https://github.com/robinmillford/tv-movie-recommendations-with-ai
This is a Flask web application that allows users to get recommendations for movies and TV shows with Ai integrated based on genres and specific titles
https://github.com/robinmillford/tv-movie-recommendations-with-ai
ai chatbot flask-application groq llama3 movie-recommendation movie-recommendation-app tmdb-api tv-shows
Last synced: 4 days ago
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This is a Flask web application that allows users to get recommendations for movies and TV shows with Ai integrated based on genres and specific titles
- Host: GitHub
- URL: https://github.com/robinmillford/tv-movie-recommendations-with-ai
- Owner: RobinMillford
- License: agpl-3.0
- Created: 2024-05-28T16:19:15.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-09-13T16:34:38.000Z (2 months ago)
- Last Synced: 2025-10-26T11:41:12.287Z (21 days ago)
- Topics: ai, chatbot, flask-application, groq, llama3, movie-recommendation, movie-recommendation-app, tmdb-api, tv-shows
- Language: HTML
- Homepage: https://tv-movie-recommendations-with-ai.onrender.com
- Size: 85.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Movie and TV Show Recommendation App With AI
A Flask web application for discovering and getting recommendations for movies, TV shows, and actors using TMDb API and an AI-powered chatbot via Groq API.
## Key Features
- **Browse & Search**: Explore movies, TV shows, and actors by genre or through search
- **AI Chatbot**: Get intelligent recommendations using multiple LLM models via Groq API
- **Personalized Experience**: User profiles with watchlists, wishlists, and viewing history
- **Detailed Information**: Comprehensive pages for movies, TV shows, and actors
- **Multiple Authentication Options**: Traditional username/password and Google OAuth
- **Persistent Storage**: PostgreSQL database and Cloudinary for profile images
## Live Demo
Check out the live demo on Render: [https://tv-movie-recommendations-with-ai.onrender.com/](https://tv-movie-recommendations.onrender.com/)
## Getting Started
### Prerequisites
- Python 3.7+
- API keys for TMDb, Groq, and optionally NewsAPI
- PostgreSQL database
- Cloudinary account (free tier available)
- Google OAuth credentials (for Google login)
### Installation
1. Clone the repository:
```bash
git clone https://github.com/RobinMillford/tv-movie-recommendations.git
cd Tv-Movie-Recommendations-with-AI
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Create a `.env` file with your API keys:
```env
SECRET_KEY=your_secret_key
TMDB_API_KEY=your_tmdb_api_key
GROQ_API_KEY=your_groq_api_key
NEWS_API_KEY=your_newsapi_key
DATABASE_URL=postgresql://username:password@localhost:5432/moviehub
CLOUDINARY_CLOUD_NAME=your_cloud_name
CLOUDINARY_API_KEY=your_api_key
CLOUDINARY_API_SECRET=your_api_secret
# Google OAuth (optional)
GOOGLE_CLIENT_ID=your_google_client_id
GOOGLE_CLIENT_SECRET=your_google_client_secret
```
5. Google OAuth Setup:
- Go to the [Google Cloud Console](https://console.cloud.google.com/)
- Create a new project or select an existing one
- Enable the Google+ API
- Go to "Credentials" and create an OAuth 2.0 Client ID
- Set authorized redirect URIs to:
- http://localhost:5000/google/callback (for local development)
- https://yourdomain.com/google/callback (for production)
- Copy the Client ID and Client Secret to your `.env` file
6. Run the application:
```bash
python app.py
```
7. Open `http://127.0.0.1:5000` in your browser.
## Technical Implementation
### Authentication & User Management
- Secure user registration and login with Flask-Login
- Password hashing and validation requirements
- "Remember Me" functionality for 30-day persistent sessions
- Google OAuth integration for easy sign-in
- Profile management with customizable information
### Cloudinary Image Handling
- Profile images uploaded directly to Cloudinary with smart transformations
- 300x300px resizing with face detection
- Automatic quality optimization and format conversion
- CDN delivery for faster loading
- Backward compatibility with existing local images
### AI Chatbot
- Multiple LLM models via Groq API (LLaMA 3.3, LLaMA 3.1, etc.)
- Authentication protection (only for logged-in users)
- TMDb integration for fetching posters and details
- Enhanced handling of new and upcoming releases
- Theme-based recommendations from movie/TV show overviews
### Database Architecture
PostgreSQL database with:
- User accounts and profile information
- Media tracking (watchlist, wishlist, viewing history)
- Many-to-many relationships between users and media items
## Contributing
Submit issues or enhancement requests via GitHub. Pull requests are welcome!
## License
Licensed under the MIT License. See [LICENSE](LICENSE) for details.
## Acknowledgements
- [TMDb API](https://www.themoviedb.org/documentation/api)
- [Groq](https://groq.com/) for AI chatbot functionality
- [Tailwind CSS](https://tailwindcss.com/) for styling
- [PostgreSQL](https://www.postgresql.org/) for database
- [Cloudinary](https://cloudinary.com/) for image handling
- [Google OAuth](https://developers.google.com/identity/protocols/oauth2) for authentication