keras-flask-deploy-webapp：漂亮简单的图像分类器应用模板。 在10分钟内使用Flask将您自己的训练模型...

# Deploy Keras Model with Flask as Web App in 10 Minutes [](http://perso.crans.org/besson/LICENSE.html) []()  A pretty and customizable web app to deploy your DL model with ease <a href="https://www.buymeacoffee.com/fing" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/yellow_img.png" alt="Buy Me A Coffee"></a> ## Getting Started in 10 Minutes - Clone this repo - Install requirements - Run the script - Go to http://localhost:5000 - Done! :tada: :point_down: Screenshot: <p align="center"> <img src="https://user-images.githubusercontent.com/5097752/71063354-8caa1d00-213a-11ea-86eb-879238887c1f.png" height="420px" alt=""> </p> ## New Features :fire: - 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](https://www.tensorflow.org/guide/keras) by default - Upgrade Docker base image to Python 3 (it's 2020) <p float="left"> <img src="https://user-images.githubusercontent.com/5097752/71065048-61c1c800-213e-11ea-92f1-274cbe4734ba.png" height="330px" alt=""> <img src="https://user-images.githubusercontent.com/5097752/71062921-aeef6b00-2139-11ea-8b23-6b9eb1e326ca.png" height="330px" alt=""> </p> _If you need to use Python 2.x or TensorFlow 1.x, check out the [legacy](https://github.com/mtobeiyf/keras-flask-deploy-webapp/tree/legacy) snapshot_ ------------------ ## Run with Docker 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/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. ```shell # 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. :smiley: <p align="center"> <img src="https://user-images.githubusercontent.com/5097752/71064959-3c34be80-213e-11ea-8e13-91800ca2d345.gif" height="480px" alt=""> </p> ------------------ ## Customization It's also easy to customize and include your models in this app. <details> <summary>Details</summary> ### 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. </details> ## Deployment To deploy it for public use, you need to have a public **linux server**. <details> <summary>Details</summary> ### 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](https://flask.palletsprojects.com/en/1.1.x/deploying/wsgi-standalone/) ### 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; } } ``` </details> ## Future Plan - [ ] Support detection and segmentation models ## More Resources [Building a simple Keras + deep learning REST API](https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html)