Update app.py

app.py

@@ -1,7 +1,14 @@
 import streamlit as st
 import pandas as pd
 from utils.data_cleaning import handle_missing_values, remove_outliers_iqr, cap_extreme_values
-from utils.visualizations import plot_correlation_heatmap
+from utils.visualizations import (
+    plot_correlation_heatmap,
+    plot_histogram,
+    plot_box_plot,
+    plot_pair_plot,
+    plot_scatter_plot,
+    plot_bar_plot,
+)
 from utils.model_training import train_all_models
 import io
 
@@ -54,6 +61,51 @@ if uploaded_file is not None:
             mime="image/png"
         )
 
+        # Additional Visualizations
+        st.subheader("Additional Visualizations")
+
+        numeric_columns = df_cleaned.select_dtypes(include=['float64', 'int64']).columns.tolist()
+        categorical_columns = df_cleaned.select_dtypes(include=['object']).columns.tolist()
+
+        # Distribution Plot
+        if numeric_columns:
+            st.write("### Distribution Plots (Histograms)")
+            for col in numeric_columns:
+                st.write(f"#### {col}")
+                hist_plot = plot_histogram(df_cleaned, col)
+                st.pyplot(hist_plot)
+
+        # Box Plot
+        if numeric_columns:
+            st.write("### Box Plots (Outlier Detection)")
+            for col in numeric_columns:
+                st.write(f"#### {col}")
+                box_plot = plot_box_plot(df_cleaned, col)
+                st.pyplot(box_plot)
+
+        # Pair Plot
+        if len(numeric_columns) > 1:
+            st.write("### Pair Plot")
+            pair_plot = plot_pair_plot(df_cleaned)
+            st.pyplot(pair_plot)
+
+        # Scatter Plot
+        if len(numeric_columns) > 1:
+            st.write("### Scatter Plot")
+            x_col = st.selectbox("Select X-axis for Scatter Plot", numeric_columns)
+            y_col = st.selectbox("Select Y-axis for Scatter Plot", numeric_columns)
+            if x_col and y_col:
+                scatter_plot = plot_scatter_plot(df_cleaned, x_col, y_col)
+                st.pyplot(scatter_plot)
+
+        # Bar Plot
+        if categorical_columns:
+            st.write("### Bar Plots (For Categorical Data)")
+            for col in categorical_columns:
+                st.write(f"#### {col}")
+                bar_plot = plot_bar_plot(df_cleaned, col)
+                st.pyplot(bar_plot)
+
         # Select Target and Features
         st.subheader("Feature and Target Selection")
         target = st.selectbox("Select Target Variable", df_cleaned.columns)