# 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