app.py

import streamlit as st
import pandas as pd
import plotly.express as px

st.set_page_config(page_title="AutoML Streamlit App", page_icon=":robot:", layout="wide")

st.title("AutoML Streamlit App")

# Upload a CSV dataset
uploaded_file = st.file_uploader("Upload your dataset", type=["csv"])
if uploaded_file is not None:
    # Load the dataset and display the first 5 rows
    df = pd.read_csv(uploaded_file)
    st.dataframe(df.head())

    # Generate a treemap or sunburst plot based on data types
    numerical_cols = df.select_dtypes(include=["float", "int"]).columns
    categorical_cols = df.select_dtypes(include=["object"]).columns

    if st.button("Generate Plot"):
        if len(numerical_cols) >= 1:
            fig_hist = px.histogram(df, nbins=20, title="Histogram Plot")
            st.plotly_chart(fig_hist)
            
            fig_violin = px.violin(df, title="Violin Plot")
            st.plotly_chart(fig_violin)

            if len(numerical_cols) >= 2:
                fig_scatter = px.scatter_matrix(df, dimensions=numerical_cols, title="Scatter Matrix Plot")
                st.plotly_chart(fig_scatter)

                fig_ternary = px.line_ternary(df, a=numerical_cols[0], b=numerical_cols[1], c=numerical_cols[2], title="Line Ternary Plot")
                st.plotly_chart(fig_ternary)
        elif len(categorical_cols) >= 2:
            fig = px.treemap(df, path=categorical_cols, title="Treemap Plot")
            st.plotly_chart(fig)
        else:
            fig = px.sunburst(df, path=categorical_cols + numerical_cols, title="Sunburst Plot")
            st.plotly_chart(fig)