Update app.py

app.py

@@ -1,33 +1,39 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import pandas_profiling
 
-@st.cache
-def load_data(file):
-    return pd.read_csv(file)
+st.set_page_config(page_title="AutoML Streamlit App", page_icon=":robot:", layout="wide")
 
-def main():
-    st.set_page_config(page_title="AutoML", page_icon=":robot:", layout="wide")
-    st.title("AutoML Streamlit Application")
-    file = st.file_uploader("Upload your CSV file", type=["csv"])
-    if file is not None:
-        df = load_data(file)
-        st.dataframe(df.head(10))
-        st.write("Statistics:")
-        st.write(df.describe())
-        st.write("Data Distribution:")
-        st.write(df.hist())
-        st.write("Pandas Profiling:")
-        profile = df.profile_report(title="Pandas Profiling Report")
-        st.write(profile)
-        st.write("Pandas Profiling (Sunburst Chart):")
-        sunburst = profile.get_final_html()
-        st.write(sunburst, unsafe_allow_html=True)
-        st.write("Filter by Column:")
-        column = st.selectbox("Select Column", df.columns)
-        filtered = df.sort_values(by=column)
-        st.write(filtered)
+st.title("AutoML Streamlit App")
 
-if __name__ == '__main__':
-    main()
+# 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)