app.py

app.py

@@ -1,54 +0,0 @@
-from ssl import Options
-import joblib
-import pandas as pd
-import streamlit as st
-
-
-model = joblib.load('model (1).joblib')
-unique_values = joblib.load('unique_values (2).joblib')
-    
-
-#unique_post_on =  unique_values["Posted On"]
-unique_fea_1 = unique_values["fea_1"]
-#unique_fea_2 = unique_values["fea_2"]
-unique_fea_3 = unique_values["fea_3"]
-#unique_fea_4 = unique_values["fea_4"]
-unique_fea_5 = unique_values["fea_5"]
-unique_fea_6 = unique_values["fea_6"]
-unique_fea_7 = unique_values["fea_7"]
-#unique_fea_8 = unique_values["fea_8"]
-unique_fea_9 = unique_values["fea_9"]
-#unique_fea_10 = unique_values["fea_10"]
-#unique_fea_11 = unique_values["fea_11"]
-
-
-def main():
-    st.title("Customer check risk")
-
-    with st.form("questionaire"):
-        fea1 = st.slider(min_value = 2, max_value=7 )
-        fea3 = st.slider(min_value = 1, max_value=2 )
-        fea5 = st.selectbox(options =unique_fea_5 )
-        fea6 = st.slider(min_value=3, max_value = 15 )
-        fea7 = st.slider(min_value=-1, max_value =10 )
-        fea9 = st.selectbox(options =unique_fea_9 )
-
-        # clicked==True only when the button is clicked
-        clicked = st.form_submit_button("credit risk")
-        if clicked:
-            result=model.predict(pd.DataFrame({"fea1": [fea1],
-                                               "fea3": [fea3],                                               
-                                               "fea5": [fea5],
-                                               "fea6": [fea6],
-                                               "fea7": [fea7],
-                                               "fea9": [fea9]
-                                               }))
-            # Show prediction
-            if result==1:
-                result='the customer is in high credit risk'
-            else :
-                result='the customer is in low credit risk'
-            st.success('Your risk is '+result)
-# Run main()
-if __name__=='__main__':
-    main()