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Streamlit based Web App

Contents

  1. Exporting the Model
  2. Creating the Web Application
  3. Hosting the Web Application
  4. References

Exporting the Model

There are many libraries/approaches that can help us in this

  1. Pickle Approach
  2. Joblib Approach
  3. Manual Save and Restore to JSON approach
If satisfied with the results, Proceed!
Dumping the model
Downloading after Committing

Creating the Web Application

There are many options to create a web application like Flask, Django etc but the latest and most apt for making a ML interface is Streamlit.

Importing the Model
Defining Input Fields.
Inputs to Model

Hosting the Web Application

Pythonanywhere, Zeit, Amazon Cloud Services etc are some services where we can host our web application. I personally like heroku for the same as it provides free hosting even with custom domain but on the flipside, it isn’t the fastest and initial setup is not the smoothest.

  1. A ProcFile
  2. requirements.txt(case sensitive)
  3. setup.sh
Just change the name of the app after cloning
Connecting with the Repo

References

Some of the best places to learn streamlit (1) (2) (3)

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