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# AI Mood-Based Activity Recommender
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**Tier:** 3-Advanced
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## Description
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The AI Mood-Based Activity Recommender is a web application that interprets a user's current emotional state and intelligently suggests personalized activities to support well-being, productivity, and emotional regulation. Users can express their mood through text input, emoji-based selection, or a slider scale, and the app responds with tailored recommendations — ranging from breathing exercises and journaling prompts to music playlists, games, or social activities.
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The application is especially valuable for students managing academic stress, individuals navigating mental health challenges, or anyone seeking a healthier way to engage with their emotions on a daily basis.
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**Resources required:**
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- A free API key from [OpenAI](https://platform.openai.com/) or [Google Gemini](https://ai.google.dev/) to power the mood analysis and activity recommendation engine.
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- (Optional) A [Firebase](https://firebase.google.com/) or [Supabase](https://supabase.com/) account for storing user mood history if persistence is needed.
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---
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## User Stories
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- [ ] User can express their current mood by typing a free-form description (e.g., "I feel restless and can't focus") or by selecting from a visual mood board with emoji/icons.
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- [ ] User can receive a curated list of 3–5 AI-generated activity recommendations tailored to their expressed mood, each with a brief explanation of why it might help.
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- [ ] User can view detailed information about a recommended activity, including estimated duration, required materials, and a step-by-step guide to get started.
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- [ ] User can mark an activity as "completed" or "not helpful" to provide feedback that refines future suggestions.
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- [ ] User can view a mood history log that shows their past mood entries alongside the activities they tried, displayed in a timeline or calendar view.
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- [ ] User can request a fresh set of alternative recommendations for the same mood without re-entering their input.
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- [ ] User can filter recommendations by category (e.g., physical, creative, social, mindfulness) to match their current energy level or preference.
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- [ ] User can save favourite activities to a personal collection for quick access later.
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---
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## Bonus Features
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- [ ] User can receive mood insights based on weekly or monthly patterns, such as "You tend to feel anxious on Sunday evenings — here are proactive activities to try."
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- [ ] User can interact with an AI-powered chatbot that asks gentle follow-up questions to better understand their mood before generating recommendations.
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- [ ] User can optionally enable webcam-based facial expression analysis (using face-api.js or MediaPipe) to auto-detect mood without typing.
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- [ ] User can set daily mood check-in reminders via browser notifications or email.
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- [ ] User can view a progress dashboard showing mood trends over time, streaks of completed activities, and most-used categories — visualised with charts.
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- [ ] User can share a specific activity recommendation as a card on social media or via a shareable link.
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- [ ] User can switch between a "Quick Mode" (instant single recommendation) and a "Deep Dive Mode" (multi-step guided session with reflection prompts).
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---
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## Useful Links and Resources
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### AI & NLP
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- [OpenAI API Docs](https://platform.openai.com/docs) — For GPT-powered mood analysis and recommendation generation
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- [Google Gemini API](https://ai.google.dev/docs) — Alternative LLM for mood understanding
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- [Hugging Face — Emotion Detection Models](https://huggingface.co/models?pipeline_tag=text-classification&search=emotion) — Pre-trained sentiment/emotion classifiers
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### Facial Expression (Bonus)
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- [face-api.js](https://github.com/justadudewhohacks/face-api.js) — In-browser face and emotion detection
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- [MediaPipe Face Landmarker](https://developers.google.com/mediapipe/solutions/vision/face_landmarker) — Google's real-time facial landmark solution
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### Frontend & UI
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- [React Documentation](https://react.dev/) — Component-based UI framework
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- [Framer Motion](https://www.framer.com/motion/) — Animations for mood transitions and activity cards
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- [Chart.js](https://www.chartjs.org/) — For mood trend visualisations in the dashboard
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### Backend & Storage
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- [Firebase Firestore](https://firebase.google.com/docs/firestore) — Real-time NoSQL database for mood history
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- [Supabase](https://supabase.com/docs) — Open-source Firebase alternative with PostgreSQL
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### Mental Health & Activity Research
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- [NHS — 5 Steps to Mental Wellbeing](https://www.nhs.uk/mental-health/self-help/guides-tools-and-activities/five-steps-to-mental-wellbeing/) — Evidence-based activity categories
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- [Greater Good Science Center](https://greatergood.berkeley.edu/topic/emotions) — Research-backed emotional regulation strategies
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---
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## Example Projects
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- **Wysa** (https://www.wysa.com) — An AI-powered mental health chatbot that recommends evidence-based exercises (CBT, mindfulness) based on user-reported emotions. Great reference for conversational mood input and structured activity flows.
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- **Reflectly** (https://reflectly.app) — A journaling app that uses AI to personalise daily reflections and mood tracking. Strong reference for mood history UI and calendar-based visualisation.
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- **Moodfit** (https://www.getmoodfit.com) — A mood tracking and mental fitness app with activity suggestions linked to emotional states. Good reference for the feedback loop between mood input and activity outcomes.
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- **Daylio** (https://daylio.net) — A micro-diary app that tracks activities and moods together. Useful reference for minimalist mood logging UX and activity correlation analytics.
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Dhanishka-CK
3 months ago
committed by
GitHub