File size: 3,660 Bytes
825e544 | 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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | ## System overview – Ytapp YouTube Music Mood-based Recommender
Independent FastAPI service for mood-based YouTube Music recommendations using emotionAI's DeepFace + Gemini API approach.
## Features
- Text emotion analysis (Gemini API) → YouTube Music song recommendations
- Face emotion analysis (DeepFace fast / Gemini API accurate) → YouTube Music song recommendations
- Search songs, artists, and get song details
- Standalone service (independent of main VibeCheck app)
## Setup
1. **Install dependencies:**
```bash
pip install -r requirements.txt --break-system-packages
```
2. **Configure Gemini API Key (Optional but recommended):**
- Create a `.env` file in the Ytapp directory
- Add: `GEMINI_API_KEY=your_api_key_here`
- Without Gemini API key, face analysis falls back to DeepFace only
- Text analysis requires Gemini API key
3. **Optional: OAuth Authentication**
- For authenticated requests (library management, playlists, etc.), set up OAuth:
- Get Client ID and Secret from [YouTube Data API](https://developers.google.com/youtube/v3)
- Select OAuth client ID → TVs and Limited Input devices
- Run: `ytmusicapi oauth`
- Follow instructions to create `oauth.json`
- Pass credentials to `YTMusic()` if needed
## Running Locally
```bash
python app.py
```
The service will run on `http://localhost:7860`
## Docker
```bash
docker build -t ytapp .
docker run -p 7860:7860 -e GEMINI_API_KEY=your_key_here ytapp
```
## API Endpoints
### Health Check
- `GET /health` - Service health status
### Search
- `GET /search?query=...&limit=20` - Search for songs
- `GET /song/{video_id}` - Get song details
- `GET /artists/search?query=...&limit=10` - Search for artists
- `GET /artists/{artist_id}/songs?limit=50` - Get artist songs
### Mood-based Recommendations
- `POST /mood/text` - Get song recommendation from text mood (uses Gemini API)
```json
{"text": "I feel happy today!"}
```
- `POST /mood/face` - Get song recommendation from face image (tries Gemini, falls back to DeepFace)
```
Form data: file (image/jpeg)
```
- `POST /mood/face/live` - Fast face analysis using DeepFace only (for live video feeds)
```
Form data: file (image/jpeg)
```
## Response Format
Mood endpoints return:
```json
{
"mood_label": "joy",
"mood_score": 0.95,
"video_id": "dQw4w9WgXcQ",
"title": "Song Title",
"artists": ["Artist Name"],
"album": "Album Name",
"duration": "3:45",
"image_url": "https://...",
"external_url": "https://music.youtube.com/watch?v=..."
}
```
## Emotion Detection Methods
### Face Analysis
1. **Primary (if Gemini API key available)**: Uses Gemini 1.5 Flash for accurate emotion detection
2. **Fallback**: Uses DeepFace for fast offline emotion detection
3. **Live mode**: Uses DeepFace only for real-time video feeds
### Text Analysis
- Uses Gemini API to analyze emotional tone of text
- Requires `GEMINI_API_KEY` to be set
## Emotion Mapping
- **Joy/Happy** → Happy, upbeat, dance music
- **Sad** → Sad songs, ballads, emotional music
- **Anger** → Rock, metal, intense music
- **Fear** → Calm, ambient, meditation music
- **Surprise** → Experimental, indie, alternative music
- **Neutral** → Chill, background, easy listening
- **Disgust** → Alternative rock, indie music
## Notes
- DeepFace models are preloaded during Docker build for faster startup
- Gemini API provides more accurate emotion detection but requires API key
- DeepFace works offline and is faster for live video feeds
- No authentication required for basic searches and mood recommendations
- OAuth optional for library management and playlist creation
|