docs/STATUS.md

✅ 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)

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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 ✅

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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.

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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:

$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:

{
  "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:

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.

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If FastAPI Space Still Has Issues

Alternative 1: Test Locally

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

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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!