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

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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. 😃

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Customization

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

Details

Use your own model

Place your trained .h5 file saved by model.save() under models directory.

Check the commented code in app.py.

Use other pre-trained model

See Keras applications for more available models such as DenseNet, MobilNet, NASNet, etc.

Check this section in app.py.

UI Modification

Modify files in templates and static directory.

index.html for the UI and main.js for all the behaviors.

Deployment

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

Details

Run the app

Run the script and hide it in background with tmux or screen.

$ python app.py

You can also use gunicorn instead of gevent

$ gunicorn -b 127.0.0.1:5000 app:app

More deployment options, check here

Set up Nginx

To redirect the traffic to your local app. Configure your Nginx .conf file.

server {
  listen  80;

  client_max_body_size 20M;

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

Future

• Support detection and segmentation models

More resources

Building a simple Keras + deep learning REST API