Bangkit-C23-PS363-Product-Based-Capstone

This is our repository for Company Based Capstone.

Our team Consist of 6 people.

• Bangkit-C23-PS363-Company-Based-Capstone
  • Members
  • What is this?
  • Getting started
  • Deployment to app engine
  • Deployment ML model to Cloud Function

Members

Name	Bangkit ID	Learning Path	Github Profile
Ega Fernanda Putra	M038DSX0496	Machine Learning	Profile
Muhammad Raffi Priyadiantama	M038DSX0498	Machine Learning	Profile
Muhammad Naufal A.	A013DSX2909	Mobile Development	Profile
Danil Ardi	A013DSX0990	Mobile Development	Profile
Ridho Kartoni Pasaribu	C013DSX0978	Cloud Computing	Profile
Ruben Tricahya Boediono	C038DSX0600	Cloud Computing	Profile

What is this?

This repository contains a Flask API that is deployed on Google App Engine. The API provides functionality for interacting with the specified endpoints using HTTP requests. The code is built with Python and utilizes the Flask web framework.

Getting started

1. Clone the repository to your local machine

git clone https://github.com/Callysta-Bangkit-Capstone-Project/Callysta-CloudComputing.git

2. Navigate to the project directory:

cd Callysta-CloudComputing

3. Create a virtual environment to isolate the project dependencies:

python -m venv callysta-env

4. Activate the virtual environment On MacOs/Linux

source callysta-env/bin/activate

On Windows

callysta-env/Scripts/activate

5. Install the required dependencies by running the following command

pip install -r requirements.txt

6. Set up the necessary configurations for deploying to Google App Engine. Make sure you have a Google Cloud Platform project with the App Engine service enabled.

7. Modify the app.yaml file to configure the App Engine deployment settings, such as the runtime, environment variables, and other configurations specific to your application.

8. Run the Flask API locally by executing the following command:

python main.py

9. This will start the development server at http://localhost:8080/.

Deployment to App Engine

1. Ensure you have the Google Cloud SDK installed on your machine.

2. Set up your Google Cloud project and configure the project ID:

gcloud config set project PROJECT_ID

3. Deploy the application to App Engine using the following command:

gcloud app deploy

4. Once the deployment is complete, you can access your API at https://YOUR_PROJECT_ID.uc.r.appspot.com/

Deployment ML model to Cloud Function

1. Create a new Cloud Function in your Google Cloud project. Make sure you have the Cloud Functions API enabled
2. Write a new function in the Cloud Function that will handle the prediction request from your Flask API
3. Within the Cloud Function, load your trained machine learning model
4. Implement the necessary code to preprocess the incoming request data from your Flask API and pass it to your machine learning model for prediction. Ensure that the response from the model is properly formatted for the API response
5. Deploy the Cloud Function by running the deployment command specific to your cloud environment

gcloud functions deploy function-name --runtime RUNTIME --trigger-http

Replace function-name with the name you want to give to your Cloud Function and RUNTIME with the language or runtime you're using (e.g., python3.9). 6. Once the Cloud Function is deployed, take note of the generated URL endpoint for the function