MOVIIE — Movie Recommendation CLI

MOVIIE is a Python-based command-line application that recommends movies based on a content-based filtering approach.
It uses TF‑IDF on movie overviews and cosine similarity to find movies similar to a title you enter.

The project includes an interactive, styled terminal UI (built with Rich) and works with the included TMDB 5000 dataset CSV files.

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Features

• Search a movie title and get top recommendations
• Random movie pick (with option to fetch recommendations for it)
• Browse the top 20 movies from the dataset
• Clean, interactive CLI interface using Rich

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Tech Stack

• Python 3
• pandas for dataset handling
• scikit-learn for TF‑IDF vectorization + cosine similarity
• Rich for terminal UI (menus, tables, panels)

Dependencies (as pinned in requirements.txt):

• joblib, numpy, pandas, scikit-learn, scipy, threadpoolctl, python-dateutil, pytz, six, tzdata

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Project Structure

Moviie/
├─ main.py
├─ requirements.txt
├─ cli/
│  └─ interface.py
├─ recommender/
│  └─ content_based.py
└─ data/
   ├─ tmdb_5000_movies.csv
   └─ tmdb_5000_credits.csv

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Installation

1. Clone the repository

   git clone https://github.com/dev0jha/Moviie.git
   cd Moviie

2. (Recommended) Create and activate a virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

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Usage

Run the CLI app:

python main.py

You’ll see a menu with options to:

1. Search & get recommendations
2. Random movie pick
3. Browse top movies
4. Exit

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How Recommendations Work (Brief)

The recommender loads movie data from:

• data/tmdb_5000_movies.csv

Then it:

1. Uses the overview text of each movie
2. Builds TF‑IDF vectors (TfidfVectorizer(stop_words="english"))
3. Computes similarity with cosine similarity
4. Returns the most similar titles to the movie you entered

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Dataset

This repo includes:

• data/tmdb_5000_movies.csv
• data/tmdb_5000_credits.csv

These files are used as the local dataset source for recommendations (no API key required).

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Notes / Troubleshooting

• If a movie title isn’t found, try adjusting spelling/capitalization to match dataset titles.
• The quality of recommendations depends on the richness of the movie overview text.

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License

Add a license here (e.g., MIT) if you plan to make the repository open-source formally.