Deploy to Hugging Face Spaces: Fix model loading and add documentation

- Fix model_utils.py: Use self.model_path instead of model_path parameter
- Fix AnomalyDetector: Return all required fields (threshold, top_drivers)
- Update test_api.py: Add support for Hugging Face Spaces URL
- Add comprehensive documentation (TESTING.md, SPACE_INFO.md)
- Update README.md with Hugging Face Spaces frontmatter

SPACE_INFO.md

@@ -0,0 +1,99 @@
+# Hugging Face Space Information
+
+## Space Details
+
+- **Username**: `bldeaw`
+- **Space Name**: `ml_service`
+- **Full Space Path**: `bldeaw/ml_service`
+- **Space URL**: `https://bldeaw-ml-service.hf.space`
+- **Git Repository**: `https://huggingface.co/spaces/bldeaw/ml_service`
+
+---
+
+## Quick Commands
+
+### Test API
+
+```bash
+# Test health endpoint
+curl https://bldeaw-ml-service.hf.space/health
+
+# Test prediction
+curl -X POST https://bldeaw-ml-service.hf.space/predict/job-fail \
+  -H "Content-Type: application/json" \
+  -d '{
+    "zone": "prod",
+    "job_nm": "daily_export",
+    "job_start_time": "2026-01-21T01:00:00",
+    "duration_sec": 5400,
+    "status": "SUCCESS",
+    "explain": true
+  }'
+
+# Test anomaly detection
+curl -X POST https://bldeaw-ml-service.hf.space/detect/anomaly \
+  -H "Content-Type: application/json" \
+  -d '{
+    "features": {
+      "duration_sec": 5400,
+      "duration_zscore": 1.6,
+      "err_msg_len": 0
+    }
+  }'
+```
+
+### Using Test Script
+
+```bash
+# Test Hugging Face Space API
+python test_api.py --url https://bldeaw-ml-service.hf.space
+```
+
+### Git Commands
+
+```bash
+# Clone Space repository
+git clone https://huggingface.co/spaces/bldeaw/ml_service
+cd ml_service
+
+# Add remote (if working from local repo)
+git remote add hf https://huggingface.co/spaces/bldeaw/ml_service
+
+# Push to Space
+git push hf main
+```
+
+---
+
+## API Endpoints
+
+- **Health Check**: `https://bldeaw-ml-service.hf.space/health`
+- **Job Failure Prediction**: `https://bldeaw-ml-service.hf.space/predict/job-fail`
+- **Anomaly Detection**: `https://bldeaw-ml-service.hf.space/detect/anomaly`
+- **API Documentation**: `https://bldeaw-ml-service.hf.space/docs`
+- **ReDoc**: `https://bldeaw-ml-service.hf.space/redoc`
+
+---
+
+## Space Settings
+
+- **SDK**: Docker
+- **Hardware**: CPU Basic (or as configured)
+- **Visibility**: Public/Private (as configured)
+- **Port**: 7860 (Hugging Face Spaces default)
+
+---
+
+## Deployment Status
+
+Check deployment status at:
+- **Space Page**: https://huggingface.co/spaces/bldeaw/ml_service
+- **Logs**: https://huggingface.co/spaces/bldeaw/ml_service/logs
+
+---
+
+## Notes
+
+- Space URL format: `https://{username}-{space-name}.hf.space`
+- Replace hyphens in space name with hyphens in URL
+- Example: `bldeaw/ml_service` → `bldeaw-ml-service.hf.space`

TESTING.md

@@ -0,0 +1,380 @@
+# API Testing Guide
+
+คู่มือการทดสอบ API สำหรับ Job Failure Prediction & Anomaly Detection
+
+## 🚀 วิธีที่ 1: ใช้ Test Script (แนะนำ)
+
+### Local Testing
+
+```bash
+# Test API ที่รันบน localhost:8000 (default)
+python test_api.py
+
+# Test API ที่รันบน localhost:7860 (Hugging Face Spaces port)
+python test_api.py --port 7860
+
+# Test API ที่ URL เฉพาะ
+python test_api.py --url http://localhost:8000
+```
+
+### Hugging Face Spaces Testing
+
+```bash
+# Test API บน Hugging Face Spaces
+python test_api.py --url https://your-username-your-space.hf.space
+```
+
+---
+
+## 🧪 วิธีที่ 2: ใช้ cURL
+
+### Health Check
+
+```bash
+# Local
+curl http://localhost:8000/health
+
+# Hugging Face Spaces
+curl https://your-username-your-space.hf.space/health
+```
+
+### Job Failure Prediction
+
+```bash
+# Local
+curl -X POST http://localhost:8000/predict/job-fail \
+  -H "Content-Type: application/json" \
+  -d '{
+    "zone": "prod",
+    "job_nm": "daily_export_customer",
+    "job_start_time": "2026-01-21T01:00:00",
+    "duration_sec": 5400,
+    "status": "SUCCESS",
+    "err_msg": "",
+    "explain": true
+  }'
+
+# Hugging Face Spaces
+curl -X POST https://your-username-your-space.hf.space/predict/job-fail \
+  -H "Content-Type: application/json" \
+  -d '{
+    "zone": "prod",
+    "job_nm": "daily_export_customer",
+    "job_start_time": "2026-01-21T01:00:00",
+    "duration_sec": 5400,
+    "status": "SUCCESS",
+    "explain": true
+  }'
+```
+
+### Anomaly Detection
+
+```bash
+# Local
+curl -X POST http://localhost:8000/detect/anomaly \
+  -H "Content-Type: application/json" \
+  -d '{
+    "features": {
+      "duration_sec": 5400,
+      "duration_zscore": 1.6,
+      "avg_duration_7": 3000,
+      "failure_rate_7": 0.15,
+      "err_msg_len": 0,
+      "hour_sin": 0.2588,
+      "hour_cos": 0.9659
+    },
+    "threshold": 0.01
+  }'
+
+# Hugging Face Spaces
+curl -X POST https://your-username-your-space.hf.space/detect/anomaly \
+  -H "Content-Type: application/json" \
+  -d '{
+    "features": {
+      "duration_sec": 5400,
+      "duration_zscore": 1.6,
+      "avg_duration_7": 3000,
+      "failure_rate_7": 0.15,
+      "err_msg_len": 0
+    }
+  }'
+```
+
+---
+
+## 🌐 วิธีที่ 3: ใช้ Swagger UI (Interactive)
+
+### Local
+
+1. เริ่ม API server:
+   ```bash
+   uvicorn app:app --host 0.0.0.0 --port 8000
+   ```
+
+2. เปิด browser ไปที่:
+   ```
+   http://localhost:8000/docs
+   ```
+
+3. ทดสอบ endpoints ผ่าน interactive UI
+
+### Hugging Face Spaces
+
+1. ไปที่ Space page
+2. คลิก "API" tab หรือไปที่:
+   ```
+   https://your-username-your-space.hf.space/docs
+   ```
+3. ทดสอบ endpoints ผ่าน interactive UI
+
+---
+
+## 📝 วิธีที่ 4: ใช้ Python Requests
+
+### Example Script
+
+```python
+import requests
+import json
+
+# Base URL
+BASE_URL = "http://localhost:8000"  # หรือ URL ของ Hugging Face Space
+
+# Test Health
+response = requests.get(f"{BASE_URL}/health")
+print("Health Check:", response.json())
+
+# Test Prediction
+payload = {
+    "zone": "prod",
+    "job_nm": "daily_export",
+    "job_start_time": "2026-01-21T01:00:00",
+    "duration_sec": 5400,
+    "status": "SUCCESS",
+    "explain": True
+}
+response = requests.post(
+    f"{BASE_URL}/predict/job-fail",
+    json=payload
+)
+print("Prediction:", response.json())
+
+# Test Anomaly Detection
+payload = {
+    "features": {
+        "duration_sec": 5400,
+        "duration_zscore": 1.6,
+        "err_msg_len": 0
+    }
+}
+response = requests.post(
+    f"{BASE_URL}/detect/anomaly",
+    json=payload
+)
+print("Anomaly Detection:", response.json())
+```
+
+---
+
+## 🔍 วิธีที่ 5: ใช้ Postman / Insomnia
+
+### Import Collection
+
+1. สร้าง new collection
+2. เพิ่ม requests:
+
+**Health Check**
+- Method: `GET`
+- URL: `http://localhost:8000/health`
+
+**Job Failure Prediction**
+- Method: `POST`
+- URL: `http://localhost:8000/predict/job-fail`
+- Headers: `Content-Type: application/json`
+- Body (JSON):
+```json
+{
+  "zone": "prod",
+  "job_nm": "daily_export",
+  "job_start_time": "2026-01-21T01:00:00",
+  "duration_sec": 5400,
+  "status": "SUCCESS",
+  "explain": true
+}
+```
+
+**Anomaly Detection**
+- Method: `POST`
+- URL: `http://localhost:8000/detect/anomaly`
+- Headers: `Content-Type: application/json`
+- Body (JSON):
+```json
+{
+  "features": {
+    "duration_sec": 5400,
+    "duration_zscore": 1.6,
+    "err_msg_len": 0
+  }
+}
+```
+
+---
+
+## ✅ Expected Responses
+
+### Health Check Response
+
+```json
+{
+  "status": "healthy",
+  "service": "job-failure-prediction",
+  "models_loaded": {
+    "predictor": true,
+    "anomaly_detector": true
+  }
+}
+```
+
+### Job Failure Prediction Response
+
+```json
+{
+  "fail_probability": 0.79,
+  "risk_level": "MEDIUM",
+  "top_drivers": [
+    {
+      "feature": "failure_rate_7",
+      "shap_value": 0.30,
+      "effect": "increase"
+    }
+  ],
+  "recommended_actions": [
+    "Monitor upstream dependencies and recent job history"
+  ]
+}
+```
+
+### Anomaly Detection Response
+
+```json
+{
+  "reconstruction_error": 0.0235,
+  "is_anomaly": true,
+  "threshold": 0.01,
+  "top_drivers": [
+    {
+      "feature": "duration_zscore",
+      "error": 0.0142
+    }
+  ]
+}
+```
+
+---
+
+## 🐛 Troubleshooting
+
+### Connection Error
+
+**ปัญหา:** `ConnectionError: Could not connect to API`
+
+**แก้ไข:**
+- ตรวจสอบว่า API server กำลังรันอยู่
+- ตรวจสอบ URL และ port
+- ตรวจสอบ firewall settings
+
+### 404 Not Found
+
+**ปัญหา:** `404 Not Found`
+
+**แก้ไข:**
+- ตรวจสอบ endpoint path (`/health`, `/predict/job-fail`, `/detect/anomaly`)
+- ตรวจสอบว่า API server รันอยู่
+
+### 500 Internal Server Error
+
+**ปัญหา:** `500 Internal Server Error`
+
+**แก้ไข:**
+- ตรวจสอบ logs ของ API server
+- ตรวจสอบว่า models ถูก load ถูกต้อง
+- ตรวจสอบ request payload format
+
+### Models Not Loaded
+
+**ปัญหา:** `models_loaded: {"predictor": false, "anomaly_detector": false}`
+
+**แก้ไข:**
+- ตรวจสอบว่า models อยู่ใน `models/` directory
+- ตรวจสอบว่า model files มีครบถ้วน
+- ตรวจสอบ logs สำหรับ error messages
+
+---
+
+## 📊 Test Cases
+
+### Test Case 1: Basic Health Check
+```bash
+curl http://localhost:8000/health
+```
+**Expected:** Status 200, models_loaded = true
+
+### Test Case 2: Prediction with Minimal Data
+```bash
+curl -X POST http://localhost:8000/predict/job-fail \
+  -H "Content-Type: application/json" \
+  -d '{"job_nm": "test_job", "job_start_time": "2026-01-21T01:00:00"}'
+```
+**Expected:** Status 200, fail_probability between 0-1
+
+### Test Case 3: Prediction with Full Data
+```bash
+curl -X POST http://localhost:8000/predict/job-fail \
+  -H "Content-Type: application/json" \
+  -d '{
+    "zone": "prod",
+    "job_nm": "daily_export",
+    "job_start_time": "2026-01-21T01:00:00",
+    "duration_sec": 5400,
+    "status": "SUCCESS",
+    "explain": true
+  }'
+```
+**Expected:** Status 200, includes top_drivers and recommended_actions
+
+### Test Case 4: Anomaly Detection
+```bash
+curl -X POST http://localhost:8000/detect/anomaly \
+  -H "Content-Type: application/json" \
+  -d '{
+    "features": {
+      "duration_sec": 10000,
+      "duration_zscore": 3.0,
+      "err_msg_len": 0
+    }
+  }'
+```
+**Expected:** Status 200, is_anomaly = true (for high zscore)
+
+---
+
+## 🎯 Quick Test Checklist
+
+- [ ] Health endpoint returns 200
+- [ ] Models are loaded (check health response)
+- [ ] Prediction endpoint accepts requests
+- [ ] Prediction returns valid probability (0-1)
+- [ ] Risk level is one of: MINIMAL, LOW, MEDIUM, CRITICAL
+- [ ] SHAP explanations work (when explain=true)
+- [ ] Anomaly detection accepts feature dict
+- [ ] Anomaly detection returns is_anomaly boolean
+- [ ] Error handling works (invalid requests return 422/500)
+- [ ] CORS works (if testing from browser)
+
+---
+
+## 📚 Additional Resources
+
+- **Swagger UI**: `/docs` - Interactive API documentation
+- **ReDoc**: `/redoc` - Alternative API documentation
+- **OpenAPI Schema**: `/openapi.json` - Machine-readable API schema

model_utils.py

@@ -305,6 +305,8 @@ class AnomalyDetector:
             return {
                 'reconstruction_error': 0.0,
                 'is_anomaly': False,
+                'threshold': float(self.global_threshold) if hasattr(self, 'global_threshold') else 0.01,
+                'top_drivers': [],
                 'error': 'Model not loaded'
             }
         
@@ -356,5 +358,7 @@ class AnomalyDetector:
             return {
                 'reconstruction_error': 0.0,
                 'is_anomaly': False,
+                'threshold': float(self.global_threshold) if hasattr(self, 'global_threshold') else 0.01,
+                'top_drivers': [],
                 'error': str(e)
             }

models/anomaly_features.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f81a93f3be00e1e437383a368faa3f93555b4c74e5066db593f7e9e973d5538a
+size 119

models/anomaly_scaler.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8ce21dfa7ef83111e4ed6c34a97795a0db69bdad7d31b0dd553eb8b4e051e5a
+size 1087

models/anomaly_threshold.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:edd1017fcc8180ff81f2cad39858d677eba86e8a10cc643b236117e1d9e05e8d
+size 117

models/feature_schema.json

@@ -0,0 +1,39 @@
+{
+  "feature_columns": {
+    "numeric": [
+      "duration_sec",
+      "duration_zscore",
+      "avg_duration_7",
+      "failure_rate_7",
+      "err_msg_len",
+      "hour_sin",
+      "hour_cos"
+    ],
+    "categorical": [
+      "job_nm",
+      "tasksgroup_nm",
+      "zone",
+      "is_zeppelin",
+      "is_weekend"
+    ],
+    "anomaly_numeric": [
+      "duration_sec",
+      "duration_zscore",
+      "avg_duration_7",
+      "failure_rate_7",
+      "err_msg_len",
+      "hour_sin",
+      "hour_cos"
+    ]
+  },
+  "required_fields": [
+    "zone",
+    "job_nm",
+    "tasksgroup_nm",
+    "job_start_time",
+    "duration",
+    "status",
+    "err_msg",
+    "zeppelin"
+  ]
+}

models/job_fail_pipeline_cpu.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b83f1647154d65fd13cf34a5a7739fec20819677acd5abccd43befdb7902d207
+size 164484

models/shap_background.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:839f96623b8c5d9a3ffc2f224d7077e9b12b381f99a280bcfb1846610f216b51
+size 791328

test_api.py

@@ -1,11 +1,25 @@
 """
 Simple test script for API endpoints.
 Run this after starting the service to verify endpoints work correctly.
+
+Usage:
+    # Test local API (default port 8000)
+    python test_api.py
+    
+    # Test local API on port 7860 (Hugging Face Spaces default)
+    python test_api.py --port 7860
+    
+    # Test Hugging Face Spaces API
+    python test_api.py --url https://bldeaw-ml-service.hf.space
 """
 import requests
 import json
+import sys
+import argparse
 
-BASE_URL = "http://localhost:8000"
+# Default configuration
+DEFAULT_URL = "http://localhost:8000"
+DEFAULT_PORT = 8000
 
 
 def test_health():
@@ -67,13 +81,54 @@ def test_anomaly():
     print()
 
 
-if __name__ == "__main__":
+def main():
+    """Main test function."""
+    parser = argparse.ArgumentParser(description="Test API endpoints")
+    parser.add_argument(
+        "--url",
+        type=str,
+        default=None,
+        help="Base URL of the API (e.g., http://localhost:8000 or https://bldeaw-ml-service.hf.space)"
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=None,
+        help="Port number for local API (default: 8000)"
+    )
+    args = parser.parse_args()
+    
+    # Determine base URL
+    if args.url:
+        base_url = args.url.rstrip('/')
+    elif args.port:
+        base_url = f"http://localhost:{args.port}"
+    else:
+        base_url = DEFAULT_URL
+    
+    global BASE_URL
+    BASE_URL = base_url
+    
+    print(f"Testing API at: {BASE_URL}")
+    print("=" * 60)
+    print()
+    
     try:
         test_health()
         test_predict()
         test_anomaly()
-        print("All tests completed!")
+        print("=" * 60)
+        print("✅ All tests completed successfully!")
     except requests.exceptions.ConnectionError:
-        print("Error: Could not connect to API. Make sure the service is running on http://localhost:8000")
+        print("❌ Error: Could not connect to API.")
+        print(f"   Make sure the service is running on {BASE_URL}")
+        sys.exit(1)
     except Exception as e:
-        print(f"Error: {e}")
+        print(f"❌ Error: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()