Moodify

Inspiration:

In today’s fast-paced world, people often struggle to understand and manage their emotions. Music has always been a powerful tool for healing and mood regulation. We were inspired to create Moodify to bridge the gap between emotional well-being and technology by using AI to detect moods and recommend personalized music and wellness activities.

What it does:

Moodify is an AI-powered platform that:

Detects user emotions through input (text, facial expression, or mood selection)

Recommends personalized music based on the detected mood

Suggests wellness activities like meditation, breathing exercises, or journaling

Helps users track and improve their emotional well-being over time

How we built it:

Frontend: HTML, CSS, JavaScript (for interactive UI)

Backend: Flask (Python-based web framework)

AI/ML Models:

Emotion detection using NLP / basic ML models

Recommendation system for music based on mood

Data Sources: Predefined datasets for emotions and music mapping

APIs (optional): Music streaming APIs (like Spotify) for real-time recommendations

Challenges we ran into:

Accurately detecting emotions from limited input data

Mapping moods to appropriate music consistently

Integrating AI models with a smooth user interface

Handling real-time responses without delays

Limited dataset for emotion-to-music mapping

Accomplishments that we're proud of: We are brainstorming and planning the prototype to build it effectively within the given time.

We aim to create a simple, user-friendly, and visually appealing interface.

We plan to integrate AI with mental wellness solutions.

The platform will provide personalized music and wellness recommendations.

Our goal is to combine technology with emotional intelligence.

What we learned:

Practical implementation of AI/ML concepts

How to build and deploy a full-stack web application

Importance of UI/UX in user engagement

Handling real-world data challenges

Team collaboration and time management during hackathons

What's next for Moodify:

Integrate real-time facial emotion detection using computer vision

Connect with live music platforms (Spotify, YouTube Music)

Add voice-based emotion detection

Improve recommendation accuracy using deep learning

Introduce a mobile app version

Add mental health tracking dashboard and insights

Built With

• css
• geminiapi
• html
• javascript
• mysql
• python