.g., 0.9) for creative outputs.

4. Streamlit Interface

   • Interactive, responsive UI with sidebar navigation and expandable sections for history and research.

Project Structure

project/
├── wiki_researcher.py                  # Main Streamlit application file.
├── utils/
│   ├── api.py             # Functions for interacting with the Hugging Face API.
│   ├── memory.py          # Memory management using LangChain.
│   ├── templates.py       # Prompt templates for generating content.
│   ├── constants.py       # API URL and headers.
│   └── wiki.py            # Wrapper for Wikipedia utilities.
├── requirements.txt        # Python dependencies.
└── README.md               # Project documentation.

Setup

Prerequisites

• Python 3.8 or higher.
• A Hugging Face account with an API token.
• Streamlit installed.

Installation

1. Clone the repository:

   git clone https://github.com/UsamaYousaf/hugging_scripts.git
   cd hugging_scripts

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .streamlit/secrets.toml file to store your Hugging Face API token:

   [secrets]
   auth_token = "your_huggingface_api_token"

Running the Application

1. Start the Streamlit app:

   streamlit run app.py

2. Open the provided URL in your browser to access the application.

Usage

• Content Generator: Enter a topic in the input field to generate titles and scripts. Adjust the temperature slider for creativity control.
• Test API: Use the "Test API" mode to send custom queries to the Hugging Face API.

Technologies Used

• Streamlit: For building the interactive web application.
• LangChain: For managing memory and prompt templates.
• Hugging Face API: To interact with the Gemma model.

Contribution

Contributions are welcome! If you have ideas or suggestions, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.

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For more details, check the GitHub Repository.