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crowci

A streamlit app for generating CI/CD pipelines for your workflow. It can be deployed to Streamlit Cloud.

Instructions to run the application locally.

Clone this repository.

Create a .env file inside the cloned directory and add the following variables.

AUTHORIZATION_URL=https://github.com/login/oauth/authorize
TOKEN_URL=https://github.com/login/oauth/access_token
CLIENT_ID="Your GitHub OAuth App's Client ID"
CLIENT_SECRET="You GitHub OAuth App's Client Secret"
REDIRECT_URI=http://localhost:8501/component/streamlit_oauth.authorize_button/index.html
OPENAI_API_KEY="Your openai api key"

Install the requirements.

pip install -r requirements.txt

Run the app.

streamlit run main.py

User Guide

Descriptive input for an accurate pipeline.

Provide a descriptive input with as much specifications as possible so that the model generates an accurate pipeline. Example:

Name: deploy-multi-env.yml (Push to main branch)

Jobs:

deploy:

Runs on: ubuntu-latest
Uses docker image: python:3.9
Steps:
Checkout code from main branch
Install dependencies: pip install boto3
deploy_staging (if ${{ github.event.branch }} == 'staging')

Needs: deploy (This job waits for the deploy job to finish)
Steps:
Use AWS credentials stored in ${{ secrets.AWS_STAGING_KEY }} and ${{ secrets.AWS_STAGING_SECRET }} (stored securely)
Configure environment variables for staging deployment using boto3
deploy_production (if ${{ github.event.branch }} == 'master')

Needs: deploy (This job waits for the deploy job to finish)
Steps:
Use AWS credentials stored in ${{ secrets.AWS_PRODUCTION_KEY }} and ${{ secrets.AWS_PRODUCTION_SECRET }} (stored securely)
Configure environment variables for production deployment using boto3

Enter the repository link in the correct format (eg. https://github.com/your-github-username/your-repository-name).

Generated Output

Clicking on Push to GitHub button pushes the generated yaml file to the .github/workflows directory of your repository.

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Automate GitHub Workflow with NLP

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