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app.py

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+import joblib
+import pandas as pd
+import streamlit as st 
+
+ANAE_DIC =  {'NO': 0, 'YES': 1}
+DIA_DIC = {'NO': 0, 'YES': 1}
+HIGHBLOOD_DIC = {'NO': 0, 'YES': 1}
+SMOKE_DIC = {'NO': 0, 'YES': 1}
+DEATH_DIC = {'NO': 0, 'YES': 1}
+
+model = joblib.load('model (1).joblib')
+unique_values = joblib.load('unique_values (1).joblib')
+    
+unique_age =  unique_values["age"]
+unique_anaemia =  unique_values["anaemia"]
+unique_creatinine =  unique_values["creatinine_phosphokinase"]
+unique_diabetes =  unique_values["diabetes"]
+unique_ejection =  unique_values["ejection_fraction"]
+unique_high_blood =  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_smoking = unique_values["smoking"]
+unique_time =  unique_values["time"]
+
+def main():
+    st.title("Adult Income Analysis")
+
+    with st.form("questionaire"):
+        age = st.slider("Age", min_value=10, max_value=100)
+        anaemia = st.selectbox("Anaemia", unique_anaemia)
+        creatinine = st.slider("Creatinine", min_value=10, max_value=10000)
+        diabetes = st.selectbox("Diabetes", unique_diabetes)
+        ejection = st.slider("Ejection(Percent)", min_value=1, max_value=100)
+        high_blood = st.selectbox("High blood pressure", unique_high_blood)
+        platelets = st.slider("Platelets", min_value=10000, max_value=1000000)
+        serum_creatinine = st.slider("Serum creatinine", min_value=0, max_value=10)
+        sodium = st.slider("Serum Sodium", min_value=100, max_value=200)
+        sex = st.selectbox("Sex", unique_sex)
+        smoking = st.selectbox("Smoke", unique_smoking)
+        time = st.slider("Time", min_value=10, max_value=100)
+
+        clicked = st.form_submit_button("Predict income")
+        if clicked:
+            result=model.predict(pd.DataFrame({"age": [age],
+                                               "anaemia": [ANAE_DIC[anaemia]],
+                                               "creatinine": [creatinine],
+                                               "diabetes": [DIA_DIC[diabetes]],
+                                               "ejection": [ejection],
+                                               "high_blood": [HIGHBLOOD_DIC[high_blood]],
+                                               "platelets": [platelets],
+                                               "serum_creatinine": [serum_creatinine],
+                                               "serum_sodium": [sodium],
+                                               "sex": [sex],
+                                               "smoking": [SMOKE_DIC[smoking]],
+                                               "time": [time]}))
+            result = 'No' if result[0] == 1 else 'Yes'
+            st.success('The predicted Death event is {}'.format(result))
+
+if __name__=='__main__':
+    main()

model (1).joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:26bca3537397b211972d4d035852111c6eb103e05e7a8eae7d3125dd016c8899
+size 126025

requirements.txt

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

unique_values (1).joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:48cb7c8db3b026991810ab071fd1bce2acbe3bc66d4c527696061eb48003570a
+size 544