About this Repository

BangkitEndpoint is an API reference. This repository uses the Flask Framework 2.0.1 and Python 3.9. This application can be run locally or online by deploying on the Cloud service, in the next section it is explained how to deploy this application on the Google Cloud Platform using the Cloud Run service. We configured this endpoint to be accessible to everyone without authentication. Configuration can be set by yourself. This applicaton

This repository is used to predict facial expressions from images and output them in the form of several parameters according to Facial Expression Recognition by justinshenk

And also this repository is part of Zmood App

• ZmoodBot
• Zmood Android App

Built With

• Flask
• Pyzoom
• Tensorflow
• OpenCV
• Numpy
• MTCNN

Getting Started

To get a local copy up and running follow these simple steps.

Installation

1. Clone the repo

   git clone https://github.com/ahsanf/BangkitEndpoint.git

2. Install Dependencies:

   pip3 install -r requirements.txt

Usages

1. Export FLASK_APP

   • in Linux Based

     export FLASK_APP=main_v1.py

   • in Windows

     set FLASK_APP=main_v1.py

2. Run the App

   flask run

3. Use /predict route to predict your face (images) with POST method, e.g:

   https://localhost:5000/predict

Output

{
    "data": {
        "expression": "fear",
        "score": 0.53
    },
    "status": "success"
}

Deploy To Cloud Service

Containerizing an app and uploading it to Container Registry

To containerize the app, Dokcerfile is the configuration when it will be deployed

1. Build your container image using Cloud Build, by running the following command from the directory containing the Dockerfile:

   gcloud builds submit --tag gcr.io/PROJECT-ID/flaskapp

   Where PROJECT-ID is your GCP project ID. You can get it by running gcloud config get-value project.

2. Upon success, you will see a SUCCESS message containing the image name (gcr.io/PROJECT-ID/flaskapp). The image is stored in Container Registry and can be re-used if desired.

Deploying to Cloud Run

To deploy the container image:

1. Deploy using the following command:

   gcloud run deploy --image gcr.io/PROJECT-ID/flaskapp

   If prompted to enable the API, Reply y to enable.

   Replace PROJECT-ID with your GCP project ID. You can view your project ID by running the command gcloud config get-value project.

   • You will be prompted for the service name: press Enter to accept the default name, flaskapp.
   • You will be prompted for region: select the region of your choice, for example us-central1.
   • You will be prompted to allow unauthenticated invocations: respond y .

   Then wait a few moments until the deployment is complete. On success, the command line displays the service URL.

2. Visit your deployed container by opening the service URL in a web browser.

Credits

• Facial Expression Recognition by justinshenk
• Cloud Run Documentation: Build and deploy a Python service