Update app_1.py

app_1.py

@@ -2,67 +2,56 @@ import joblib
 import pandas as pd
 import streamlit as st
 
-EDU_DICT = {'Preschool': 1,
-            '1st-4th': 2,
-            '5th-6th': 3,
-            '7th-8th': 4,
-            '9th': 5,
-            '10th': 6,
-            '11th': 7,
-            '12th': 8,
-            'HS-grad': 9, 
-            'Some-college': 10,
-            'Assoc-voc': 11,
-            'Assoc-acdm': 12,
-            'Bachelors': 13,
-            'Masters': 14,
-            'Prof-school': 15,
-            'Doctorate': 16
+purpose_1 = {'all_other': 1,
+            'credit_card': 2,
+            'debt_consolidation': 3,
+            'educational': 4,
+            'home_improvement': 5,
+            'major_purchase': 6,
+            'small_business': 7,
             }
 
-model = joblib.load('model.joblib')
-unique_values = joblib.load('unique_values.joblib')
-    
-unique_class =  unique_values["workclass"]
-unique_education =  unique_values["education"]
-unique_marital_status =  unique_values["marital.status"]
-unique_relationship =  unique_values["relationship"]
-unique_occupation =  unique_values["occupation"]
-unique_sex =  unique_values["sex"]
-unique_race = unique_values["race"]
-unique_country =  unique_values["native.country"]
+model = joblib.load('model_1.joblib')
+unique_values = joblib.load('unique_values_1.joblib')
+unique_Purpose =  unique_values["purpose"]
 
 def main():
-    st.title("Adult Income")
+    st.title("Loan Data")
 
     with st.form("questionaire"):
-        age = st.slider("Age", min_value=10, max_value=100)
-        workclass = st.selectbox("Workclass", options = unique_class)
-        education = st.selectbox("Education", options = unique_education)
-        Marital_Status = st.selectbox("Marital Status", options = unique_marital_status)
-        occupation = st.selectbox("Occupation", options = unique_occupation)
-        relationship = st.selectbox("Relationship", options = unique_relationship)
-        race = st.selectbox("Race", options = unique_race)
-        sex = st.selectbox("Sex", options = unique_sex)
-        hours_per_week = st.slider("Hours per week", min_value=1, max_value=100)
-        native_country = st.selectbox("Native country", options = unique_country)
-
+        purpose = st.selectbox("Purpose", options = unique_Purpose )
+        int_rate = st.slider("The interest rate of the loan", 0.0000,1.0000) 
+        installments = st.number_input("The monthly installments owed") 
+        log_annual_inc  = st.number_input("The natural log of the self-reported annual income of the borrower")
+        dti = st.number_input("The debt to income ratio of the borrower")
+        fico = st.slider("The FICO credit score of the borrower.", 0,1000)
+        days_with_cr_line = st.number_input("The number of days the borrower has had a credit line.")
+        revol_bal = st.number_input("The borrower's revolving balance")
+        revol_util= st.number_input("The borrower's revolving line utilization rate")
+        inq_last_6mths= st.slider("The borrower's number of inquiries by creditors in the last 6 months.", 0,100)
+        delinq_2yrs = st.slider("The number of times the borrower had been 30+ days past due on a payment in the past 2 year", 0,100 )
+        pub_rec= st.slider("The borrower's number of derogatory public records", 0,100 )
+        not_fully_paid =  st.slider("not fully paid.", 0,100)
+       
         # clicked==True only when the button is clicked
         clicked = st.form_submit_button("Predict income")
         if clicked:
-            result=model.predict(pd.DataFrame({"age": [age],
-                                               "workclass": [workclass],
-                                               "education": [EDU_DICT[education]],
-                                               "marital.status": [Marital_Status],
-                                               "occupation": [occupation],
-                                               "relationship": [relationship],
-                                               "race": [race],
-                                               "sex": [sex],
-                                               "hours.per.week": [hours_per_week],
-                                               "native.country": [native_country]}))
+            result=model.predict(pd.DataFrame({"purpose": [purpose_1],
+                                               "int.rate": [int_rate],
+                                               "installment": [installments],
+                                               "log.annual.inc": [log_annual_inc],
+                                               "dti": [dti],
+                                               "fico": [fico],
+                                               "days.with.cr.line": [days_with_cr_line],
+                                               "revol.bal": [revol_bal],
+                                               "revol.util": [revol_util],
+                                               "inq.last.6mths": [inq_last_6mths]
+                                               "delinq.2yrs": [delinq_2yrs]
+                                               "pub.rec": [pub_rec]
+                                               "not.fully.paid": [not_fully_paid]}))
             # Show prediction
-            result = '>50K' if result[0] == 1 else '<=50K'
-            st.success("Your predicted income is "+result) #แสดงผล
+            result = 'Pass' if result[0] == 1 else 'Not Pass'
+            st.success("Your predicted loan is "+result) #แสดงผล
             
 # Run main()
 if __name__ == "__main__":