Upload 4 files

app.py

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+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
+            }
+
+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"]
+
+def main():
+    st.title("Adult Income Analysis")
+
+    with st.form("questionaire"):
+        age = st.slider("Age", min_value=10, max_value=100)
+        workclass = st.selectbox("Workclass", unique_class)
+        education = st.selectbox("Education", unique_education)
+        Marital_Status = st.selectbox("Marital Status", unique_marital_status)
+        occupation = st.selectbox("Occupation", unique_occupation)
+        relationship = st.selectbox("Relationship", unique_relationship)
+        race = st.selectbox("Race", unique_race)
+        sex = st.selectbox("Sex", unique_sex)
+        hours_per_week = st.slider("Hours per week", min_value=1, max_value=100)
+        native_country = st.selectbox("Country", unique_country)
+
+        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 = '>50K' if result[0] == 1 else '<=50K'
+            st.success('The predicted income is {}'.format(result))
+
+if __name__=='__main__':
+    main()

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9058b70f3e0a5127bdc2a3c0084aa089b12980f942000f1477b19ef08b3c62f7
+size 284431

requirements.txt

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

unique_values.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6cf6f87cf9db4cc1fa51bed068a654b336484b543c8df6c454e012b187f5e6c7
+size 3546