Deploy Keras Model with Flask as Web App in 10 Minutes

A pretty and customizable web app to deploy your DL model with ease

Getting Started in 10 Minutes

• Clone this repo
• Install requirements
• Run the script
• Go to http://localhost:5000
• Done! 🎉

👇 Screenshot:

New Features 🔥

• Enhanced, mobile-friendly UI
• Support image drag-and-drop
• Use vanilla JavaScript, HTML and CSS. Remove jQuery and Bootstrap
• Switch to TensorFlow 2.0 and tf.keras by default
• Upgrade Docker base image to Python 3 (it's 2020)

If you need to use Python 2.x or TensorFlow 1.x, check out the legacy snapshot

Run with Docker

With Docker, you can quickly build and run the entire application in minutes 🐳

# 1. First, clone the repo
$ git clone https://github.com/mtobeiyf/keras-flask-deploy-webapp.git
$ cd keras-flask-deploy-webapp

# 2. Build Docker image
$ docker build -t keras_flask_app .

# 3. Run!
$ docker run -it --rm -p 5000:5000 keras_flask_app

Open http://localhost:5000 and wait till the webpage is loaded.

Local Installation

It's easy to install and run it on your computer.

# 1. First, clone the repo
$ git clone https://github.com/mtobeiyf/keras-flask-deploy-webapp.git
$ cd keras-flask-deploy-webapp

# 2. Install Python packages
$ pip install -r requirements.txt

# 3. Run!
$ python app.py

Open http://localhost:5000 and have fun. 😃

Customization

It's also easy to customize and include your models in this app.

Deployment

To deploy it for public use, you need to have a public linux server.

$ python app.py

$ gunicorn -b 127.0.0.1:5000 app:app

server {
  listen  80;

  client_max_body_size 20M;

  location / {
      proxy_pass http://127.0.0.1:5000;
  }
}

Future Plan

• [ ] Support detection and segmentation models

More Resources

Building a simple Keras + deep learning REST API