Personal Movie Assistant

A chatbot application designed to assist users in exploring and discovering movies. This project uses Python with a PyQt5-based graphical user interface and advanced language modeling via the Ollama LLM (Llama 3.1) for generating personalized responses.

Features

• Movie Recommendations: Offers personalized movie recommendations based on user preferences, including genre, favorite actors, and desired year range.
• Detailed Movie Information: Fetches and displays detailed information about movies, including titles, release dates, overviews, ratings, and more, using The Movie Database (TMDb) API.
• Interactive GUI: A user-friendly interface built with PyQt5, featuring a chat-based interaction system.
• Conversation History: Maintains a history of user interactions to provide context-aware responses.
• Advanced Language Model: Utilizes the Ollama LLM (Llama 3.1) to generate detailed and contextually accurate responses to user queries.

Prerequisites

• Python: Ensure Python is installed on your machine.
• PyQt5: For GUI components.
• Ollama: Install the Ollama platform on your machine.
• Llama 3.1: Download and configure the Llama 3.1 model.
• TMDb API Key: Obtain a key from TMDb and set it in your environment variables as TMDB_API_KEY.

Installation

1. Clone the Repository:

   git clone https://github.com/asultanli/Personal-Movie-Assistant.git
   cd Personal-Movie-Assistant

2. Install Python Packages:

   pip install -r requirements.txt

3. Setup TMDb API Key:

   • Obtain an API key from TMDb.
   • Set it in your environment variables as TMDB_API_KEY.

4. Install Ollama and Llama 3.1:

   • Follow the installation instructions for Ollama and download the Llama 3.1 model from the official sources.
   • Ensure they are correctly configured and accessible on your system.

Running the Application

1. Start the Chatbot:

   • Run the application using the command:

     python3 main.py

2. Interacting with the Chatbot:

   • Use the GUI to enter your queries about movies or ask for recommendations.
   • The chatbot will provide responses based on the integrated API data and user preferences, leveraging the advanced capabilities of the Ollama LLM.