Update app.py

app.py

@@ -1,65 +1,67 @@
 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
+EDU_DICT = {'unknown': 1,
+            'secondary': 2,
+            'primary': 3,
+            'tertiary': 4,
             }
 
-model = #
-unique_values = #
+model = joblib.load('model.joblib')
+unique_values = joblib.load('unique_values.joblib')
     
-unique_class =  unique_values["workclass"]
+unique_job =  unique_values["job"]
+unique_marital =  unique_values["marital"]
+unique_default =  unique_values["default"]
+unique_housing =  unique_values["housing"]
+unique_loan =  unique_values["loan"]
+unique_contact =  unique_values["contact"]
+unique_month = unique_values["month"]
+unique_poutcome =  unique_values["poutcome"]
 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"]
-
+ 
 def main():
-    st.title("Adult Income")
+    st.title("Bank Marketing")
 
     with st.form("questionaire"):
-        age = # user's input
-        workclass = # user's input
-        education = # user's input
-        Marital_Status = # user's input
-        occupation = # user's input
-        relationship = # user's input
-        race = # user's input
-        sex = # user's input
-        hours_per_week = # user's input
-        native_country = # user's input
-
+        age = st.slider("Age", min_value=15, max_value=100)
+        job = st.selectbox("Job", options = unique_job)
+        marital = st.selectbox("Marital", options = unique_marital)
+        education = st.selectbox("Education", options = unique_education)
+        default = st.selectbox("Default", options = unique_default)
+        balance = st.slider("Balance", min_value=-9000, max_value=200000) 
+        housing = st.selectbox("Housing", options = unique_housing)
+        loan = st.selectbox("Loan", options = unique_loan)
+        contact = st.selectbox("Contact", options = unique_contact)
+        day = st.slider("Day", min_value=1, max_value=50) 
+        month = st.selectbox("Month", options = unique_month)
+        duration = st.slider("Duration", min_value=0, max_value=5000) 
+        campaign = st.slider("Campaign", min_value=1, max_value=100) 
+        pdays = st.slider("Pdays", min_value=-10, max_value=1000) 
+        previous = st.slider("Previous", min_value=0, max_value=300) 
+        poutcome = st.selectbox("Poutcome", options = unique_poutcome)
+      
         # clicked==True only when the button is clicked
-        clicked = st.form_submit_button("Predict income")
+        clicked = st.form_submit_button("Predict y")
         if clicked:
             result=model.predict(pd.DataFrame({"age": [age],
-                                               "workclass": [workclass],
+                                               "job": [job],
+                                               "marital": [marital],
                                                "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]}))
+                                               "default": [default],
+                                               "balance": [balance],
+                                               "housing": [housing],
+                                               "loan": [loan],
+                                               "contact": [contact],
+                                               "day": [day]
+                                               "month": [month]
+                                               "duration": [duration]
+                                               "campaign": [campaign]
+                                               "pdays": [pdays]
+                                               "previous": [previous]
+                                               "poutcome": [poutcome]}))
+                                               
             # Show prediction
 
 # Run main()