shantanuo/keras-flask-deploy-webapp repository overview

⁠Deploy Keras Model with Flask as Web App in 10 Minutes

⁠Getting started in 10 minutes

• Clone this repo
• Install requirements
• Run the script
• Check http://localhost:5000⁠
• Done! :tada:

:point_down:Screenshot:

⁠Docker Installation

⁠Build and run an image for keras-application pretrained model

$ cd keras-flask-deploy-webapp
$ docker build -t keras_flask_app .
$ docker run -d -p 5000:5000 keras_flask_app 

⁠Build and run an image from your model into the containeri.

After build an image as above, and

$ docker run -e MODEL_PATH=/mnt/models/your_model.h5  -v volume-name:/mnt/models -p 5000:5000 keras_flask_app

⁠Pull an built-image from Docker hub

For your convenience, can just pull the image instead of building it.

$ docker pull physhik/keras-flask-app:2 
$ docker run -d -p 5000:5000 physhik/keras-flask-app:2

Open http://localhost:5000⁠ after waiting for a minute to install in the container.

⁠Local Installation

⁠Clone the repo

$ git clone https://github.com/mtobeiyf/keras-flask-deploy-webapp.git

⁠Install requirements

$ pip install -r requirements.txt

Make sure you have the following installed:

• tensorflow
• keras
• flask
• pillow
• h5py
• gevent

⁠Run with Python

Python 2.7 or 3.5+ are supported and tested.

$ python app.py

⁠Play

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

⁠Customization

⁠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.

⁠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;
    }
}

⁠More resources

Check Siraj's "How to Deploy a Keras Model to Production"⁠ video. The corresponding repo⁠.

Building a simple Keras + deep learning REST API⁠