🎬 Find My Film

A smart movie discovery app that helps you find the perfect film based on your preferences, genres, and AI-powered suggestions.

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📸 Screenshots

Movie Dashboard

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⚙️ Technical Highlights

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📝 Developer Notes

• Backend entrypoint: src/backend/server.js
• API + DB access helpers: src/backend/movieApi.js and src/backend/db.js
• Main UI logic: frontend/script.js
• Preferences flow: src/backend/routes/preferences.js and frontend/script.js

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🚀 Overview

Find My Film is a full-stack movie recommendation platform built with a simple frontend and a Node/Express backend. It combines TMDb data with an LLM-powered suggestion layer and stores user watchlists and preferences in SQLite.

Users can browse and search movies, pick preferred genres, receive AI suggestions, and maintain a personal watchlist.

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✨ Features

• AI-powered movie suggestions (LLM + TMDb)
• Search and filter by title, genre, year, actor, rating
• Persistent watchlist with duplicate prevention
• Preferences (favorite genres)
• Test coverage for key routes (Vitest)

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🧰 Tech Stack

Layer	Tech
Frontend	HTML, CSS, JavaScript
Backend	Node.js, Express
Database	SQLite
AI	LLM (via API) + TMDb
Testing	Vitest

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🏗️ Project Structure

project/
# front-end
├── frontend/
│   ├── index.html
│   ├── script.js
│   └── styles.css
# source
├── src/
│   ├── backend/
│   │   ├── routes/
│   │   │   ├── llmRoutes.js
│   │   │   ├── movies.js
│   │   │   └── watchlist.js
│   │   ├── utils/
│   │   │   └── genreMap.js
│   │   ├── db.js
│   │   ├── llm.js
│   │   ├── movieApi.js
│   │   └── server.js
│   └── database/
│       ├── movies_recommendation.db
│       └── setup.sql
# tests and misc
├── tests/
│   ├── example.test.js
│   ├── llmroutes.test.js
│   ├── movieroute.test.js
│   └── server.test.js
├── .env.example
├── package.json
└── README.md

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▶️ Quick Start (Local)

1. Install dependencies

npm install

2. Create a .env file (see .env.example) and set required API keys

3. Initialize the SQLite database (run once):

# from project root
sqlite3 src/backend/database/movies_recommendation.db < src/backend/database/setup.sql

4. Start the backend

npm start

5. Open the frontend in your browser:

http://localhost:3000

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🔑 Environment

Create a .env file in the project root with the following values:

# TMDb API key
TMDB_API_KEY=your_tmdb_api_key

# LLM/API key for suggestions
LLM_API_KEY=your_llm_api_key

# Port (optional)
PORT=3000

You can copy .env.example to .env and fill the values.

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🗄️ Database

• The project uses SQLite. The database file is src/backend/database/movies_recommendation.db.
• Use src/backend/database/setup.sql to create required tables.

If you need to reset the DB locally, remove the .db file and re-run the sqlite3 command above.

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🧪 Tests

Run unit tests with:

npm test

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👥 Contributors

👨‍💻 Team Contributions Dagim Group contributions (with Salem): Worked together on the HTML structure Collaborated on the CSS styling for the frontend Individual contributions: Set up the Express server Implemented the basic watchlist router Configured the GitHub Actions CI pipeline Organized the project folder structure Wrote and updated the README Created the .env.example file Added the initial test file Salem Group contributions (with Dagim): Worked together on the HTML structure Collaborated on the CSS styling for the frontend Individual contributions: Assisted with building the frontend interface Implemented several movie routes Fetched movie details using movie ID Added features to display movie information from IMDb Built frontend functionality for searching movies and actors Implemented the watchlist display feature Implemented the "Pick Genres" feature Atiqa Individual contributions: Watchlist feature (end-to-end): Created the watchlist table in setup.sql Implemented watchlist functions in db.js Implemented watchlist UI functionality LLM feature (end-to-end): Developed llm.js for core LLM AI logic Contributed to movieApi.js as TMDb wrapper Implemented routes/llmRoutes.js endpoint handler Updated routes/movies.js for movie display routes Added UI logic for suggestions in script.js Group contributions (with Muna): Planned the app and proposed the watchlist feature Resolved merge conflicts and reviewed PRs Collaborated on preferences and recommendation features Co-presented the project Muna Individual contributions: Implemented all TMDb API functions in movieApi.js (search, filter, recommend) Organized API logic and data processing Built the preferences feature end-to-end Implemented UI logic in script.js to render and save preferences Created backend routes in routes/preferences.js Added CSS styling for the preferences form Group contributions (with Atiqa): Set up db.js and the database structure together Built shared database functions Collaborated on preferences and recommendation features Helped resolve merge conflicts and reviewed PRs

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License

This project is available under the MIT License.