🎬 Movie Recommendation System

A Netflix / IMDb–style Movie Recommendation Web App built with Python & Streamlit, powered by the TMDB API and a content-based recommendation engine.

🔗 Live Demo:
https://movie-recommendation-system--gopiprajapati20.replit.app

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👤 What Users Can Do

• Discover trending movies
• Select a movie and get similar recommendations
• Browse movies by category and genre
• Navigate smoothly using page-based pagination

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🚀 Features

• 🔥 Trending Movies
  • Weekly trending movies from TMDB
• 🎯 Content-Based Recommendations
  • Based on genres, overview, cast, and crew
• 🎬 Movie Selection via Dropdown
  • Clean UI (no search clutter)
• 🎞 Browse by Category & Genre
  • Hollywood, Bollywood, K-Drama
  • Action, Comedy, Romance
  • Horror, Thriller, Sci-Fi
  • Drama, Animation
• 📄 Production-Correct Pagination
  • Fixed movies per page
  • Next / Previous navigation
  • No duplicates
• 🖼 High-Quality Posters
  • Fetched directly from TMDB
• ⚡ Fast & Interactive UI
  • Cached API calls & data processing

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🖼️ Screenshots

Home Page

Trending movies and main layout:

Find a Movie & Recommendations

Select a movie from the dropdown and get recommendations:

Browse by Category

Category browsing with pagination:

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🧠 Recommendation Logic

This system uses content-based filtering:

• Movie overview
• Genres
• Cast
• Crew

All features are combined into a single representation, and
cosine similarity is used to find movies most similar to the selected one.

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🛠 Tech Stack

• Python 3
• Streamlit
• Pandas
• Scikit-learn
• TMDB API
• Requests

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📂 Project Structure

Movie-Recommendation-System/
│
├── app.py
├── requirements.txt
├── README.md
├── .gitignore
|---pages
    |__1_dashboard.py
├── data/
│   ├── tmdb_5000_movies.csv
│   └── tmdb_5000_credits.csv
│
├── src/
│   ├── preprocess.py
│   ├── recommender.py
│   ├── cache.py
│   ├── tmdb_api.py
│   └── ui_components.py
│
└── screenshots/
    ├── Trending_now.png
    ├── recommendation.png
    ├── browse_by_category1.png
    └── browse_by_category2.png

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🏗️ System Architecture

C4Container
    title Movie Recommendation System – Architecture

    %% --- Users ---
    Person(user, "User", "Browses movies and gets recommendations")

    %% --- Frontend / UI ---
    Container(ui, "Streamlit UI (app.py)", "Streamlit", "Handles layout, user input, navigation, pagination")

    Container(components, "UI Components", "Python", "Reusable movie cards, buttons, trailers (ui_components.py)")

    %% --- Application Layer ---
    Container(logic, "Application Logic", "Python", "Coordinates recommendation flow, category browsing, session state")

    Container(cache, "Caching Layer", "Streamlit Cache", "Caches API calls and heavy computations (cache.py)")

    %% --- Recommendation Engine ---
    Container(engine, "Recommendation Engine", "Scikit-learn", "Content-based filtering using cosine similarity")

    %% --- Data & External Services ---
    ContainerDb(csv, "Local Movie Dataset", "CSV", "TMDB 5000 Movies & Credits dataset")

    System_Ext(tmdb, "TMDB API", "Provides posters, trending movies, trailers, metadata")

    %% --- Relationships ---
    Rel(user, ui, "Interacts with")
    Rel(ui, components, "Renders UI using")
    Rel(ui, logic, "Triggers actions")
    Rel(logic, cache, "Uses cached results")
    Rel(logic, engine, "Requests recommendations")
    Rel(engine, csv, "Loads & processes")
    Rel(logic, tmdb, "Fetches movies, posters, trailers")
    Rel(cache, tmdb, "Caches API responses")

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⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/your-username/movie-recommendation-system.git
cd movie-recommendation-system

2️⃣ Create virtual environment (optional but recommended)

python -m venv venv
source venv/bin/activate   # macOS/Linux
venv\Scripts\activate      # Windows

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Set TMDB API Key

Create a TMDB API key from 👉 https://www.themoviedb.org/

Then set it as an environment variable:

macOS/Linux

export TMDB_API_KEY="your_api_key_here"

Windows

set TMDB_API_KEY=your_api_key_here

▶️ Run the App

streamlit run app.py

The app will open automatically in your browser 🚀

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📜 License

This project is licensed under the MIT License.
You are free to use, modify, and distribute this project with attribution.

See the LICENSE file for details.