Dental Diagnosis and Treatment Framework

This code is the deployment code for the deep learning model developed in this repo. The deployment was done using Python's FastAPI, Docker, and Hugging Face Spaces.

1. API Description

To use the API, you may use the web interface at this Link.

Or you could make the request directly using this endpoint:

https://Kareem-404-Tooth-Diagnosis-and-Treatment-Framework.hf.space/inference

Simply, you send a POST request is sent where the images are in body's form-data where key is image_files and and value is the images. Please note that your request might take a while to process since we are using the free tier in hugging face to host the application.

2. How to Run Locally

Clone the repo:

git clone https://github.com/Kareem404/dental-diagnosis-treatment-framework.git

After cloning the repo, you could run the API as a FastAPI application using uvicorn or as a docker container.

2.1 Run as FastAPI App

To run the application as a FastAPI application. First, install the requirements:

pip install -r requirements.txt

Second, run using uvicorn:

uvicorn app:app --host 0.0.0.0 --port 8000

This runs the application at http://localhost:8000.

2.2 🐋 Run as Docker Container

To run the app as a docker container. First, build a docker image:

docker build -t dental-diagnosis-app .

Once the image is built, you could run it like this:

docker run -it -p 7860:7860 dental-diagnosis-app

The app should be accessible at http://localhost:7680