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https://github.com/imfing/keras-flask-deploy-webapp
:smiley_cat: 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.
https://github.com/imfing/keras-flask-deploy-webapp
deep-learning deployment flask keras pre-trained tensorflow webapp
Last synced: 2 days ago
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:smiley_cat: 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.
- Host: GitHub
- URL: https://github.com/imfing/keras-flask-deploy-webapp
- Owner: imfing
- License: apache-2.0
- Created: 2018-02-04T18:49:54.000Z (almost 7 years ago)
- Default Branch: main
- Last Pushed: 2024-03-24T20:14:23.000Z (10 months ago)
- Last Synced: 2025-01-11T21:06:43.880Z (9 days ago)
- Topics: deep-learning, deployment, flask, keras, pre-trained, tensorflow, webapp
- Language: JavaScript
- Homepage:
- Size: 98.6 KB
- Stars: 1,181
- Watchers: 30
- Forks: 469
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deploy Keras Model with Flask as Web App in 10 Minutes
[]()
A minimal and customizable repo to deploy your image models as web app easily.
## Getting Started
- Quick run with Docker:
```bash
docker run --rm -p 5000:5000 ghcr.io/imfing/keras-flask-deploy-webapp:latest
```
- Go to http://localhost:5000 and enjoy :tada:
Screenshot:
## New Features :fire:
- Enhanced, mobile-friendly UI
- Support image drag-and-drop
- Use vanilla JavaScript, HTML and CSS. No jQuery or Bootstrap
- Switch to TensorFlow 2.x and [tf.keras](https://www.tensorflow.org/guide/keras) by default
- Upgrade Docker base image to Python 3.11
------------------
## Run with Docker
#### Use prebuilt image
```
$ docker run --rm -p 5000:5000 ghcr.io/imfing/keras-flask-deploy-webapp:latest
```
#### Build locally
With **[Docker](https://www.docker.com)**, you can quickly build and run the entire application in minutes :whale:
```shell
# 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.
## Local Installation
It's easy to install and run it on your computer.
```shell
# 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. :smiley:
------------------
## Customization
It's also easy to customize and include your models in this app.
> **Note**
> Also consider [gradio](https://github.com/gradio-app/gradio) or [streamlit](https://github.com/streamlit/streamlit) to create complicated web apps for ML models.
Details
### Use your own model
Place your trained `.h5` file saved by `model.save()` under models directory.
Check the [commented code](https://github.com/mtobeiyf/keras-flask-deploy-webapp/blob/master/app.py#L37) in app.py.
### Use other pre-trained model
See [Keras applications](https://keras.io/applications/) for more available models such as DenseNet, MobilNet, NASNet, etc.
Check [this section](https://github.com/mtobeiyf/keras-flask-deploy-webapp/blob/master/app.py#L26) in app.py.
### UI Modification
Modify files in `templates` and `static` directory.
`index.html` for the UI and `main.js` for all the behaviors.
## More Resources
[Building a simple Keras + deep learning REST API](https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html)