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.
The following links were used to implement the multi-page functionality in streamlit:
- https://docs.streamlit.io/get-started/tutorials/create-a-multipage-app
- https://docs.streamlit.io/library/advanced-features/multipage-apps
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
The classification results are displayed in the streamlit app in a separate page and look like the following:
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