MoodMash — AI-Powered Taste Engine for India's Youth

"Every platform optimizes for your attention. We optimize for your growth — through the content you already love."

An AI-powered personal taste engine that ingests your digital life (quiz responses, Spotify history) and builds a unified cross-domain taste identity across music, books, films, fashion, art, and creators. It generates a shareable "Taste DNA" card and creates progressive Growth Paths that blend Absorb → Create → Reflect steps.

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For Judges: Quick Start

Time Required: 5-7 minutes No sign-up needed

The 4 Flows to Test:

1. Onboarding → DNA Card (2-3 min)

   • Answer 15 questions about Indian music, films, books, art
   • Get AI-generated Taste DNA with unique archetype
   • See radar chart across 5 domains

2. Mindful Path (2-3 min)

   • Select mood + time available
   • Get "Tonight's Path" with 3-5 curated items
   • Each item explains WHY you'll love it + WHY it grows you

3. Feed (1-2 min)

   • Instagram-style grid of personalized content
   • 18 cards ranked by your taste DNA
   • Hover effects and interactions

4. Data Controls (1-2 min)

   • View your anonymized taste vector
   • See "What We Store vs Don't Store"
   • Export/delete your data

See JUDGE_QUICK_REFERENCE.md for detailed testing guide.

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Features

1. Taste Onboarding — 15 emotionally resonant questions across 5 domains (music, films, visual, creative, consumption)
2. Taste DNA Card — AI-generated archetype with name, vibe summary, radar chart, and taste markers
3. Growth Paths — Curated 3-5 item journeys with Absorb/Create/Reflect engagement types
4. Analytics Dashboard — Goal alignment scores, domain breakdown, and AI-generated insights
5. Privacy Data Control — Export and delete your data, DPDP Act 2023 compliant

Architecture

• Frontend: React 18 + Vite + Tailwind CSS + Recharts
• Backend: FastAPI (Python) + Uvicorn
• AI: Mock adapter (swappable to AWS Bedrock)
• Database: In-memory (swappable to DynamoDB)
• Content: 65+ curated items (70%+ Indian content)

Quick Start

Prerequisites

• Python 3.10+
• Node.js 18+
• npm or yarn

Installation

1. Clone the repository

   cd moodmash

2. Set up Python virtual environment

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

3. Install Python dependencies

   pip install -r backend/requirements.txt

4. Install frontend dependencies

   cd frontend
   npm install
   cd ..

5. Install root dependencies (for concurrent dev servers)

   npm install

6. Create .env file

   cp .env.example .env

Running the Application

Option 1: Run both servers with one command (recommended)

npm run dev

This starts:

• Frontend on http://localhost:3000
• Backend on http://localhost:8000

Option 2: Run servers separately

Terminal 1 (Backend):

source venv/bin/activate
PYTHONPATH=. uvicorn backend.main:app --reload --port 8000

Terminal 2 (Frontend):

cd frontend
npm run dev

Testing the API

Run the test script to verify all endpoints:

python3 test_api.py

Expected output:

✓ Health check passed
✓ Onboard passed
✓ DNA generation passed
✓ Growth path passed
✓ Analytics passed
All API tests passed!

Project Structure

moodmash/
├── frontend/                 # React frontend
│   ├── src/
│   │   ├── components/       # UI components
│   │   │   ├── Onboarding/   # Quiz flow
│   │   │   ├── DNACard/      # Taste DNA display
│   │   │   ├── GrowthPath/   # Path recommendations
│   │   │   ├── Analytics/    # Dashboard
│   │   │   ├── DataPanel/    # Privacy controls
│   │   │   └── common/       # Shared components
│   │   ├── pages/            # Route pages
│   │   ├── services/         # API client
│   │   └── data/             # Quiz questions
│   └── package.json
│
├── backend/                  # FastAPI backend
│   ├── functions/            # API handlers
│   │   ├── onboard.py
│   │   ├── generate_dna.py
│   │   ├── get_path.py
│   │   ├── path_feedback.py
│   │   ├── analytics.py
│   │   └── data_control.py
│   ├── lib/                  # Shared utilities
│   │   ├── ai.py             # AI adapter (mock/Bedrock)
│   │   ├── db.py             # DB adapter (in-memory/DynamoDB)
│   │   ├── archetypes.py     # 8 Indian archetypes
│   │   ├── path_engine.py    # Path generation logic
│   │   └── taste_analyzer.py # Quiz analysis
│   ├── main.py               # FastAPI app
│   └── requirements.txt
│
├── knowledge-base/           # Content catalog
│   └── content.json          # 65+ curated items
│
├── prompts/                  # AI prompt templates
│   ├── dna.prompt.md
│   ├── path.prompt.md
│   └── analytics.prompt.md
│
└── test_api.py               # API test script

User Flow

1. Landing (/) → Clean hero with value prop + CTA
2. Onboarding (/onboard) → 15 questions + goal selection
3. Taste DNA (/dna/:id) → Archetype card with radar chart
4. Growth Path (/path/:id) → Mood/time selector → curated items
5. Analytics (/analytics/:id) → Goal alignment + domain breakdown
6. Data Panel (/data/:id) → Export/delete data controls

API Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/api/onboard	Submit quiz answers → get session_id
GET	/api/dna/:id	Get taste DNA card data
POST	/api/path	Generate growth path
POST	/api/path/:id/feedback	Submit item feedback
GET	/api/analytics/:id	Get analytics data
GET	/api/data/:id	Export user data
DELETE	/api/data/:id	Delete user data

Testing

Manual Testing Flow

1. Visit http://localhost:3000
2. Click "Discover Your Taste DNA"
3. Answer 15 quiz questions
4. Select your north star goal
5. View your generated Taste DNA card
6. Click "Enter Growth Path"
7. Select mood and time available
8. Get your curated path
9. Mark items as Done/Skip/Save
10. View Analytics
11. Check Data Panel

API Testing

# Test health
curl http://localhost:8000/health

# Test onboard
curl -X POST http://localhost:8000/api/onboard \
  -H "Content-Type: application/json" \
  -d '{"quiz_answers": {"music_album": ["Prateek Kuhad"]}, "goal": "Build something"}'

# Test DNA (replace SESSION_ID)
curl http://localhost:8000/api/dna/SESSION_ID

Switching to AWS

Enable Bedrock AI

1. Update .env:

   USE_MOCK=false
   AWS_REGION=ap-south-1
   

2. Configure AWS credentials:

   aws configure

3. The backend/lib/ai.py will automatically route to Bedrock

Enable DynamoDB

1. Update .env:

   USE_DYNAMODB=true
   DYNAMODB_TABLE_SESSIONS=taste_profiles
   DYNAMODB_TABLE_COMPLETIONS=path_completions
   

2. Create tables using backend/template.yaml (SAM)

Design Principles

1. Direction over discovery — We help you know what to DO with your time
2. Productive rest — Entertainment and growth are fused
3. Identity over recommendations — The viral hook is "who you are"
4. User agency — You see your patterns and make conscious choices
5. Indian-first — Content, personas, cultural references built for India

Privacy

• Raw quiz answers are hashed and discarded immediately
• Only anonymized taste vectors are stored
• No PII, no tracking, no surveillance
• Users can export and delete all data
• Aligned with India's DPDP Act 2023

Environment Variables

# Backend
USE_MOCK=true                    # Use mock AI (true) or Bedrock (false)
USE_DYNAMODB=false               # Use in-memory (false) or DynamoDB (true)
AWS_REGION=ap-south-1            # AWS region for Bedrock/DynamoDB

# Frontend
VITE_API_URL=http://localhost:8000  # Backend API URL

Troubleshooting

Backend won't start

# Ensure venv is activated
source venv/bin/activate

# Reinstall dependencies
pip install -r backend/requirements.txt

# Check Python version
python --version  # Should be 3.10+

Frontend won't start

# Clear node_modules and reinstall
cd frontend
rm -rf node_modules package-lock.json
npm install

CORS errors

• Ensure backend is running on port 8000
• Check frontend/vite.config.js proxy settings
• Verify .env has correct VITE_API_URL

API returns 404

• Check backend logs for errors
• Verify PYTHONPATH is set: PYTHONPATH=. uvicorn backend.main:app
• Ensure all handler files exist in backend/functions/

Deployment

Frontend (AWS Amplify)

cd frontend
npm run build
# Upload dist/ to Amplify

Backend (AWS Lambda + API Gateway)

# Install SAM CLI
pip install aws-sam-cli

# Build and deploy
sam build
sam deploy --guided

License

MIT License - see LICENSE file for details

Acknowledgments

Built for the AI for Bharat Hackathon by Team Smooth Landing.

Special thanks to:

• Indian artists, musicians, filmmakers, and creators featured in our knowledge base
• The open-source community for amazing tools and libraries

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Made with ❤️ for India's youth