Flask Email Prediction API

This is a Flask API for predicting the category of an email domain using OpenAI's GPT-3 model.

Requirements

Python 3.x Flask OpenAI API key Installation Clone the repository: git clone https://github.com/yourusername/flask-email-prediction-api.git Navigate to the project directory: cd flask-email-prediction-api Install the required packages: pip install -r requirements.txt Usage Run the application: python app.py Send a GET or POST request to the /prediction/ endpoint where email is the domain name you want to predict the category for.

Example

python Copy code

import requests

url = "http://localhost:5000/prediction/example.com"
response = requests.get(url)
print(response.json())  # {"RESPONSE": "Technology"}

Continuous Integration and Continuous Deployment (CI/CD)

This project uses GitHub Actions to implement a CI/CD pipeline to automatically build and deploy changes to Elastic Beanstalk whenever changes are pushed to the main branch.

CI Pipeline

When changes are pushed to the main branch, the CI pipeline will be triggered. The pipeline consists of the following steps:

Checkout source code from GitHub repository. Generate deployment package using zip. Configure AWS credentials. Copy the deployment package to an S3 bucket. Finish the CI pipeline. CD Pipeline The CD pipeline is triggered by the completion of the CI pipeline. The pipeline consists of the following steps:

Configure AWS credentials.

Create a new Elastic Beanstalk application version. Deploy the new application version to the Elastic Beanstalk environment. Print a message indicating that the deployment was successful.

Secrets

To implement the CI/CD pipeline, we need to configure two secrets in our repository settings:

AWS_ACCESS_KEY_ID: The access key ID for an AWS account with permissions to access Elastic Beanstalk and S3. AWS_SECRET_ACCESS_KEY: The secret access key for the same AWS account. License This project is licensed under the MIT License. See the LICENSE file for more information.