README.md

---
title: Netflix Recommendation App
emoji: 🍿
colorFrom: blue
colorTo: red
sdk: docker
python_version: "3.12"
app_port: 7860
pinned: false
short_description: "Netflix title recommendations using cosine similarity."
tags: ["recommendation", "netflix", "flask", "cosine-similarity", "docker"]
---

# Netflix Recommender App

A Flask-based web application that provides personalized recommendations for Netflix content using a content-based filtering approach.

## Features
- Content-based recommendations based on TF-IDF and cosine similarity
- Search and autocomplete functionality
- Visualizations of Netflix content distribution
- Deployed on huggingface for easiest access
- Works on both macOS and Windows locally
- Docker deployment available

## Prerequisites
- **Google Chrome and Internet Access**

Ensure you have the following installed to build/run locally:
- **Python 3.12+** (Check with `python --version` or `python3 --version`)
- **Node.js and npm** (Check with `node -v` and `npm -v`)
- **pip** (Python package manager)


## View application online (easiest method)
https://huggingface.co/spaces/erichier/finalcapstone


## Manual Installation

### 1. Clone the Repository
```sh
git clone https://huggingface.co/spaces/erichier/finalcapstone
cd finalcapstone
```

### 2. Set Up a Virtual Environment

#### macOS & Linux
```sh
python3.12 -m venv venv
source venv/bin/activate
```

#### Windows (Command Prompt)
```sh
python -m venv venv
venv\Scripts\activate
```

#### Windows (PowerShell)
```powershell
python -m venv venv
venv\Scripts\Activate.ps1
```

### 3. Install Dependencies

```sh
pip install -r requirements.txt
npm install
```

### 4. Download Dataset
Verify `netflix_titles.csv` is placed present from git clone.
Available here if needed: https://www.kaggle.com/datasets/shivamb/netflix-shows

### 5. Build or Load Model & Run Application
Run the following command to preprocess the dataset and generate a similarity model:

```sh
gunicorn -w 2 -b 0.0.0.0:7860 recommend_app:app
```

This will load the Netflix dataset, process it, and save a cached similarity model (`cosine_sim_cache.pkl`).

The app will be available at:  
Access app at [http://0.0.0.0:7860] or [http://localhost:7860]


## File Structure

```
finalcapstone/
│── Dockerfile                # For huggingface or local easy deployment
│── README.md                 # Info and instructions on how to build and run
│── recommend_app.py          # Flask API entry point
│── recommendation_engine.py  # Machine Learning Recommendation Engine
│── netflix_titles.csv        # Dataset file from Kaggle.com
│── requirements.txt          # Python dependencies
│── package.json              # Frontend dependencies
│── static/
│   ├── index.html            # Main frontend page
│   ├── js/                   # Frontend scripts
│   ├── styles.css            # Stylesheet
│── venv/                     # Python virtual environment
```

## Stopping the Server
To stop the Flask server, press **Ctrl + C** in the terminal.

To deactivate the virtual environment:

#### macOS & Linux
```sh
deactivate
```

#### Windows
```sh
venv\Scripts\deactivate
```
## DOCKER Deployment (optional EASY local deployment using Docker)

```sh
docker build -t my-recommend-app .
docker run -p 7860:7860 my-recommend-app
```

access app at http://0.0.0.0:7860 or http://localhost:7860

# Example Use of the Application

## Steps:
1. Enter the name of a Netflix show or movie you like.
2. As you type, autocomplete will suggest matching options.
3. Select one of the autocomplete suggestions.
4. The application will display 10 similar movies and shows, each with a corresponding similarity score percentage.