X-ray Colab ML Model

What is this for?

This repository give you information about the X-ray image and predicting the diseases of a person.

Ngrok

Ngrok allows you to expose a web server running on your local machine to the internet. Just tell ngrok what port your web server is listening on.

Models and technologies used.

This is a pocket application that is mainly focused on aiding medical professionals on their diagnostics and treatments for chest anomalies based on chest X-Rays. On this application, users can upload a chest X-Ray image and a deep learning model will output the probability of 14 different anomalies taking place on that image

Links to references

Huggingface ML Model

https://huggingface.co/spaces/Rules99/YouRadiologist

File Path

Here is the file path for Xray Colab ML Model. /Xray Colab API/XrayColab.ipynb

You can use the Xray Colab file in your Colab setup

Installation

pip install flask-ngrok

pip install pyngrok

pip install -U flask-cors

pip install flask-ngrok

! pip install transformers

Instruction

First, set up this repository on your local machine or colab. Installing all dependency in your local machine or colab. and, authentication you ngrok token.

To run run all the jupiter or colab cell

To make changes at line 2 on ngrok authtoken "<_YOUR_NGROK_TOKEN_>" this is your ngrok token.

Laravel and Vue Installation

Clone the Application on your local system. After cloning the application on your local system use cd X-ray-Colab-ML-Model command to go to the Cartoon ML model Directory

Install the default dependencies by running the following command.

composer update

npm install

Setting Up Database

First change the default database in config/database.php Add your database credentials in .env file. Run php artisan migrate to setup your database migration.

To start your Local server

npm run dev

php artisan serve

To Add your Ngrok link

Open your local server and go to this path:

http://127.0.0.1:8000/linkadd

Sample Video

Contributing

If you want to contribute to a project and make it better, your help is very welcome. Contributing is also a great way to learn more about social coding on Github, new technologies and and their ecosystems and how to make constructive, helpful bug reports, feature requests and the noblest of all contributions: a good, clean pull request.