Music Recommendation Chatbot

This project is a music recommendation chatbot that provides personalized song suggestions based on user input and emotional context. The system leverages state-of-the-art models like EmoRoBERTa for emotion analysis and integrates item-based collaborative filtering to generate accurate and diverse music recommendations. The chatbot is deployed and accessible at flask-song-recommender.onrender.com.

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Features

• Emotion-Based Recommendations:
  Utilizes EmoRoBERTa to analyze user dialogues and infer emotional states (e.g., joy, sadness, anger), aligning recommendations with the user’s mood.

• User Preference Filtering:
  Allows users to provide a favorite song or artist to personalize recommendations. The system also supports filtering by emotion alone when no preferences are provided.

• Item-Based Collaborative Filtering:
  Recommends similar songs by analyzing audio features such as danceability, energy, and tempo, ensuring both personalization and diversity.

• Dynamic and Scalable Deployment:
  The chatbot is hosted on Render, ensuring robust scalability and low latency for real-time user interactions.

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How It Works

1. User Interaction:
   Users interact with the chatbot through a web interface, providing inputs such as mood, song, or artist preferences.

2. Emotion Analysis:
   User conversations are analyzed using EmoRoBERTa to determine emotional states.

3. Filtering and Recommendations:

   • If a song or artist is provided, the system anchors the recommendations around this input.
   • If no preferences are given, emotion-based filtering drives the recommendations.
   • The final output consists of five songs: one matching the user's input and four similar tracks.

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Deployment

The project is deployed and live at:
https://flask-song-recommender.onrender.com

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Technologies Used

• Backend: Flask
• Machine Learning:
  • EmoRoBERTa (Emotion Detection)
  • Collaborative Filtering
• APIs:
  • Hugging Face API (Emotion Detection)
  • Spotify API (Song Metadata)
• Hosting: Render

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Getting Started Locally

Prerequisites

• Python 3.7+
• Virtual Environment (recommended)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name