Flask Tutorial

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ML Model Flask-Deployment

This is a demo project to elaborate how Machine Learn Models are deployed on production using Flask API

Prerequisites

• Scikit Learn
• Pandas
• Numpy
• Flask

Project Structure

This project has four major parts :

1. model.py - This contains code fot our Machine Learning model to predict employee salaries absed on trainign data in '50_Startup.csv' file.
2. app.py - This contains Flask APIs that receives employee details through GUI or API calls, computes the precited value based on our model and returns it.
3. request.py - This uses requests module to call APIs already defined in app.py and dispalys the returned value.
4. templates - This folder contains the HTML template to allow user to enter employee detail and displays the predicted employee salary.

Running the project

1. Ensure that you are in the project home directory. Create the machine learning model by running below command -

python model.py

This would create a serialized version of our model into a file model.pkl

2. Run app.py using below command to start Flask API

python app.py

By default, flask will run on http://127.0.0.1:5000/ (localhost)

3. Navigate to URL http://127.0.0.1:5000/ (localhost)

Contributors

_{Chirag Samal}
💻 📖 🚧 🤔 💬