WebSite

About

Under the FlaksServer directory, there are 9 folders. The Breed_Cat, Breed_Dog, Cat_vs_Dog, Emotion_Cat and Emotion_Dog folder contain the corresponding models and helper functions. The scripts directory contaisn the files which using the above models to predict. The static folder contains the images resources and the css and js libraries. The templates directory contains the html files. The testImages file used for temporarily store the image for prediction. The app.py file contains the server functions. Get trained models from models

Features

Python Flask,
TensorFlow

Dependencies

Python 3.7.7,
TensorFlow 1.15.0

Env variables

HAP_DB_NAME=hap
HAP_DB_HOST=localhost
HAP_DB_PORT=27017
HAP_DB_USERNAME
HAP_DB_PASSWORD

Deployment

Run on local

1. git clone the repo
2. Enable the python env 3.7.7
3. All dependencies are in requirements.txt. Run pip install -r requirements.txt to install all project dependencies (If any error comes from cmake dlib then run pip3 install cmake).
4. To run the app: python src/app.py
5. Hit the endpoint http://localhost:5000/

Run with Flask

For running the back-end server with Flask:

pip install -r requirements.txt

export FLASK_APP=app.py

flask run --host=0.0.0.0

Then you can visit the website on your localhost and the port is 5000. But run on this way, only one user can access the website simultaneously.

Run with Gunicorn

Using Gunicorn to run the server:

pip install -r requirements.txt

gunicorn -b 0.0.0.0:5000 -t 60 app:app

Then you can visit the website on your localhost and the port is 5000. On this way, the website can be visited by multiple users simultaneously.

Run with Docker

Build the base image

Use the following command to build the base image (Docker version 19.03 or above):

Without GPU support:

DOCKER_BUILDKIT=1 docker build -t repo.eresearch.unimelb.edu.au:8000/happypet/tensorflow:1.15.2-py3 -f docker/base_image/Dockerfile .

With GPU support:

DOCKER_BUILDKIT=1 docker build -t repo.eresearch.unimelb.edu.au:8000/happypet/tensorflow:1.15.2-gpu-py3 -f docker/base_image/Dockerfile .

Build the release image

Use the following command to build the release image:

Without GPU support:

docker build -t repo.eresearch.unimelb.edu.au:8000/happypet/webapp:latest -f docker/release/Dockerfile .

With GPU support:

docker build -t repo.eresearch.unimelb.edu.au:8000/happypet/webapp:latest -f docker/release/Dockerfile .

Run Docker container

Without GPU support:

docker run -p 5000:5000 repo.eresearch.unimelb.edu.au:8000/happypet/webapp:latest

With GPU support:

docker run -p 5000:5000 repo.eresearch.unimelb.edu.au:8000/happypet/webapp-gpu:latest