models added

app.py

@@ -1,21 +1,57 @@
 import streamlit as st
-from pybanking.churn_prediction import model_churn
+import streamlit.components.v1 as components
+import pandas as pd
 import pickle
+import matplotlib.pyplot as plt
+from pybanking.churn_prediction import model_churn
+from pybanking.EDA import data_analysis 
 import sklearn.metrics as metrics
 from mlxtend.plotting import plot_confusion_matrix
-import matplotlib.pyplot as plt
+import streamlit.components.v1 as components
+import webbrowser
 
 st.set_page_config(page_title="Customer Churn Prediction Model")
 
 st.title('Customer Churn Prediction Model')
-# x = st.slider('Select a value')
-
-st.subheader('This is the Sample Data')
 
 df = model_churn.get_data()
 
+option2 = st.selectbox(
+     'Which dataset would you like to use for prediction?',
+     ['Sample Dataset', 'Upload Custom']
+     )
+
+if option2 == 'Upload Custom':
+     file = st.file_uploader("Choose a file")
+     if file is not None:
+          #read csv
+          df = pd.read_csv(file)
+     else:
+          st.warning("you need to upload a csv file.")
+
+st.subheader('This is the Selected Data')
+
 st.dataframe(df.head(5))
 
+analysis_class = data_analysis.Analysis()
+
+option3 = st.selectbox(
+     'Select Exploratory Data Analysis type',
+     ['None', 'DataPrep', 'SweetViz',]
+     )
+
+if option3 == 'SweetViz':
+     res = analysis_class.sweetviz_analysis(df)
+     res.show_html(open_browser=True, layout='widescreen', scale=None)
+
+elif option3 == 'DataPrep':
+     res = analysis_class.dataprep_analysis(df)
+     res.show_browser()
+     
+elif option3 == 'Pandas Profiling':
+     res = analysis_class.pandas_analysis(df)
+
+
 model_names = [
      "Logistic_Regression",
      "Support_Vector_Machine",
@@ -31,21 +67,18 @@ option = st.selectbox(
      model_names
      )
 
+model = pickle.load(open(option+'.pkl', 'rb'))
+
 st.write("Model Loaded :  ", option)
 
 X, y = model_churn.preprocess_inputs(df, option)
 
-model = pickle.load(open(option+'.pkl', 'rb'))
-
-option2 = st.selectbox(
-     'Which dataset would you like to use for prediction?',
-     ['Sample Dataset', 'Upload Custom']
-     )
-
 if option2 == 'Upload custom':
      model = model_churn.train(df, model)
 
-st.dataframe(X.head(5))
+# st.subheader('This is the Preprocessed Data')
+
+# st.dataframe(X.head(5))
 
 y_pred = model.predict(X)