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# Deployment Instructions for HuggingFace Spaces
## Files Ready for Deployment
Your HuggingFace Space needs these files (all created and ready):
1. **`sema_translation_api.py`** - Main API application
2. **`requirements.txt`** - Python dependencies
3. **`Dockerfile`** - Container configuration
4. **`README.md`** - Space documentation and metadata
## Deployment Steps
### Option 1: Using Git (Recommended)
1. **Navigate to your existing HF Space repository:**
```bash
cd backend/sema-api
```
2. **The files are ready to deploy as-is:**
```bash
# All files are ready:
# - sema_translation_api.py (main application)
# - requirements.txt
# - Dockerfile
# - README.md
```
3. **Commit and push to HuggingFace:**
```bash
git add .
git commit -m "Update to use consolidated sema-utils models with new API"
git push origin main
```
### Option 2: Using HuggingFace Web Interface
1. Go to your Space: `https://huggingface.co/spaces/sematech/sema-api`
2. Click on "Files" tab
3. Upload/replace these files:
- Upload `sema_translation_api.py`
- Replace `requirements.txt`
- Replace `Dockerfile`
- Replace `README.md`
## What Happens After Deployment
1. **Automatic Build**: HF Spaces will automatically start building your Docker container
2. **Model Download**: During build, the app will download models from `sematech/sema-utils`:
- `spm.model` (SentencePiece tokenizer)
- `lid218e.bin` (Language detection)
- `translation_models/sematrans-3.3B/` (Translation model)
3. **API Startup**: Once built, your API will be available at the Space URL
## Testing Your Deployed API
### 1. Health Check
```bash
curl https://sematech-sema-api.hf.space/
```
### 2. Translation with Auto-Detection
```bash
curl -X POST "https://sematech-sema-api.hf.space/translate" \
-H "Content-Type: application/json" \
-d '{
"text": "Habari ya asubuhi",
"target_language": "eng_Latn"
}'
```
### 3. Translation with Source Language
```bash
curl -X POST "https://sematech-sema-api.hf.space/translate" \
-H "Content-Type: application/json" \
-d '{
"text": "WΔ© mwega?",
"source_language": "kik_Latn",
"target_language": "eng_Latn"
}'
```
### 4. Interactive Documentation
Visit: `https://sematech-sema-api.hf.space/docs`
## Expected Build Time
- **First build**: 10-15 minutes (downloading models ~5GB)
- **Subsequent builds**: 2-5 minutes (models cached)
## Monitoring the Build
1. Go to your Space page
2. Click on "Logs" tab to see build progress
3. Look for these key messages:
- "π₯ Downloading models from sematech/sema-utils..."
- "β
All models loaded successfully!"
- "π API started successfully!"
## Troubleshooting
### If Build Fails:
1. Check the logs for specific error messages
2. Common issues:
- Model download timeout (retry build)
- Memory issues (models are large)
- Network connectivity issues
### If API Doesn't Respond:
1. Check if the Space is "Running" (green status)
2. Try the health check endpoint first
3. Check logs for runtime errors
## Key Improvements in This Version
1. **Consolidated Models**: Uses your unified `sema-utils` repository
2. **Better Error Handling**: Clear error messages and validation
3. **Performance Monitoring**: Tracks inference time
4. **Clean API Design**: Follows FastAPI best practices
5. **Automatic Documentation**: Built-in OpenAPI docs
6. **Flexible Input**: Auto-detection or manual source language
## Next Steps After Deployment
1. **Test the API** with various language pairs
2. **Monitor performance** and response times
3. **Update documentation** with your actual Space URL
4. **Consider adding rate limiting** for production use
5. **Add authentication** if needed for private use
## Important Note About File Structure
The Dockerfile correctly references `sema_translation_api:app` (not `app:app`) since our main file is `sema_translation_api.py`. No need to rename files - deploy as-is!
---
Your new API is ready to deploy! π
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