Create Repository from Template using Github API

Test this app for free
96
import logging
from fastapi import FastAPI, HTTPException
from fastapi.responses import RedirectResponse
from some_get_request_handler import handle_get_endpoint
from some_post_request_handler import handle_post_endpoint, Data
from github_api_handler import create_repo_from_template

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.WARNING)

app = FastAPI()

@app.get("/", include_in_schema=False)
def root():
    return RedirectResponse(url='/docs')

@app.post("/create_repo")
def create_repository():
    try:
        result = create_repo_from_template()
        return result
    except Exception as e:
        logger.error(f"Failed to create repository: {e}")
        raise HTTPException(status_code=500, detail="Internal server error")
Get full code

Frequently Asked Questions

What are some business applications for this "Create Repository from Template using Github API" app?

This app can be valuable for businesses in several ways:

How can this template improve productivity in software development teams?

The "Create Repository from Template using Github API" app can significantly boost productivity by: - Reducing setup time for new projects - Ensuring all team members start with the same baseline - Allowing for easy replication of best practices across multiple projects - Enabling rapid prototyping and experimentation with new ideas

Can this template be customized for different types of projects or teams?

Yes, the "Create Repository from Template using Github API" app is highly customizable. You can: - Create different template repositories for various project types - Modify the environment variables to suit different team needs - Extend the functionality to include additional GitHub API operations

How can I modify the app to create private repositories instead of public ones?

To create private repositories using the "Create Repository from Template using Github API" app, you need to modify the create_repo_from_template function in the github_api_handler.py file. Change the private key in the data dictionary to True:

python data = { "name": new_repo_name, "description": new_repo_description, "private": True # Changed from False to True }

This will ensure that all repositories created from the template are private by default.

How can I add error handling for specific GitHub API errors in this template?

To add more specific error handling in the "Create Repository from Template using Github API" app, you can modify the create_repository function in main.py. Here's an example of how you could handle specific HTTP status codes:

python @app.post("/create_repo") def create_repository(): try: result = create_repo_from_template() if 'error' in result: status_code = result.get('status_code', 500) if status_code == 404: raise HTTPException(status_code=404, detail="Template repository not found") elif status_code == 422: raise HTTPException(status_code=422, detail="Invalid repository name") else: raise HTTPException(status_code=status_code, detail=result['error']) return result except Exception as e: logger.error(f"Failed to create repository: {e}") raise HTTPException(status_code=500, detail="Internal server error")

This modification allows for more granular error responses based on specific GitHub API error codes.

Created: | Last Updated:

This app creates a GitHub repository using a template repository with user-defined environment variables. The user can add the template repository name and repository owner details as environment variables. They can also add the custom name they would like to give the new repository created from the template. The app uses FastAPI for making the api call to Github endpoints.

Introduction to the Create Repository from Template using Github API Template

Welcome to the "Create Repository from Template using Github API" template! This template is designed to help you create a new GitHub repository using an existing template repository. It automates the process through a simple API call, allowing you to specify the new repository's name and description. This is particularly useful for quickly setting up new projects with a predefined structure and files.

Clicking Start with this Template

To begin using this template, simply click on the "Start with this Template" button. This will initialize the template in the Lazy Builder interface, pre-populating the code for you.

Initial Setup: Adding Environment Secrets

Before you can use this template, you need to set up some environment secrets within the Lazy Builder. These secrets will store sensitive information required by the GitHub API to create a repository from a template.

Here are the environment secrets you need to set up:

  • GITHUB_TOKEN: Your GitHub personal access token. You can create one by following the instructions on the GitHub documentation.
  • TEMPLATE_OWNER: The username of the owner of the template repository.
  • TEMPLATE_REPO: The name of the template repository.
  • NEW_REPO_NAME: The name you want to give your new repository.
  • NEW_REPO_DESCRIPTION: A description for your new repository.

To add these secrets:

  1. Go to the Environment Secrets tab within the Lazy Builder.
  2. Click on the "Add Secret" button.
  3. Enter the name of the secret (e.g., GITHUB_TOKEN) and its value.
  4. Repeat the process for each secret.

Test: Pressing the Test Button

Once you have set up the environment secrets, you can test the template by clicking the "Test" button. This will deploy the app and launch the Lazy CLI.

Using the App

After pressing the "Test" button, the Lazy CLI will handle the deployment of your application. Once the deployment is complete, Lazy will provide you with a dedicated server link. You can use this link to interact with the API.

Since this template uses FastAPI, you will also be provided with a link to the API documentation. This documentation will be accessible at the /docs endpoint and will allow you to see all the available API routes and test them directly from your browser.

Integrating the App

If you wish to integrate the newly created repository into another service or frontend, you can use the URL provided by the Lazy CLI after the repository is successfully created. This URL will point to the new GitHub repository, where you can clone the repository or set up webhooks and other integrations as needed.

For example, if you want to clone the repository, you can use the following command in your terminal:

git clone [URL provided by Lazy CLI] Replace [URL provided by Lazy CLI] with the actual URL provided after the repository creation.

Remember, all the steps listed above are mandatory to run and integrate the template. Follow them carefully to ensure a smooth experience with the "Create Repository from Template using Github API" template on Lazy.



Here are 5 key business benefits for this template:

Template Benefits

  1. Rapid Project Initialization: Enables businesses to quickly set up new projects based on pre-defined templates, significantly reducing setup time and ensuring consistency across projects.

  2. Automated Repository Creation: Streamlines the process of creating new repositories, allowing development teams to focus on coding rather than manual setup tasks, thereby increasing productivity.

  3. Standardization of Project Structures: Ensures that all new projects follow a standardized structure and include necessary boilerplate code, promoting best practices and reducing onboarding time for new team members.

  4. Scalable Project Management: Facilitates easy scaling of project creation, particularly useful for organizations managing multiple clients or projects simultaneously.

  5. Integration with CI/CD Pipelines: The automated repository creation can be easily integrated into existing CI/CD pipelines, enabling a fully automated workflow from project initiation to deployment.

Technologies

Enhance GitHub Workflows with Lazy AI Automation Templates for Issues, Pull Requests and More Enhance GitHub Workflows with Lazy AI Automation Templates for Issues, Pull Requests and More
FastAPI Templates and Webhooks FastAPI Templates and Webhooks

Similar templates

FastAPI endpoint for Text Classification using OpenAI GPT 4

This API will classify incoming text items into categories using the Open AI's GPT 4 model. If the model is unsure about the category of a text item, it will respond with an empty string. The categories are parameters that the API endpoint accepts. The GPT 4 model will classify the items on its own with a prompt like this: "Classify the following item {item} into one of these categories {categories}". There is no maximum number of categories a text item can belong to in the multiple categories classification. The API will use the llm_prompt ability to ask the LLM to classify the item and respond with the category. The API will take the LLM's response as is and will not handle situations where the model identifies multiple categories for a text item in the single category classification. If the model is unsure about the category of a text item in the multiple categories classification, it will respond with an empty string for that item. The API will use Python's concurrent.futures module to parallelize the classification of text items. The API will handle timeouts and exceptions by leaving the items unclassified. The API will parse the LLM's response for the multiple categories classification and match it to the list of categories provided in the API parameters. The API will convert the LLM's response and the categories to lowercase before matching them. The API will split the LLM's response on both ':' and ',' to remove the "Category" word from the response. The temperature of the GPT model is set to a minimal value to make the output more deterministic. The API will return all matching categories for a text item in the multiple categories classification. The API will strip any leading or trailing whitespace from the categories in the LLM's response before matching them to the list of categories provided in the API parameters. The API will accept lists as answers from the LLM. If the LLM responds with a string that's formatted like a list, the API will parse it and match it to the list of categories provided in the API parameters.

Icon 1 Icon 1
218

We found some blogs you might like...