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ece1b22 6c4fcf6 ece1b22 6c4fcf6 ece1b22 6c4fcf6 ece1b22 6c4fcf6 ece1b22 6c4fcf6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | ## VibeCheck – Mood-based Spotify Song Recommender
Backend + mobile app for an AI agent that:
- **Shows Vicoka songs on the home screen** (using Vicoka's Spotify artist ID)
- **Takes mood as input** via text or facial scan
- **Infers emotion** using Hugging Face models
- **Maps emotion → music** and suggests a **Nigerian playlist** on Spotify
- (Optionally) **logs interactions** in PostgreSQL.
### Tech stack
- **Language**: Python
- **Web framework**: FastAPI
- **AI models**: Hugging Face `transformers` (text & image pipelines)
- **Database**: PostgreSQL (via SQLAlchemy)
- **Music**: Spotify Web API (`spotipy`)
### Quick start
1. Create and activate a virtualenv.
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Set environment variables (or a `.env` file):
- `DATABASE_URL` – e.g. `postgresql+psycopg2://user:password@localhost:5432/vicoka`
- `SPOTIFY_CLIENT_ID`
- `SPOTIFY_CLIENT_SECRET`
- `SPOTIFY_REDIRECT_URI` (for OAuth, if you later build a full user-auth flow)
4. Run the API:
```bash
uvicorn app.main:app --reload
```
### System flow (end-to-end)
#### 1. Spotify & data sources
- VibeCheck uses the **Spotify Web API** for:
- Vicoka artist data (ID: `4IqQ3ooH5vvzRl3c3vBfwN`)
- Vicoka **top tracks** for the home screen
- Nigerian playlists and tracks for recommendations
- Hugging Face models provide:
- **Text emotion** detection (`/mood/text`)
- **Face emotion** detection (`/mood/face`)
#### 2. Backend (FastAPI, `app/main.py`)
- Exposes API endpoints:
- `POST /mood/text` → detect mood from text and return a **playlist recommendation**
- `POST /mood/face` → detect mood from face image and return a **playlist recommendation**
- `GET /spotify/artists/id/4IqQ3ooH5vvzRl3c3vBfwN/top-tracks` → Vicoka's **top tracks**
- Additional helpers: `/spotify/me`, `/spotify/playlists/*`, etc.
- Flow for mood:
1. Receive text or image.
2. Run the appropriate Hugging Face pipeline to get `mood_label` and `mood_score`.
3. Use the label (e.g. `sadness`, `joy`) to build Afrobeats/Naija-focused **search queries**.
4. Call Spotify Search API to pick a **playlist** (not a single track).
5. Return mood + playlist fields to clients.
- Flow for Vicoka top tracks:
1. Use artist ID to call `GET /v1/artists/{id}/top-tracks` with `market=NG`.
2. Normalize to simple track objects (id, name, uri, album, image, external URL).
#### 3. Mobile app (Expo / React Native, `mobile/`)
- Home screen shows:
- **Vicoka songs (top tracks)**:
- Grid of song cards (cover art, name, artist, album).
- Tapping a card opens Spotify via `spotify:track:...` or track URL.
- **Mood Check**:
- Tabs for **Text** and **Face** input.
- Calls `POST /mood/text` or `POST /mood/face`.
- **Latest playlist suggestion** for the detected mood.
- Users log in with Spotify (PKCE flow) before interacting:
- Mobile app uses Spotify **Authorization Code with PKCE** (no client secret on device).
- Access token is stored securely (SecureStore).
- Backend `/spotify/me` can be used to show basic profile info.
#### 4. Database (optional)
- PostgreSQL (or Neon) stores:
- `MoodLog` – user_id (optional), source (`text`/`face`), raw input, emotion label/score.
- `RecommendationLog` – which track/playlist was suggested for which mood.
- This logging is **non-critical**: if the DB is unavailable, the API still returns the recommendation.
### Next steps
- Polish mobile UI/UX for the VibeCheck home screen:
- Strong visual section for **Vicoka top tracks**.
- Clear “Login with Spotify” state and error handling.
- Iterate on mood → playlist mapping (more genres/queries per mood).
- Add lightweight analytics (which moods and playlists are most frequent).
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