updated analysis

app.py

@@ -10,7 +10,7 @@ from mlxtend.plotting import plot_confusion_matrix
 import streamlit.components.v1 as components
 from PIL import Image
 
-st.set_page_config(page_title="Customer Churn Prediction Model", layout="wide")
+st.set_page_config(page_title="Customer Churn Prediction Model", layout="wide") # , layout="wide"
 
 col1,col2 = st.columns([1,2])
 
@@ -44,25 +44,33 @@ analysis_class = data_analysis.Analysis()
 
 option3 = st.selectbox(
      'Select Exploratory Data Analysis type',
-     ['None', 'DataPrep', 'SweetViz',]
+     ['None', 'DataPrep', 'SweetViz', 'PandasProfiling']
      )
 
 if option3 == 'SweetViz':
      res = analysis_class.sweetviz_analysis(df)
-     res.show_html(open_browser=True, layout='widescreen', scale=None)
+     res.show_html(filepath='SweetViz.html', open_browser=True, layout='widescreen', scale=None)
+     HtmlFile = open('SweetViz.html', 'r', encoding='utf-8')
+     source_code = HtmlFile.read()
+     with st.expander("See Report"):
+          components.html(source_code, height=1000, scrolling=True)
 
 elif option3 == 'DataPrep':
      res = analysis_class.dataprep_analysis(df)
-     res.show_browser()
-     # res.save('DataPrep.html')
-     # HtmlFile = open('DataPrep.html', 'r', encoding='utf-8')
-     # source_code = HtmlFile.read()
-     # with st.expander("See Report"):
-     #      components.iframe(source_code, height=1000)
+     # res.show_browser()
+     res.save('DataPrep.html')
+     HtmlFile = open('DataPrep.html', 'r', encoding='utf-8')
+     source_code = HtmlFile.read()
+     with st.expander("See Report"):
+          components.html(source_code, height=1000, scrolling=True)
      
-     
-elif option3 == 'Pandas Profiling':
+elif option3 == 'PandasProfiling':
      res = analysis_class.pandas_analysis(df)
+     res.to_file("PandasProfiling.html")
+     HtmlFile = open('PandasProfiling.html', 'r', encoding='utf-8')
+     source_code = HtmlFile.read()
+     with st.expander("See Report"):
+          components.html(source_code, height=1000, scrolling=True)
 
 
 model_names = [