Update app.py

app.py

@@ -3,67 +3,58 @@ 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_age =  unique_values["age"]
+unique_anaemia =  unique_values["anaemia"]
+unique_creatinine_phosphokinase =  unique_values["creatinine_phosphokinase"]
+unique_diabetes =  unique_values["relationship"]
+unique_ejection_fraction =  unique_values["ejection_fraction"]
+unique_high_blood_pressure =  unique_values["high_blood_pressure"]
+unique_platelets = unique_values["platelets"]
+unique_serum_creatinine =  unique_values["serum_creatinine"]
+unique_serum_sodium =  unique_values["serum_sodium"]
 unique_sex =  unique_values["sex"]
-unique_race = unique_values["race"]
-unique_country =  unique_values["native.country"]
+unique_smoking =  unique_values["smoking"]
+unique_time =  unique_values["time"]
+
 
 def main():
-    st.title("Adult Income")
+    st.title("Predict death rates from heart failure")
 
     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)
+        age = st.slider("Age", min_value=0, max_value=100)
+        anaemia = st.selectbox("anaemia", options= unique_anaemia)
+        creatinine_phosphokinase = st.slider("creatinine_phosphokinase", min_value=0, max_value=100)
+        diabetes = st.selectbox("diabetes", options=unique_diabetes)
+        ejection_fraction = st.slider("ejection_fraction", min_value=0, max_value=100)
+        high_blood_pressure = st.selectbox("high_blood_pressure", options= unique_high_blood_pressure)
+        platelets = st.slider("platelets", min_value=0, max_value=100)
+        serum_creatinine = st.slider("serum_creatinine", min_value=0, max_value=100)
+        serum_sodium = st.slider("serum_sodium", min_value=1, max_value=100)
+        sex = st.selectbox("sex", options= unique_sex)
+        smoking = st.slider("smoking",options= unique_smoking)
+        time= st.slider("time", options= unique_time)
 
         # clicked==True only when the button is clicked
-        clicked = st.form_submit_button("Predict income")
+        clicked = st.form_submit_button("Predict death rates")
         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],
+                                               "anaemia": [anaemia],
+                                               "creatinine_phosphokinase": [creatinine_phosphokinase],
+                                               "diabetes": [diabetes],
+                                               "ejection_fraction": [ejection_fraction],
+                                               "high_blood_pressure": [high_blood_pressure],
+                                               "platelets": [platelets],
+                                               "serum_creatinine": [serum_creatinine],
+                                               "serum_sodium ": [serum_sodium ],
                                                "sex": [sex],
-                                               "hours.per.week": [hours_per_week],
-                                               "native.country": [native_country]}))
+                                               "smoking": [smoking],
+                                               "time": [time]}))
             # Show prediction
-            result = '>50K' if result[0] == 1 else '<=50K'
-            st.success("Your prediction income is ", result)
+            result = '1' if result[0] == 1 else '0'
+            st.success("Your prediction death is ", result)
 
 # Run main()
 if __name__ == "__main__":