python-api/TROUBLESHOOTING.md

# 🔧 Python API Troubleshooting Guide

Panduan mengatasi error umum di Python API.

## ❌ Error: "No module named 'sklearn'"

### Problem
```
Error loading model: No module named 'sklearn'
```

### Penyebab
scikit-learn belum terinstall atau nama import tidak sesuai.

### Solusi

**Option 1: Reinstall scikit-learn**
```bash
pip uninstall scikit-learn -y
pip install scikit-learn==1.3.2
```

**Option 2: Install semua dependencies ulang**
```bash
cd python-api
pip install -r requirements.txt
```

**Option 3: Gunakan virtual environment (Recommended)**
```bash
# Windows
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt

# Linux/Mac
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```

### Verifikasi
```bash
python -c "import sklearn; print(sklearn.__version__)"
# Output: 1.3.2
```

---

## ⚠️ Warning: "Supabase credentials not found"

### Problem
```
⚠ Supabase credentials not found, predictions won't be saved to database
```

### Penyebab
File `.env` tidak ada atau environment variables tidak diset.

### Solusi

**1. Buat file .env**
```bash
cd python-api
copy .env.example .env  # Windows
# atau
cp .env.example .env    # Linux/Mac
```

**2. Edit .env dengan credentials yang benar:**
```env
SUPABASE_URL=https://xyddxrfiacdcnipdclas.supabase.co
SUPABASE_ANON_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...
```

**3. Restart server:**
```bash
python app.py
```

### Expected Output
```
✅ Supabase client initialized successfully
✅ Model loaded successfully
```

---

## 🚫 Error: "FileNotFoundError: models/svm_densenet201_rbf.joblib"

### Problem
```
FileNotFoundError: [Errno 2] No such file or directory: 'models/svm_densenet201_rbf.joblib'
```

### Penyebab
Model files belum ada di folder `python-api/models/`.

### Solusi

**1. Download model files dari Google Drive**
- `svm_densenet201_rbf.joblib` (5.2 MB)
- `metadata.json` (353 bytes)

**2. Letakkan di folder yang benar:**
```
python-api/
├── app.py
├── models/
│   ├── svm_densenet201_rbf.joblib  ← Di sini
│   └── metadata.json               ← Di sini
└── requirements.txt
```

**3. Verify:**
```bash
# Windows
dir python-api\models

# Linux/Mac
ls -lh python-api/models/
```

📖 **Panduan lengkap:** Lihat `CARA_MELETAKKAN_FILE_MODEL.md`

---

## 🌐 Error: CORS Issues

### Problem
```javascript
Access to fetch at 'http://localhost:5000/classify' from origin 'http://localhost:3000'
has been blocked by CORS policy
```

### Penyebab
CORS headers tidak dikonfigurasi dengan benar.

### Solusi

**1. Verify flask-cors terinstall:**
```bash
pip show flask-cors
```

**2. Check app.py line 15:**
```python
CORS(app)  # Pastikan ini ada
```

**3. Restart server**

### Verifikasi
```bash
curl -X OPTIONS http://localhost:5000/classify -v
# Harus ada header: Access-Control-Allow-Origin: *
```

---

## 💾 Error: "Predictions not saving to database"

### Problem
API respond sukses tapi data tidak masuk Supabase.

### Penyebab
1. Environment variables tidak diset
2. RLS policies terlalu restrictive
3. Table schema tidak sesuai

### Solusi

**1. Check environment variables:**
```python
import os
print(os.getenv('SUPABASE_URL'))
print(os.getenv('SUPABASE_ANON_KEY'))
```

**2. Check Supabase RLS policies:**
```sql
-- Di Supabase SQL Editor
SELECT * FROM pg_policies WHERE tablename = 'predictions';
```

**3. Verify table structure:**
```sql
\d predictions
```

Expected columns:
- `id` (uuid, primary key)
- `image_data` (text)
- `predicted_class` (text)
- `confidence` (float8)
- `probabilities` (jsonb)
- `mode` (text)
- `created_at` (timestamptz)

**4. Test manual insert:**
```python
from supabase import create_client
import os

client = create_client(
    os.getenv('SUPABASE_URL'),
    os.getenv('SUPABASE_ANON_KEY')
)

result = client.table('predictions').insert({
    'image_data': 'test',
    'predicted_class': '6 Bulan',
    'confidence': 0.85,
    'probabilities': {'3 Bulan': 0.05, '6 Bulan': 0.85, '9 Bulan': 0.10},
    'mode': 'api'
}).execute()

print(result)
```

---

## 🔥 Error: "torch.cuda.OutOfMemoryError"

### Problem
```
RuntimeError: CUDA out of memory
```

### Penyebab
GPU memory tidak cukup untuk load model.

### Solusi

**Option 1: Force CPU mode**

Edit `app.py`:
```python
# Line ~25
device = torch.device('cpu')  # Force CPU
print(f"Using device: {device}")
```

**Option 2: Reduce batch size**

Untuk production, gunakan Hugging Face Spaces dengan GPU atau Google Cloud Run.

---

## 🐌 Performance: API Too Slow

### Problem
Response time > 10 detik per request.

### Penyebab
1. Model reload setiap request
2. CPU-only inference
3. Large image size

### Solusi

**1. Model caching (Already implemented):**
```python
# app.py - Model loaded once saat startup
model = load_model()  # Global variable
```

**2. Image optimization:**
```python
# Resize sebelum send
max_size = 1024
if image.size[0] > max_size or image.size[1] > max_size:
    image.thumbnail((max_size, max_size))
```

**3. Deploy ke platform dengan GPU:**
- Hugging Face Spaces (T4 GPU gratis)
- Google Cloud Run (GPU available)
- Railway (CPU optimized)

---

## 📦 Error: "pip install failed"

### Problem
```
ERROR: Could not find a version that satisfies the requirement torch==2.1.0
```

### Penyebab
Python version atau platform tidak compatible.

### Solusi

**1. Check Python version:**
```bash
python --version
# Harus: Python 3.10.x atau 3.11.x
```

**2. Install torch dengan index URL:**
```bash
# CPU only
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

# Kemudian install sisanya
pip install -r requirements.txt
```

**3. Update pip:**
```bash
python -m pip install --upgrade pip
```

---

## 🔌 Error: "Connection refused"

### Problem
```
ConnectionError: HTTPConnectionPool(host='localhost', port=5000):
Max retries exceeded with url: /classify
```

### Penyebab
1. Server tidak running
2. Port sudah dipakai
3. Firewall blocking

### Solusi

**1. Check server status:**
```bash
# Harus tampil: "Running on http://127.0.0.1:5000"
python app.py
```

**2. Check port:**
```bash
# Windows
netstat -ano | findstr :5000

# Linux/Mac
lsof -i :5000
```

**3. Gunakan port lain:**
```bash
# Di .env
PORT=5001
```

**4. Test dengan curl:**
```bash
curl http://localhost:5000/health
# Expected: {"status": "ok"}
```

---

## 📝 Logging & Debugging

### Enable Debug Mode

```python
# app.py
if __name__ == '__main__':
    app.run(
        host='0.0.0.0',
        port=int(os.getenv('PORT', 5000)),
        debug=True  # ← Set True untuk development
    )
```

### Check Logs

```bash
# Lihat full error trace
python app.py 2>&1 | tee api.log
```

### Test Endpoints

```bash
# Health check
curl http://localhost:5000/health

# Test classification
cd python-api
python test_local.py http://localhost:5000 test_image.jpg
```

---

## 🌍 Production Deployment Issues

### Hugging Face Spaces

**Problem:** Space status "Building" forever

**Solution:**
```dockerfile
# Verify Dockerfile
FROM python:3.10

WORKDIR /app
COPY . .
RUN pip install --no-cache-dir -r requirements.txt

EXPOSE 7860
CMD ["gunicorn", "--bind", "0.0.0.0:7860", "--timeout", "120", "app:app"]
```

### Railway

**Problem:** "Module not found" di production tapi lokal OK

**Solution:**
```bash
# Pastikan requirements.txt complete
pip freeze > requirements.txt
```

### Environment Variables

**Problem:** Env vars tidak terbaca di production

**Solution:**
1. Set via platform dashboard (Railway/Hugging Face)
2. Jangan commit `.env` ke git
3. Verify dengan `/health` endpoint

---

## 🆘 Masih Bermasalah?

### Langkah Debugging Sistematis

1. **Check Prerequisites:**
   ```bash
   python --version  # 3.10+
   pip list | grep -E "torch|sklearn|flask"
   ```

2. **Verify Files:**
   ```bash
   ls python-api/models/  # Harus ada .joblib dan .json
   ls python-api/.env     # Harus ada
   ```

3. **Test Step-by-Step:**
   ```python
   # test_import.py
   import torch
   import sklearn
   import flask
   from supabase import create_client
   print("All imports OK!")
   ```

4. **Check Logs:**
   ```bash
   python app.py 2>&1 | tee error.log
   # Kirim error.log untuk analisis
   ```

5. **Minimal Test:**
   ```bash
   curl -X POST http://localhost:5000/classify \
     -H "Content-Type: application/json" \
     -d '{"image": "data:image/jpeg;base64,/9j/4AAQ..."}'
   ```

---

## 📚 References

- [Python API README](./README.md)
- [Supabase Setup Guide](./SUPABASE_SETUP.md)
- [Model Setup Guide](./CARA_MELETAKKAN_FILE_MODEL.md)
- [Main Project README](../README.md)
- [Environment Setup](../ENVIRONMENT_SETUP.md)

## 💡 Tips

1. **Always use virtual environment** untuk avoid dependency conflicts
2. **Check logs first** sebelum cari solusi lain
3. **Test locally** sebelum deploy to production
4. **Keep dependencies updated** tapi test dulu di local
5. **Use GPU** di production untuk performance

---

**Butuh bantuan lebih lanjut?** Check dokumentasi atau review error logs secara detail.