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import joblib.py

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+import joblib
+import pandas as pd
+import streamlit as st 
+
+slope_DICT = {'normal':0,
+              'fixed defect':1,
+              'reversable defect':2
+            }
+
+model = joblib.load('model_1.joblib')
+unique_values = joblib.load('unique_values_1.joblib')
+    
+unique_sex =  unique_values["sex"]
+unique_slope =  unique_values["slope"]
+unique_fbs = unique_values["fbs"]
+unique_exang = unique_values["exang"]
+
+
+def main():
+    st.title("Heart Disease Analysis")
+
+    with st.form("questionaire"):
+        sex = st.selectbox("Sex", unique_sex)
+        age = st.slider("Age", min_value=10, max_value=100)
+        cp = st.slider("cp", min_value=0, max_value=3)
+        trestbps = st.slider("trestbps", min_value=90, max_value=200)
+        chol = st.slider("chol", min_value=120, max_value=570)
+        fbs = st.selectbox("fbs", unique_fbs)
+        restecg = st.slider("restecg", min_value=0, max_value=2)
+        thalach	 = st.slider("thalach", min_value=70, max_value=210)
+        exang = st.selectbox("exang",unique_exang )
+        oldpeak = st.slider("oldpeak", min_value=0, max_value=7)
+        slope = st.selectbox("slope", unique_slope)
+        ca = st.slider("ca", min_value=0, max_value=3)
+        thal = st.slider("ca", min_value=0, max_value=3)
+        
+
+        clicked = st.form_submit_button("Predict Disease")
+        if clicked:
+            result=model.predict(pd.DataFrame({"sex": [sex],
+                                               "age": [age],
+                                               "cp": [cp],
+                                               "trestbps": [trestbps],
+                                               "chol": [chol],
+                                               "fbs": [fbs],
+                                               "restecg": [restecg],
+                                               "thalach": [thalach],
+                                               "exang": [exang],
+                                               "oldpeak": [oldpeak],
+                                               "slope": [slope_DICT[slope]],
+                                               "ca": [ca],
+                                               "thal": [thal]
+                                               }))
+            result = 'disease' if result[0] == 1 else 'no disease'
+            st.success('The predicted heart disease is {}'.format(result))
+
+if __name__=='__main__':
+    main()

model_1.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:411d378491df1cf609d9bdca335abc8221a7e2f693620119e831b999f52c05ba
+size 1117215

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 sha256:68131164199551731df29aaf451c1b689a14ee9de913e908bb95e04a9ffd9c18
+size 3922