Update app.py

app.py

@@ -1,12 +1,10 @@
-
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import cx_Oracle as ora
-import pandas as pd
-from pandas_profiling import ProfileReport
 
-st.title("File Upload and Profiling")
+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"])
@@ -18,13 +16,13 @@ if uploaded_file is not None:
     # 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 len(numerical_cols) >= 2:
         fig = px.scatter_matrix(df, dimensions=numerical_cols)
         st.plotly_chart(fig)
     elif len(categorical_cols) >= 2:
-        fig = px.treemap(df, path=['CD_CDNG_C', 'RECORDCOUNTUNIQUEASSESSMENTLOINC'])
+        fig = px.treemap(df, path=categorical_cols)
         st.plotly_chart(fig)
     else:
-        fig = px.sunburst(df, path=['CD_CDNG_C', 'RECORDCOUNTUNIQUEASSESSMENTLOINC'])
-        st.plotly_chart(fig)
+        fig = px.sunburst(df, path=categorical_cols + numerical_cols)
+        st.plotly_chart(fig)