Movie Name Guessing using Plot with RAG

This project is a Retrieval-Augmented Generation (RAG) application that can guess a movie's name based on a given plot summary. The application is built using LangChain, Google Palm LLM, CSVLoader, RetrievalQA, Google Palm Embeddings, and FAISS, and it is deployed on Streamlit for a user-friendly interface.

Features

• Plot-based Movie Guessing: Provide a plot summary, and the application will guess the corresponding movie title.
• AI-Powered Retrieval: The application uses advanced AI models and embedding techniques to find the most relevant matches quickly.
• Interactive Streamlit Interface: The application is deployed on Streamlit, offering a simple and interactive user experience.

Technologies Used

• LangChain: A framework for building AI applications that combine large language models (LLMs) with custom data sources.
• Google Palm LLM: A powerful large language model that processes text and provides accurate interpretations.
• CSVLoader: Loads the movie database from CSV files.
• RetrievalQA: Retrieves relevant data from the loaded dataset for question-answering tasks.
• Google Palm Embeddings: Creates efficient embeddings for text data.
• FAISS: A library for fast similarity search and clustering of dense vectors.

Getting Started

To run the application, you'll need to follow these steps:

1. Clone the Repository

   git clone https://github.com/2003HARSH/AI-Guesses-the-movie.git
   cd AI-Guesses-the-movie

2. Install Dependencies

   pip install -r requirements.txt

3. Set Up the Environment Ensure you have the necessary environment variables configured, such as API keys for Google Palm LLM and any other services used.

4. Start the Streamlit App

   streamlit run app.py

Usage

1. Enter a Plot Summary Input the plot summary for a movie into the provided text box.

2. Generate Movie Name Click the "Guess Movie" button to get the predicted movie name based on the provided plot.

3. Review Results The application will return the most likely movie name and additional information, if available.

Contributions

Contributions are welcome! If you have ideas for improvements or new features, feel free to submit a pull request or open an issue. Please ensure your code follows the project's coding standards and is well-documented.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

Thanks to the developers of LangChain, Google Palm, FAISS, and Streamlit for providing the tools and libraries that made this project possible.