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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) |