Update app.py

app.py

@@ -2,66 +2,46 @@ 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_gender =  unique_values["gender"]
-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("Workcalss", 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)
+    with st.form("questionnaire"):
+        age = st.slider("Age", min_value=10, max_value=100)
+        workclass = st.selectbox("Workclass", unique_class)
+        fnlwgt = st.selectbox("fnlwgt", unique_fnlwgt)
+        education = st.selectbox("Education", unique_education)
+        educational_num = st.selectbox("Educational Num", unique_education_num)
+        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)
+        gender = st.selectbox("Gender", unique_gender)
+        capital_gain = st.selectbox("Capital Gain", unique_capital_gain)
+        capital_loss = st.selectbox("Capital Loss", unique_capital_loss)
+        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],
-                                               "gender": [gender],
-                                               "sex": [sex],
-                                               "hours.per.week": [hours_per_week],
-                                               "native.country": [native_country]}))
-            
+            education_encoded = EDU_DICT.get(education, 0)  # Default to 0 if education is not found
+            result = model.predict(pd.DataFrame({
+                "age": [age],
+                "workclass": [workclass],
+                "fnlwgt": [fnlwgt],
+                "education": [education_encoded],
+                "educational-num": [educational_num],
+                "marital.status": [marital_status],
+                "occupation": [occupation],
+                "relationship": [relationship],
+                "race": [race],
+                "gender": [gender],
+                "capital_gain": [capital_gain],
+                "capital_loss": [capital_loss],
+                "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))
+              st.success('The predicted income is {}'.format(result))
 
-if __name__=='__main__':
+if __name__ == '__main__':
     main()