Pretty & simple image classifier app template. Deploy your own trained model or pre-trained model (VGG, ResNet, Densenet) to a web app using Flask in 10 minutes.
APACHE-2.0 License
A minimal and customizable repo to deploy your image models as web app easily.
docker run --rm -p 5000:5000 ghcr.io/imfing/keras-flask-deploy-webapp:latest
Screenshot:
$ docker run --rm -p 5000:5000 ghcr.io/imfing/keras-flask-deploy-webapp:latest
With Docker, you can quickly build and run the entire application in minutes 🐳
# 1. First, clone the repo
$ git clone https://github.com/imfing/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.
It's easy to install and run it on your computer.
# 1. First, clone the repo
$ git clone https://github.com/imfing/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. 😃
It's also easy to customize and include your models in this app.
Note Also consider gradio or streamlit to create complicated web apps for ML models.
Place your trained .h5
file saved by model.save()
under models directory.
Check the commented code in app.py.
See Keras applications for more available models such as DenseNet, MobilNet, NASNet, etc.
Check this section in app.py.
Modify files in templates
and static
directory.
index.html
for the UI and main.js
for all the behaviors.