🎬 CinemaPulse: AI-Powered Movie Discovery
CinemaPulse is a full-stack movie recommendation engine that combines modern web technologies with machine learning to provide users with personalized movie suggestions. By leveraging TF-IDF Content-Based Filtering and the TMDB API, CinemaPulse offers a premium "Netflix-style" experience for exploring trending titles and finding similar movies based on plot descriptions.
LIVE-https://moviereccsystem-jakyvjy8tsfkanodvixqhi.streamlit.app/
🌟 Key Features
Premium Dark UI: A modern, responsive "Netflix-style" dashboard built with Streamlit.
Intelligent Search: Real-time keyword search with auto-suggestions powered by TMDB.
Dual-Engine Recommendations:
Content-Based (Local): Uses TF-IDF and Cosine Similarity on a local dataset to find movies with similar plot themes.
Genre-Based (Live): Fetches real-time recommendations from TMDB based on movie genres.
High-Performance Backend: FastAPI-powered REST API with asynchronous request handling.
Live Metadata: Fetches high-quality posters, backdrops, ratings, and overviews in real-time.
🏗️ Technical Architecture
The system is split into three main layers:
Frontend: Streamlit provides the interactive UI, managing session states and routing.
API Layer: FastAPI acts as the bridge, serving local ML data and proxying requests to external APIs.
Data Engine: A Python-based ML pipeline that pre-processes movie data and generates TF-IDF matrices stored as pickle files.
🚀 Getting Started
- Prerequisites
Python 3.9+
A TMDB API Key (Get one for free at themoviedb.org)
- Installation Clone the repository:
Bash git clone https://github.com/adarsh005599/Movie_Rec_System.git cd Movie_Rec_System Install dependencies:
Bash pip install -r requirements.txt 3. Environment Setup Create a .env file in the root directory and add your API key:
Code snippet TMDB_API_KEY=your_api_key_here 4. Running the Application You need to run the Backend and the Frontend simultaneously in two separate terminals.
Terminal 1 (FastAPI Backend):
Bash python main.py Terminal 2 (Streamlit Frontend):
Bash streamlit run app.py 📂 Project Structure Plaintext ├── app.py # Streamlit Frontend (UI/UX)
├── main.py # FastAPI Backend (REST API)
├── movie_recc.ipynb # ML Pipeline / Model training
├── .env # Environment variables (private)
├── requirements.txt # Python dependencies
├── df.pkl # Processed movie dataframe
├── tfidf_matrix.pkl # Pre-computed similarity matrix
└── movies_metadata.csv # Raw dataset 🛠️ Built With FastAPI - High-performance web framework.
Streamlit - For the interactive frontend.
Pandas/NumPy - Data manipulation.
Scikit-Learn - TF-IDF Vectorization and similarity metrics.
HTTPX - Asynchronous HTTP requests.