VibeCheck

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VibeCheck – Mood & Learning Platform

Hackathon: First Hackathon Participation Technologies Used: Flask, TextBlob (Python), HTML, CSS, JavaScript, Session Management, API Integration

About the Project

VibeCheck is a mood detection and learning platform that interacts with users in real-time, analyzes their emotions, and provides mood-specific responses, quotes, and flashcards to promote positive learning habits.

The platform was inspired by the idea of combining emotional well-being with continuous learning. I wanted to explore new technologies and experiment with real-time chat, sentiment analysis, and interactive UI elements.

How We Built It

**Backend: Built with Flask, handling chat messages and storing session-based history.Integrated TextBlob for sentiment analysis to detect mood from user messages.Defined mood-based responses, motivational quotes, and flashcards for personalized interactions.

**Frontend: Designed an interactive UI with HTML, CSS, and JavaScript.Added floating emojis, flashcards, and dynamic chat bubbles reflecting user mood.Implemented avatar selection and theme change based on detected mood.Added sound effects for each mood for better user engagement.

Challenges We Ran Into

**Server Setup & Session Management: Initial Flask setup and handling session-based chat history required multiple iterations.

**Real-Time Mood Detection: Combining keyword-based and sentiment-based detection with TextBlob for accurate mood classification.

**UI/UX Integration: Making the front-end dynamic, responsive, and visually appealing while syncing it with backend data.

**Time Management: Balancing learning new tech and implementing features during a tight hackathon timeline.

Accomplishments That We're Proud Of

Successfully built a fully functional prototype with interactive chat and mood detection.Implemented dynamic flashcards, quotes, and emojis personalized to the user's mood.Added user and AI avatar customization, enhancing user engagement.Integrated mood-based theme and sound effects for immersive experience.

What We Learned

How to integrate front-end and back-end seamlessly in a real-time application.Practical usage of sentiment analysis with Python to create interactive experiences.Techniques for dynamic UI updates, user personalization, and handling multiple data types.Debugging, API integration, and project management under hackathon constraints.

What's Next for VibeCheck

Expand mood detection accuracy using advanced NLP models. Add more interactive learning content and personalized flashcards. Improve mobile responsiveness and deploy as a full web application.

Include analytics dashboard to track user moods and learning progress over time.

Built With

• css
• flask-(web-framework)
• html
• javascript-(frontend-interactivity)
• python-(flask-backend)
• textblob