# Deploy to HuggingFace Spaces - Quick Start

## ✅ Issue Fixed
**The `quote_extractor` import error has been fixed!** The app will now work even if the file is missing.

---

## 🚀 Option 1: Automated Preparation (Recommended)

Run this script to prepare a clean deployment package:

```bash
python prepare_for_spaces.py
```

This will:
- Create a `spaces_deployment/` directory
- Copy only the required files
- Remove any .env or test files
- Show you a summary of what's included

Then upload everything from `spaces_deployment/` to your Space.

---

## 📋 Option 2: Manual Upload

Upload these files to your HuggingFace Space:

### Required Files (Must have)
```
app.py
llm.py
extractors.py
tagging.py
chunking.py
validation.py
reporting.py
dashboard.py
production_logger.py
quote_extractor.py
requirements.txt
```

### Optional Files
```
README.md
HUGGINGFACE_SPACES_SETUP.md
```

**DO NOT upload:**
- `.env` file
- `test_*.py` files
- `logs/` directory
- `outputs/` directory

---

## 🔧 Space Configuration

### 1. Create Space
- Go to https://huggingface.co/new-space
- Name: `transcriptor-ai` (or your choice)
- SDK: **Gradio**
- Hardware: **GPU (T4 or better)** ← Important!

### 2. Upload Files
- Drag and drop all files from the list above
- OR connect a Git repository

### 3. Configure (Optional)
Go to **Settings → Variables** and add:

| Variable | Value | When to Use |
|----------|-------|-------------|
| `DEBUG_MODE` | `True` | To see detailed logs |
| `LOCAL_MODEL` | `TinyLlama/TinyLlama-1.1B-Chat-v1.0` | For faster (but lower quality) processing |
| `LLM_TEMPERATURE` | `0.5` | For more deterministic outputs |

**Note:** All settings have defaults - you don't need to configure anything!

---

## ⏱️ First Deployment

### What to Expect
1. **Build time:** 2-5 minutes (installing dependencies)
2. **Model download:** 2-5 minutes (first time only - downloads Phi-3-mini)
3. **Subsequent starts:** 30-60 seconds

### Watch the Logs
Click **Logs** tab to see:
```
✅ Configuration loaded for HuggingFace Spaces
🚀 TranscriptorAI Enterprise - LLM Backend: local
[Local Model] Loading microsoft/Phi-3-mini-4k-instruct...
Downloading (…)lve/main/config.json: 100%
[Local Model] ✅ Model loaded on cuda:0
Running on local URL:  http://0.0.0.0:7860
```

---

## 🧪 Test Your Space

1. Wait for "Running on local URL" message
2. Upload a sample transcript (DOCX or PDF)
3. Select "HCP" as interviewee type
4. Click "Analyze Transcripts"

**Expected:**
- Processing time: 5-10 minutes (depending on transcript length)
- Quality score: 0.7-1.0
- CSV and PDF downloads available

---

## 🐛 Troubleshooting

### Error: `ModuleNotFoundError: No module named 'quote_extractor'`
**Status:** ✅ FIXED - This is now optional

### Error: `ModuleNotFoundError: No module named 'xyz'`
**Solution:** Upload the missing `xyz.py` file

### Error: `CUDA out of memory`
**Solution:**
- Change model: Add Variable `LOCAL_MODEL=TinyLlama/TinyLlama-1.1B-Chat-v1.0`
- OR upgrade to larger GPU

### Error: Very slow processing
**Check:**
- Is GPU hardware selected? (Not CPU)
- Look for "Model loaded on cuda:0" in logs
- If you see "cpu", upgrade to GPU tier

### Quality Score still 0.00
**Debug:**
1. Set `DEBUG_MODE=True` in Variables
2. Check logs for "[Local Model] ✅ Generated X characters"
3. Look for "[LLM Debug] Successfully extracted JSON"
4. If you see `[Error]` messages, share them

---

## 💡 Tips

### Reduce Costs
- Space sleeps after 48h inactivity (free)
- Only pays for GPU time when active
- ~$0.60/hour for T4 GPU

### Improve Speed
- Use smaller model (TinyLlama)
- Reduce max tokens (edit llm.py line 410)
- Process fewer chunks

### Improve Quality
- Use larger model (Mistral-7B)
- Increase temperature for creative outputs
- Keep default Phi-3-mini for best balance

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## 📞 Need Help?

1. **Check logs first** - Most issues show clear error messages
2. **Read HUGGINGFACE_SPACES_SETUP.md** - Detailed troubleshooting
3. **Test locally first** - Run `python test_local_model.py`

---

## ✨ You're Ready!

Run the preparation script:
```bash
python prepare_for_spaces.py
```

Then upload to HuggingFace Spaces and you're done! 🎉

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**Last Updated:** October 2025