PrimeMovies: AI-Powered Movie Recommender

A high-fidelity movie discovery platform built with an Amazon Prime Video–inspired interface. It combines a FastAPI-based machine learning backend with a Streamlit frontend to deliver an interactive movie recommendation experience.

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Features

• Prime Video–Style UI

• Custom dark navy theme inspired by modern streaming platforms

• Glassmorphism effects and hover-scale interactions

• Clean, distraction-free browsing experience

• AI Recommendation Engine

  • Hybrid recommendation system using:
  • TF-IDF content-based filtering
  • Genre-based clustering
  • Similarity-driven movie recommendations

• Dynamic Hero Banner

  • Displays trending movies dynamically
  • High-resolution backdrop images
  • IMDb rating integration

• Real-Time Search

  • Autocomplete suggestions
  • Keyword-based search functionality
  • Integrated with TMDB API

• Navigation

  • Horizontal navigation bar
  • Streamlined layout without sidebar dependency
  • Web-app-like user experience

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Tech Stack

• Frontend: Streamlit (Python)
• Backend: FastAPI (Python)
• Machine Learning: Scikit-learn, Pandas, NumPy
• Data Source: TMDB API, IMDb dataset

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Project Structure

├── app.py                  # Streamlit frontend
├── main.py                 # FastAPI backend
├── requirements.txt        # Project dependencies
├── data/
│   ├── df.pkl              # Processed dataset
│   ├── tfidf_matrix.pkl    # Feature vectors
│   └── imdb_movies.csv     # Raw dataset

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How to Run Locally

1. Clone the repository

git clone [https://github.com/parinaB/MovieRecommend.git](https://github.com/parinaB/MovieRecommend.git)
cd MovieRecommend

2. Create virtual environment

python -m venv .venv

3. Activate environment:

1. Mac/Linux:

source .venv/bin/activate

2. Windows:

.venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the application

   • Start backend:

    uvicorn main:app --reload

   • Start frontend:

streamlit run app.py

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Future Improvements

• Multilingual support with language selection
• User authentication and personalized watchlists
• Collaborative filtering based on user behavior

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Note

Ensure the FastAPI backend is running at:

http://127.0.0.1:8000

The Streamlit frontend communicates with the backend via the API_BASE configuration.