bcd-Flask and Flasgger

An end-to-end implementation of Breast Cancer Detection using prosemble ML package within the Flask framework and Flasgger dockerized for deployment and also within streamlit ML app.

How to use

To diagnose breast cancer disease and return the confidence of the diagnosis,

1. Run the app.py and get the local host http://localhost/apidocs
2. To predict for a single test case, click on Get ---> Try it out
3. Enter the values for Radius_mean, Radius_texture and Method either as soft or hard
4. click on execute to get diagnosis with confidence

To diagnose from multiple inputs from a file

1. Click on POST ---> Try it out
2. Select your input file from its location and enter the Method either as soft or hard
3. Click on execute to get diagnosis with confidence for each input in the file.

To run the docker image first execute the following in cmd

1. docker build -t <name> .
2. docker run -p <port>:<port> <name>
3. run http://localhost:<port>/apidocs

FastAPI framework Version

For fastapi framework deployment version refer to bcd-fastapi

FlaskPyWebIO framework Version

For FlaskPyWebIO framewokr version refer to bcd-FlaskPyWebIO