Music-Weather Recommender

Overview

The Music-Weather Recommender is a machine learning-powered application that suggests music based on the current weather conditions. The project uses Flask for the backend and scikit-learn for the recommendation model.

Features

• Uses a trained machine learning model to suggest songs based on weather conditions.
• Fetches live weather data from an API.
• Provides a simple API to get song recommendations.

Installation

Prerequisites

Make sure you have the following installed:

• Python 3.10+
• pip
• virtualenv (optional but recommended)

Setup

1. Clone the repository:

   git clone https://github.com/linhkhanhhoang/Music-Weather-Recommender.git
   cd Music-Weather-Recommender

2. Set up a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Upgrade pip and install dependencies:

   pip install --upgrade pip setuptools wheel
   pip install -r requirements.txt

4. Run the app:

   python3 ml_model/scripts/app.py

   node discord_bot/index.js

   The server will start at http://127.0.0.1:5000

Usage

API Endpoints

Get Song Recommendations

• Endpoint: GET /recommend
• Query Parameters:
  • location: City name or coordinates for weather lookup.
• Example Request:

  curl "http://127.0.0.1:5000/recommend?location=New%20York"

• Response Example:

  {
    "location": "New York",
    "weather": "Rainy",
    "recommended_songs": [
      "Song 1",
      "Song 2",
      "Song 3"
    ]
  }