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app.py

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+import joblib
+import pandas as pd
+import streamlit as st 
+
+
+model = joblib.load('model (1).joblib')
+unique_values = joblib.load('unique_values (1).joblib')
+    
+unique_Married_Single =  unique_values["Married/Single"]
+unique_House_Ownership =  unique_values["House_Ownership"]
+unique_Car_Ownership =  unique_values["Car_Ownership"]
+unique_Profession =  unique_values["Profession"]
+unique_CITY =  unique_values["CITY"]
+unique_STATE =  unique_values["STATE"]
+
+
+def main():
+    st.title("Loan Risk_Flag Analysis")
+
+    with st.form("questionaire"):
+        Income = st.slider("Income", min_value=10000, max_value=9999999)
+        Age = st.slider("Age", min_value=10, max_value=100)
+        Experience = st.slider("Experience", min_value=0, max_value=20)
+        CURRENT_JOB_YRS = st.slider("CURRENT_JOB_YRS", min_value=0, max_value=14)
+        CURRENT_HOUSE_YRS = st.slider("CURRENT_HOUSE_YRS", min_value=10, max_value=14)
+
+        Married_Single = st.selectbox("Married/Single", unique_Married_Single)
+        House_Ownership = st.selectbox("House_Ownership", unique_House_Ownership)
+        Car_Ownership = st.selectbox("Car_Ownership", unique_Car_Ownership)
+        Profession = st.selectbox("Profession", unique_Profession)
+        CITY = st.selectbox("CITY", unique_CITY)
+        STATE = st.selectbox("STATE", unique_STATE)
+        
+        
+
+        clicked = st.form_submit_button("Predict Risk_Flag")
+        if clicked:
+            result=model.predict(pd.DataFrame({"Income": [Income],
+                                               "Age": [Age],
+                                               "Experience": [Experience],
+                                               "CURRENT_JOB_YRS": [CURRENT_JOB_YRS],
+                                               "CURRENT_HOUSE_YRS": [CURRENT_HOUSE_YRS],
+                                               "Married_Single": [Married_Single],
+                                               "House_Ownership": [House_Ownership],
+                                               "Car_Ownership": [Car_Ownership],
+                                               "Profession": [Profession],
+                                               "CITY": [CITY],
+                                               "STATE": [STATE]}))
+            result = 'none_risk_flag' if result[0] == 1 else 'risk_flag'
+            st.success('The predicted income is {}'.format(result))
+
+if __name__=='__main__':
+    main()

requirements.txt

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+joblib
+pandas
+scikit-learn==1.2.2
+xgboost==1.7.6
+altair<5