# ✅ Deployment Status & Next Steps

## What's Been Deployed

### 1. ✅ Model Repository
**URL**: https://huggingface.co/chenhaoq87/MilkSpoilageClassifier  
**Status**: LIVE ✅

Contains the trained model, config, handler, and documentation.

### 2. ✅ Gradio Space (Interactive UI) 
**URL**: https://huggingface.co/spaces/chenhaoq87/MilkSpoilageClassifier-Demo  
**Status**: FIXED & REBUILDING ⏳

**Fixed Issues**:
- ✅ Removed FastAPI hybrid (kept it simple - Gradio only)
- ✅ Pinned scikit-learn to 1.7.2 to match model version
- ✅ Simplified code to avoid Pydantic/Gradio 6 warnings

### 3. 🔧 FastAPI Space (REST API for Custom GPT)
**URL**: https://huggingface.co/spaces/chenhaoq87/MilkSpoilageClassifier-API  
**Status**: FIXED & REBUILDING ⏳  

**Fixed Issues**:
- ✅ Fixed Dockerfile to reference correct requirements file
- ✅ Pinned scikit-learn to 1.7.2
- ✅ Using Python 3.10 (not 3.13 which has compatibility issues)

---

## Why Separate Spaces is Better

| Aspect | Hybrid Space | Separate Spaces |
|--------|-------------|-----------------|
| **Simplicity** | ❌ Complex config | ✅ Simple, focused |
| **Debugging** | ❌ Hard to isolate issues | ✅ Easy to debug |
| **Dependencies** | ❌ Conflicts (Gradio vs FastAPI) | ✅ Minimal, specific |
| **Performance** | ❌ Heavier | ✅ Lightweight |
| **Maintenance** | ❌ Harder | ✅ Independent updates |

**Decision**: Keep them **separate** ✅

---

## Check Space Status

### Gradio Space
Visit: https://huggingface.co/spaces/chenhaoq87/MilkSpoilageClassifier-Demo

Should show: **Running** with green indicator

### FastAPI Space  
Visit: https://huggingface.co/spaces/chenhaoq87/MilkSpoilageClassifier-API

Docker build takes 2-5 minutes. Check logs if error persists.

---

## Testing Instructions

### Test Gradio Space (UI)
1. Go to: https://chenhaoq87-milkspoilageclassifier-demo.hf.space/
2. Enter values and click "Classify"
3. Should see prediction with probabilities

### Test FastAPI Space (API)
Once the Space shows "Running":

**PowerShell**:
```powershell
$body = @{
    spc_d7 = 4.0; spc_d14 = 5.0; spc_d21 = 6.0
    tgn_d7 = 3.0; tgn_d14 = 4.0; tgn_d21 = 5.0
} | ConvertTo-Json

Invoke-RestMethod -Uri "https://chenhaoq87-milkspoilageclassifier-api.hf.space/predict" `
  -Method POST -ContentType "application/json" -Body $body
```

**Expected Response**:
```json
{
  "prediction": "PPC",
  "probabilities": {
    "PPC": 0.85,
    "no spoilage": 0.10,
    "spore spoilage": 0.05
  },
  "confidence": 0.85
}
```

---

## Custom GPT Integration

Once the FastAPI Space is running, use this OpenAPI schema:

```yaml
openapi: 3.1.0
info:
  title: Milk Spoilage Classifier
  version: 1.0.0
servers:
  - url: https://chenhaoq87-milkspoilageclassifier-api.hf.space
paths:
  /predict:
    post:
      operationId: classifyMilkSpoilage
      summary: Predict milk spoilage type
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required: [spc_d7, spc_d14, spc_d21, tgn_d7, tgn_d14, tgn_d21]
              properties:
                spc_d7: {type: number, description: "SPC Day 7 (log CFU/mL)"}
                spc_d14: {type: number, description: "SPC Day 14 (log CFU/mL)"}
                spc_d21: {type: number, description: "SPC Day 21 (log CFU/mL)"}
                tgn_d7: {type: number, description: "TGN Day 7 (log CFU/mL)"}
                tgn_d14: {type: number, description: "TGN Day 14 (log CFU/mL)"}
                tgn_d21: {type: number, description: "TGN Day 21 (log CFU/mL)"}
      responses:
        '200':
          description: Prediction result
          content:
            application/json:
              schema:
                type: object
                properties:
                  prediction: {type: string}
                  probabilities: {type: object}
                  confidence: {type: number}
```

**No authentication required** - the Space is public.

---

## If FastAPI Space Still Has Issues

### Alternative 1: Test Locally
```powershell
cd D:\HuggingFace\MilkSpoilageClassifier
python fastapi_app.py
```

Then test at: `http://localhost:7860/predict`

### Alternative 2: Use Dedicated Inference Endpoint (Paid)
1. Go to model page → Deploy → Inference Endpoints
2. Select CPU instance (~$0.06/hour)
3. Get instant REST API with guaranteed uptime

---

## Summary

✅ **Model trained and saved** (96% accuracy)  
✅ **Model uploaded to HuggingFace**  
✅ **Gradio Space created** (for human users)  
⏳ **FastAPI Space deploying** (for Custom GPT)  
✅ **All code and docs created**  

**Next**: Wait 3-5 minutes for FastAPI Space to finish building, then test!