🐍 Startup Template Python

📝 Description

This repository contains additional services required by the DataClouder template. For reference implementations, check out our other templates:

• DataClouder Angular Template
• DataClouder Node Template

🚀 Getting Started

Clone the Project

git clone https://github.com/dataclouder-dev/dataclouder-template-python [your-project-name]

or use the button on github right top corner CREATE TEMPLATE

✅ Prerequisites

• Python >= 3.11
• Make >= 3.0.0 (Optional but highly recommended)
• Poetry >= 2.0.0 (Optional but recommended)
• Docker (Optional)
• Google Cloud credentials and environment variables
• MongoDB credentials

⚙️ Installation Options

• .env file is required, you need to create it but can copy and paste from .env.example, then set the variables

• Google service account file is required and placed it in the ./.cred folder at the project root

check documentation how to create service account here

You should be ready to go

Option 1: Using Makefile (Recommended) M

Requires Poetry and Docker to be installed.

make install # Only the first time
# Single command setup (Work in Progress)
make start

Option 2: Traditional Setup 🚶

1. Environment Setup

# Create virtual environment
python3 -m venv .venv

# Activate virtual environment
# For Unix/MacOS:
source .venv/bin/activate
# For Windows:
.venv\Scripts\activate

2. Install Dependencies

# Install required packages
pip install -r requirements.txt

3. Configuration

You'll need to obtain the following from the Polilan development team:

• Google Cloud credential file (place in /.cred folder)
• Environment variables template (.env file)

4. Launch Application

# Option 1: Using uvicorn
uvicorn app.main:app --reload

# Option 2: Using FastAPI development server
fastapi dev app/main.py

# Option 3) Recommended
make start

Once running, access the API documentation at: http://127.0.0.1:8000/docs

☁️ Deployment Environments

Environment	URL
QA	https://.....
Production	https://.....

🚢 Deployment Options

Manual Deployment 👨‍💻

1. Set environment variables:

   • Ensure the .env file is present in the project root

2. Build Docker image:

   make gcp-build

3. Deploy to Google Cloud Run:

   make gcp-deploy

Automated Deployment with Cloud Build 🤖

Change project name in Makefile. then just run make deploy

Note: if you want to automate multiple environment remember that makefile just replace if the variables in the .env dont exist so adding this variables in .env will have priority. you can add per every environment

Note: Before setting up automated deployment, we recommend performing one manual deployment to verify everything works correctly. Initial deployments require setting up Cloud Run service variables, while subsequent deployments do not. Also note that manual deployments use the default GCR repository for artifacts, while automated deployments use a custom repository.

Steps:

1. Fork the repository
2. Go to Cloud Build and create a new trigger
3. Grant GitHub access, select the repository, and accept conditions
4. Configure trigger settings according to your needs
5. Optional: Add permissions to the service account (Logs Writer, Cloud Run Admin, or default logs only)
6. Add the repository in Artifact Registry (recommended: add policies to remove old versions)

🔧 Development Tools

Poetry Package Manager 📦

Poetry is the recommended package manager for this project. Here are some useful commands:

poetry add <package>        # Add a new package
poetry remove <package>     # Remove a package
poetry update <package>     # Update a package
poetry install             # Install all dependencies
poetry build               # Build the project
poetry publish            # Publish the package
poetry show               # Check dependencies

Merge Upstream Updates 🔄

You can create new project but, if you want to get updates from the template, you can run

make merge-upstream

Docker Commands 🐳

# Build the image
docker build -t dc_python_server_image .

# Run the container
docker run -it -p 8080:8080 dc_python_server_image

Code Quality Tools ✨

We highly recommend using Ruff, a fast Python linter and formatter that replaces multiple tools like flake8. Settings are configured in the pyproject.toml file.

VSCode Integration

Install the Ruff VSCode Extension

Useful Ruff Commands

ruff check .                  # Check for issues
ruff check --fix .           # Fix issues automatically
ruff format .                # Format code
ruff check --fix --format .  # Fix issues and format code

For more information about Ruff rules and configuration, visit the official documentation.