Upload 4 files

app (2).py

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+import joblib
+import pandas as pd
+import streamlit as st 
+
+EDU_DICT = {'18-24': 1,
+            '25-29': 2,
+            '30-34': 3,
+            '35-39': 4,
+            '40-44': 5,
+            '45-49': 6,
+            '50-54': 7,
+            '55-59': 8,
+            '60-64': 9,
+            '65-69': 10,
+            '70-74': 11,
+            '75-79': 12,
+            '80+': 13
+            }
+
+
+model = joblib.load('model_n.joblib')
+unique_values = joblib.load('unique_values_n.joblib')
+    
+#unique_General_Health =  unique_values["General_Health"]
+unique_Checkup =  unique_values["Checkup"]
+unique_Exercise =  unique_values["Exercise"]
+unique_Heart_Disease =  unique_values["Heart_Disease"]
+unique_Skin_Cancer =  unique_values["Skin_Cancer"]
+unique_Other_Cancer =  unique_values["Other_Cancer"]
+unique_Depression = unique_values["Depression"]
+unique_Diabetes =  unique_values["Diabetes"]
+unique_Arthritis =  unique_values["Arthritis"]
+unique_Sex =  unique_values["Sex"]
+unique_Age_Category =  unique_values["Age_Category"]
+unique_Smoking_History =  unique_values["Smoking_History"]
+
+
+
+def main():
+    st.title("General Health analysis")
+
+    with st.form("questionaire"):
+        
+        Checkup = st.selectbox("Checkup", unique_Checkup)
+        Exercise = st.selectbox("Exercise", unique_Exercise)
+        Heart_Disease = st.selectbox("Heart Disease", unique_Heart_Disease)
+        Skin_Cancer = st.selectbox("Skin Cancer", unique_Skin_Cancer)
+        Other_Cancer = st.selectbox("Other Cancer", unique_Other_Cancer)
+        Depression = st.selectbox("Depression", unique_Depression)
+        Diabetes = st.selectbox("Diabetes", unique_Diabetes)
+        Arthritis = st.selectbox("Arthritis", unique_Arthritis)
+        Sex = st.selectbox("Sex", unique_Sex)
+        Age_Category = st.selectbox("Age Category", unique_Age_Category)
+        BMI = st.slider("BMI", min_value=1, max_value=50)
+        Smoking_History = st.selectbox("Smoking History", unique_Smoking_History)
+        Alcohol_Consumption = st.slider("Alcohol Consumption", min_value=0, max_value=100)
+        Fruit_Consumption = st.slider("Fruit Consumption", min_value=0, max_value=100)
+        Green_Vegetables_Consumption = st.slider("Green Vegetables Consumption", min_value=0, max_value=100)
+        FriedPotato_Consumption = st.slider("Fried Potato Consumption", min_value=0, max_value=100)
+        Weight_ == st.slider("Weight_(kg)", min_value=100, max_value=200)
+        Height_ == st.slider("Height_(cm)", min_value=100, max_value=200)
+        
+
+        
+        
+        clicked = st.form_submit_button("Predict General Health")
+        if clicked:
+            result=model.predict(pd.DataFrame({"Checkup": [Checkup],
+                                               "Exercise": [Exercise],
+                                               "Heart_Disease": [Heart_Disease],
+                                               "Skin_Cancer": [Skin_Cancer],
+                                               "Other_Cancer": [Other_Cancer],
+                                               "Depression": [Depression],
+                                               "Diabetes": [Diabetes],
+                                               "Arthritis": [Arthritis],
+                                               "Sex": [Sex],
+                                               "Age_Category": [EDU_DICT[Age_Category]],
+                                               "BMI": [BMI],
+                                               "Smoking_History": [Smoking_History],
+                                               "Alcohol_Consumption": [Alcohol_Consumption],
+                                               "Fruit_Consumption": [Fruit_Consumption],
+                                               "Green_Vegetables_Consumption": [Green_Vegetables_Consumption],
+                                               "FriedPotato_Consumption": [FriedPotato_Consumption],
+                                               "Weight_(kg)": [Weight_],
+                                               "Height_(cm)": [Height_]}))
+            result = 'Very Good' if result[0] == 1 else 'Poor'
+            st.success('The predicted General Health is {}'.format(result))
+
+if __name__=='__main__':
+    main()

model_n.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:768211c97b9fb1cdadf6ddc106dc17099267d7c46f91239b9c7566853add2d87
+size 376097

requirements.txt

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

unique_values_n.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:905cd1a1edec7e903345f03557f7b8eab46362f62190f9a314de42c6af9ba2d0
+size 3216