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# Gradio Model Server Skeleton | |
This repository contains a Gradio skeleton application which can be used to rapid prototype a demonstration app for your next machine learning/deep learning model. | |
To experiment and get a feeling on how to use this skeleton, a sample YOLOv5 object detection model is included in this proejct. Follow the installation and setup instructions to run the deep learning application. | |
## Pre-requisite & Setup | |
Ensure to have a Python environment before setting up, preferably Python 3.8+. | |
```sh | |
apt-get update | |
apt-get install ffmpeg libsm6 libxext6 -y | |
``` | |
```sh | |
pip install -r requirements.txt | |
``` | |
```sh | |
# for dev env, hot-reloading is enabled | |
gradio app.py | |
# for testing/UAT/prod env, ensure port number is cleared | |
python app.py --host 0.0.0.0 --port 7860 | |
``` | |
## Docker alternative | |
Alternatively, you can use docker to containerize the Gradio application. | |
```sh | |
# REQUIRED | |
export docker_repo_name=gradio-ml-skeleton | |
export docker_tag=dev_latest | |
# build an image from Dockerfile | |
sh build_docker.sh | |
``` | |
```sh | |
# creates a container layer over the image | |
sh launch_docker.sh | |
``` | |
## Application Preview | |
<img src="assets/Web%20capture_2-12-2022_17491_tyh71-miniature-memory.jpeg" alt="Preview"> | |