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Interactive web-app using Streamlit and FastAPI back-end: Exploratory data analysis (EDA) and classification model integration

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FastAPI-Streamlit: Interactive web-app - EDA and classification model integration

This repository contains a comprehensive project that performs Exploratory Data Analysis (EDA) and classification tasks on the Iris dataset. The project utilizes FastAPI for creating a robust API endpoints, and Streamlit for building an interactive web application.

The goal of this project is to provide a hands-on approach to understanding data analysis and machine learning workflows while demonstrating the use of FastAPI and Streamlit in a practical context.

Streamlit Multi-Page Functionality

The following links were used to implement the multi-page functionality in streamlit:

Run Streamlit App

To run the streamlit app, first FastAPI app should be running. To run the FastAPI app, run the following command:

uvicorn main:app --reload

Once the FastAPI app is running, run the following command to run the streamlit app:

streamlit run app/Home.py

OR, you can run the app using make command:

make run_app

It will run both FastAPI and Streamlit apps, and open the streamlit app in the browser.

Cloud Deployment

This streamlit app is deployed on streamlit cloud. You can access the deployed app using the following link:

Streamlit App

Classification Results

The classification results are displayed in the streamlit app on a separate page and look like the following:

Classification Results

Requirements

requirements.yml file contains the list of all the packages required to run the code in this repository. requirements.yml is generated using the following command:

conda env export --no-builds | grep -v "prefix" > requirements.yml

To create a conda environment using the requirements.yml file, run the following command:

conda env create -f requirements.yml

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Interactive web-app using Streamlit and FastAPI back-end: Exploratory data analysis (EDA) and classification model integration

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