Movie Recommendation Chatbot

This project is a Flask-based web application that provides movie recommendations based on user queries. It uses advanced language models and vector-based search to retrieve and rank movie suggestions. The system integrates multiple AI-powered APIs and models to ensure an engaging and accurate chatbot experience.

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Live

You can play with the testbot with the link below. Note that it requires 1 minute to startup after trigger because it goes idle when it is not used. https://cinechatrag.onrender.com/

Features

• Movie Recommendations: Suggests movies based on user input and retrieves relevant titles with detailed metadata.
• Flask-based Web Interface: Simple and user-friendly web interface for interaction.
• AI-Powered Search: Uses OpenAI embeddings and self-query retrievers to find the most relevant movies.
• Customizable Backend: Supports Groq and OpenAI models for dynamic query handling.

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Prerequisites

1. Python 3.12.8 or later: Ensure you have Python installed.
2. API Keys: Set up API keys for OpenAI and Groq.
3. Environment: pip and virtualenv or any Python environment manager.

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Installation

1. Clone the Repository:

   git clone https://github.com/your-repo/movie-recommendation-chatbot.git
   cd movie-recommendation-chatbot

2. Create and Activate a Virtual Environment:

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

3. Install Dependencies:

   pip install -r requirements.txt

4. Set Up Environment Variables:

   • Create a .env file in the project directory.
   • Add the following variables:

     OPENAI_API_KEY=your_openai_api_key
     GROQ_API_KEY=your_groq_api_key
     PORT=5000  # Optional, default is 5000

5. Prepare the Dataset:

   • Place the CineChatCSV_cleaned_new.csv file in the root directory.

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Running the Application

1. Start the Application:

   python app.py

2. Access the Application:

   • Open a web browser and navigate to http://localhost:5000.

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Usage

• Use the chatbot interface to ask for movie recommendations or details.
• The chatbot will return:
  • A list of movie suggestions with metadata (title, year, rating, etc.).
  • A response based on your query generated by the AI model.

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Project Structure

movie-recommendation-chatbot/
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├── app.py                  # Main Flask application
├── requirements.txt        # Python dependencies
├── templates/
│   └── chat.html           # HTML template for the chatbot UI
├── static/
│   └── styles.css          # CSS for UI styling (if any)
├── CineChatCSV_cleaned_new.csv  # Input dataset
├── .env                    # Environment variables file
└── README.md               # Documentation

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Notes

• Ensure the movie_vectorstore5openai directory exists or will be created during runtime for vector storage.
• Use the latest versions of dependencies to avoid compatibility issues.

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Troubleshooting

• Missing API Keys: Verify the .env file contains valid API keys.
• Dependency Issues: Run pip install --upgrade -r requirements.txt to update dependencies.
• Port Conflicts: Modify the PORT variable in the .env file to use a different port.

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Demo Video

Watch the project demo on YouTube