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📖 Symphonic : A Harmonious Journey Through Literature & Poetry

Welcome to the Symphonic, an interactive platform designed to explore the beauty of words, poetry, and literature. With the power of AI, users can query a vast range of topics in their chosen language and receive expertly crafted responses in prose or poetry, with customizable tones.

🔧 Table of Contents

📚 Project Overview

The Symphonic offers users the ability to:

  • Select from a variety of languages (Odia, Hindi, English).
  • Choose between prose and poetry formats.
  • Set the tone of the response (Neutral, Formal, Casual).
  • Receive responses to user queries based on preloaded literary works or custom topics.

This project integrates LangChain for dynamic prompt generation and the Gemini API to generate rich literary responses.

🚀 Features

  • Customizable Response Style: Prose or poetry with tone control.
  • Multi-language Support: Odia, Hindi, and English.
  • Famous Works: Preloaded literary works for quick access.
  • Interactive Interface: Built with Streamlit for a smooth user experience.

🛠 Installation

Follow these steps to set up the project on your local machine:

  1. Clone the repository:

    git clone https://github.com/CYBERBULL123/Symphonic.git
cd Symphonic

  2. Create a virtual environment (optional but recommended):

    python -m venv venv

  3. Activate the virtual environment:

    • On Windows: 
      .\venv\Scripts\activate
    • On macOS/Linux: 
      source venv/bin/activate

  4. Install dependencies:

    pip install -r requirements.txt

⚙️ Configuration

To use the API keys securely and configure the environment, follow the steps below:

Setting Up API Keys

  1. Create a secrets.toml file:

    • After cloning the repository, navigate to the .streamlit folder.

    • Copy the secrets.toml.sample file and rename it to secrets.toml.

      cp .streamlit/secrets.toml.sample .streamlit/secrets.toml

  2. Edit the secrets.toml file:

    • Open the secrets.toml file in a text editor and replace the placeholders with your actual API keys and secrets.

    Example secrets.toml file:

    # secrets.toml.sample

 # Sample API Key for Gemini (users should replace this with their actual API key)

 [api_keys]
 gemini = "your-gemini-api-key-here"

  3. Ensure that the .gitignore file is configured: The .gitignore file should already include the following line to ignore the secrets.toml file from being committed to version control.

    # Ignore the secrets file
.streamlit/secrets.toml

  4. Load Secrets in Your App: The secrets.toml file will automatically be loaded by Streamlit. You can access these secrets in your app as shown

🖥 Usage

  1. Running the app:

    • After setting up your environment and secrets, run the app using the following command: 
      streamlit run app.py

  2. Interacting with the app:

    • Language Selection: Choose between Odia, Hindi, or English.
    • Response Style: Select between Prose or Poetry.
    • Tone: Set the tone of the response (Neutral, Formal, Casual).
    • Query Input: Enter your query (e.g., a theme, topic, or famous work).

    The app will generate a literary response based on your inputs, returning either prose or poetry with the selected tone.

🤝 Contributing

We welcome contributions to this project! If you'd like to contribute, follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix.
  3. Make your changes and commit them.
  4. Push your changes to your forked repository.
  5. Open a pull request with a clear description of your changes.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Project Structure

Here's the directory structure of the project:

Symphonic
│
├── app.py                # Main Streamlit app
├── requirements.txt      # Python dependencies
├── .gitignore            # Git ignore configuration
├── .streamlit/
│   └── secrets.toml.sample # Sample secrets file
└── backend/
    ├── gemini_api.py     # API integration for Gemini
    ├── langchain.py      # LangChain prompt generation
    └── __pycache__/      # Compiled Python files