eda.py

import numpy as np
import pandas as pd
import streamlit as st
from pandas_profiling import ProfileReport
from streamlit_pandas_profiling import st_profile_report

# Web App Title
st.markdown('''
# **The Exploratory Data Analysis (EDA) App**

Designed and built for Research purposes by **CHAKRADAR M** under the guidance of *Dr.Alok Aggarwal*

---
''')

# Upload CSV data
with st.sidebar.header('1. Upload your CSV data'):
    uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"])
    st.sidebar.markdown("""
[Example CSV input file](https://raw.githubusercontent.com/chakree10/EDA-web-app/main/Boston.csv)
""")

# Pandas Profiling Report
if uploaded_file is not None:
    @st.cache
    def load_csv():
        csv = pd.read_csv(uploaded_file)
        return csv
    df = load_csv()
    pr = ProfileReport(df, explorative=True)
    st.header('**Input DataFrame**')
    st.write(df)
    st.write('---')
    st.header('**Statistics of Data its Distributions and Relationships Report**')
    st_profile_report(pr)
else:
    st.info('Awaiting for CSV file to be uploaded.')
    if st.button('Press to use Example Dataset'):
        # Example data
        @st.cache
        def load_data():
            a = pd.DataFrame(
                np.random.rand(100, 5),
                columns=['a', 'b', 'c', 'd', 'e']
            )
            return a
        df = load_data()
        pr = ProfileReport(df, explorative=True)
        st.header('**Input DataFrame**')
        st.write(df)
        st.write('---')
        st.header('**Pandas Profiling Report**')
        st_profile_report(pr)

link1 = '[Regression-Problem](https://share.streamlit.io/chakree10/regression/main/regressor.py)'
st.markdown(link1, unsafe_allow_html=True)

link2 = '[Classification-Problem](https://share.streamlit.io/chakree10/classification/main/classifier.py)'
st.markdown(link2, unsafe_allow_html=True)