logo added

app.py

@@ -8,11 +8,18 @@ from pybanking.EDA import data_analysis
 import sklearn.metrics as metrics
 from mlxtend.plotting import plot_confusion_matrix
 import streamlit.components.v1 as components
-import webbrowser
+from PIL import Image
 
-st.set_page_config(page_title="Customer Churn Prediction Model")
+st.set_page_config(page_title="Customer Churn Prediction Model", layout="wide")
 
-st.title('Customer Churn Prediction Model')
+col1,col2 = st.columns([1,2])
+
+with col1:
+     image = Image.open('Shorthills.png')
+     st.image(image)
+
+with col2:
+     st.title('Customer Churn Prediction Model')
 
 df = model_churn.get_data()
 
@@ -47,6 +54,12 @@ if option3 == 'SweetViz':
 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)
+     
      
 elif option3 == 'Pandas Profiling':
      res = analysis_class.pandas_analysis(df)
@@ -76,10 +89,6 @@ X, y = model_churn.preprocess_inputs(df, option)
 if option2 == 'Upload custom':
      model = model_churn.train(df, model)
 
-# st.subheader('This is the Preprocessed Data')
-
-# st.dataframe(X.head(5))
-
 y_pred = model.predict(X)
 
 st.write("Accuracy:",metrics.accuracy_score(y, y_pred))