# Development Guide

## Project Structure

```
/
├── scripts/
│   ├── install/              # Installation scripts (*-install.sh)
│   │   ├── core-service-install.sh   # Your service dependencies
│   │   ├── filebrowser-install.sh    # File browser
│   │   └── persistence-install.sh    # Data persistence
│   └── start/                # Service startup scripts (*-start.sh)
│       ├── core-service-start.sh     # Your main service (IMPLEMENT THIS)
│       ├── filebrowser-start.sh      # File browser startup
│       └── persistence-start.sh      # Persistence service
├── configs/                  # Configuration template files
│   └── templates/            # Service configuration templates
│       ├── aria2.conf.template       # aria2 configuration template
│       ├── restic.conf.template      # restic configuration template
│       └── webdav.toml.template      # webdav configuration template
├── Dockerfile               # Container build configuration
└── docker-entrypoint.sh    # Main container entry point
```

## Core Service Implementation

The core service is implemented in `scripts/start/core-service-start.sh` and contains your main application logic.

### Basic Implementation

```bash
#!/bin/bash
set -e

# Log function
log() {
    echo "[$(date '+%Y-%m-%d %H:%M:%S')] [CORE-SERVICE] $*"
}

# Activate Python virtual environment
source /opt/venv/bin/activate

# Configuration from environment variables
SERVICE_PORT="${SERVICE_PORT:-7860}"
MODEL_NAME="${MODEL_NAME:-your-default-model}"

# Set up directories and cache
mkdir -p /home/user/models /home/user/cache
export HF_HOME=/home/user/cache
export TRANSFORMERS_CACHE=/home/user/cache

# Start your service
log "Starting service on port $SERVICE_PORT..."
exec python /home/user/your_app.py
```

### Common Frameworks

**Gradio Interface:**
```bash
# In core-service-install.sh
pip install gradio

# In core-service-start.sh
exec python -c "
import gradio as gr
def predict(text): return f'Echo: {text}'
gr.Interface(predict, 'text', 'text').launch(server_name='0.0.0.0', server_port=7860)
"
```

**FastAPI Service:**
```bash
# In core-service-install.sh
pip install fastapi uvicorn

# In core-service-start.sh
exec uvicorn your_app:app --host 0.0.0.0 --port 7860
```

**Streamlit Dashboard:**
```bash
# In core-service-install.sh
pip install streamlit

# In core-service-start.sh
exec streamlit run your_app.py --server.port 7860 --server.address 0.0.0.0
```

### Dependencies Installation

Add your specific dependencies to `scripts/install/core-service-install.sh`:

```bash
# =============================================================================
# CUSTOM DEPENDENCIES SECTION
# =============================================================================

log "Installing service dependencies..."
source /opt/venv/bin/activate

# Example: AI/ML service
pip install --no-cache-dir \
    torch \
    transformers \
    gradio \
    numpy

# Example: System libraries
apt-get install -y --no-install-recommends \
    libssl-dev \
    ffmpeg
```

**Common dependency patterns:**
- AI/ML: `torch transformers huggingface-hub`
- Web APIs: `fastapi uvicorn`
- Dashboards: `streamlit plotly`
- Computer vision: `opencv-python pillow`

### Environment Variables

```bash
# Core configuration
SERVICE_PORT="${SERVICE_PORT:-7860}"
MODEL_NAME="${MODEL_NAME:-microsoft/DialoGPT-medium}"
HF_TOKEN="${HF_TOKEN:-}"
DEBUG_MODE="${DEBUG_MODE:-false}"

# Custom variables
YOUR_API_KEY="${YOUR_API_KEY:-}"
YOUR_CONFIG_OPTION="${YOUR_CONFIG_OPTION:-default}"
```

## Optional Services

Enable optional services via environment variables:

| Service | Enable | Purpose |
|---------|--------|---------|
| **File Browser** | `FILEBROWSER_ENABLED=true` | Web-based file management |
| **Persistence** | `PERSISTENCE_ENABLED=true` | Automatic backup/restore with HF Datasets |
| **Cloudflare Tunnel** | `CLOUDFLARED_ENABLED=true` | Secure external access |
| **FRP Client** | `FRPC_ENABLED=true` | Reverse proxy for complex networking |

**Required variables:**
- Persistence: `HF_TOKEN`, `DATASET_ID`
- Cloudflare: `CLOUDFLARED_TUNNEL_TOKEN`
- FRP: `FRPC_SERVER_ADDR`, `FRPC_AUTH_TOKEN`

## Development Workflow

### Local Development

```bash
# Build and test
docker build -t my-hf-service .
docker run -p 7860:7860 \
  -e SERVICE_PORT=7860 \
  -e MODEL_NAME=your-model \
  -e DEBUG_MODE=true \
  my-hf-service
```

### Testing

```bash
# Test core service
docker run -p 7860:7860 my-hf-service

# Test with optional services
docker run -p 7860:7860 \
  -e FILEBROWSER_ENABLED=true \
  -e PERSISTENCE_ENABLED=true \
  -e HF_TOKEN=your_token \
  -e DATASET_ID=username/dataset \
  my-hf-service
```

### Deployment to HF Spaces

1. Push your changes to repository
2. Create a new Docker Space on Hugging Face
3. Set environment variables in Space settings
4. HF Spaces automatically builds and deploys

## Adding Custom Services

### 1. Create Installation Script
Create `scripts/install/your-service-install.sh`:

```bash
#!/bin/bash
set -e

log() {
    echo "[$(date '+%Y-%m-%d %H:%M:%S')] [YOUR-SERVICE] $*"
}

log "Installing your service..."
# Installation steps here
log "Installation completed"
```

### 2. Create Startup Script
Create `scripts/start/your-service-start.sh`:

```bash
#!/bin/bash
set -e

log() {
    echo "[$(date '+%Y-%m-%d %H:%M:%S')] [YOUR-SERVICE] $*"
}

if [[ "${YOUR_SERVICE_ENABLED:-false}" == "true" ]]; then
    log "Starting your service..."
    exec your-service-binary
else
    log "Service disabled"
    exit 0
fi
```

The system automatically discovers and starts services based on the `*_ENABLED` environment variable pattern.

## Troubleshooting

### Common Issues
| Problem | Solution |
|---------|----------|
| Service not starting | Check logs and verify environment variables are set |
| Port conflicts | Ensure SERVICE_PORT matches exposed port (7860) |
| Model loading failures | Verify model name and HF_TOKEN permissions |
| Permission denied | Check file ownership: `chown user:user /home/user/script/start/*` |

### Debugging Commands
```bash
# Check running processes
ps aux | grep -E "(python|your-service)"

# Check environment variables
env | grep -E "(SERVICE_|MODEL_|HF_)"

# Check logs
tail -f /tmp/*.log

# Test connectivity
curl -I http://localhost:7860
```

## Best Practices

### Code Organization
- Keep application code in `/home/user/`
- Use `/home/user/models/` for model files
- Use `/home/user/cache/` for temporary files
- Use `/home/user/data/` for persistent data

### Development
- Use environment variables for configuration
- Provide sensible defaults
- Implement proper error handling
- Use HF Spaces secrets for sensitive data
- Test locally before deploying
- Document custom environment variables

## 配置模板系统

项目使用模板化配置系统，简化服务配置管理。所有服务配置模板都保存在`configs/templates/`目录中，启动脚本通过环境变量替换生成最终配置。

### 添加新的配置模板

1. 为你的服务创建配置模板文件 `configs/templates/your-service.conf.template`
2. 在模板中使用环境变量格式 `$VARIABLE_NAME` 作为可配置参数
3. 在服务启动脚本中使用以下代码加载模板：

```bash
# Determine template path
TEMPLATE_PATH="/etc/hf_docker_template/configs/templates/your-service.conf.template"
if [ ! -f "$TEMPLATE_PATH" ]; then
    TEMPLATE_PATH="$(dirname "$(dirname "$(dirname "$0")")")/configs/templates/your-service.conf.template"
fi

# Generate configuration from template
if [ -f "$TEMPLATE_PATH" ]; then
    envsubst < "$TEMPLATE_PATH" > "/home/user/config/your-service.conf"
    log "Using template from: $TEMPLATE_PATH"
else
    log "ERROR: Template file not found"
    exit 1
fi
```

### 模板最佳实践

- 使用明确的变量名称并提供默认值：`${YOUR_VARIABLE:-default_value}`
- 在模板中添加注释说明各参数的用途
- 将模板组织为逻辑部分（基础配置、网络、安全等）
- 遵循目标服务的配置文件格式和语法