Upload app.py

app.py

@@ -7,6 +7,7 @@ Created on Fri Mar 31 17:45:36 2023
 import os
 import streamlit as st
 
+from streamlit import session_state as session
 from htbuilder import HtmlElement, div, hr, a, p, styles
 from htbuilder.units import percent, px
 from catboost import CatBoostClassifier
@@ -77,47 +78,72 @@ def footer():
     layout(*myargs)
 
 
-def update_prediction():
+def update_prediction(input_data):
     """Callback to automatically update prediction if button has already been
     clicked"""
     if is_clicked:
-        launch_prediction()
+        launch_prediction(input_data)
 
 
-def input_layout():
+def get_input_data():
+    """
+    Generate input layout and get input values.
+        
+    -return: DataFrame with input data.
+    """
+    session.input_data = pd.DataFrame()
 
     input_expander = st.expander('Input parameters', True)
     with input_expander:
         # Row 1
         col_age, col_sex = st.columns(2)
         with col_age:
-            st.slider('Age', 18, 75, on_change=update_prediction())
+            session.input_data['age'] = st.slider(
+                'Age', 18, 75, on_change=update_prediction(session.input_data))
         with col_sex:
-            st.radio('Gender', ['Female', 'Male'],
-                     on_change=update_prediction())
-        # st.write('div.row-widget.stRadio > div{flex-direction: row \
-        #          justify-content: center}', unsafe_allow_html=True)
+            session.input_data['sex'] = st.radio(
+                'Sex', ['Female', 'Male'],
+                on_change=update_prediction(session.input_data))
 
         # Row 2
         col_height, col_weight = st.columns(2)
         with col_height:
-            st.slider(
-                'Height', 140, 200, on_change=update_prediction())
+            session.input_data['height'] = st.slider(
+                'Height', 140, 200,
+                on_change=update_prediction(session.input_data))
         with col_weight:
-            st.slider(
-                'Weight', 40, 140, on_change=update_prediction())
+            session.input_data['weight'] = st.slider(
+                'Weight', 40, 140,
+                on_change=update_prediction(session.input_data))
 
         # Row 3
         col_ap_hi, col_ap_lo = st.columns(2)
         with col_ap_hi:
-            st.slider(
-                'Systolic blood pressure', 90, 200, on_change=update_prediction())
+            session.input_data['ap_hi'] = st.slider(
+                'Systolic blood pressure', 90, 200,
+                on_change=update_prediction(session.input_data))
         with col_ap_lo:
-            st.slider(
-                'Diastolic blood pressure', 50, 120, on_change=update_prediction())
-        
+            session.input_data['ap_lo'] = st.slider(
+                'Diastolic blood pressure', 50, 120,
+                on_change=update_prediction(session.input_data))
+
         st.write("")
 
+    return session.input_data
+
+
+def generate_prediction(input_data):
+    """
+    Generate prediction of cardiovascular disease probability based on input
+    data.
+    
+    -param input_data: DataFrame with input data
+    
+    -return: prediction of cardiovascular disease probability
+    """
+    return MODEL.predict(input_data)
+
+
 ###############################
 ## --------- MAIN ---------- ##
 ###############################
@@ -144,24 +170,23 @@ if __name__ == "__main__":
     ## --------------------------- ##
 
     # Load classification model
-    model = CatBoostClassifier()      # parameters not required.
-    model.load_model('./model.cbm')
+    MODEL = CatBoostClassifier()
+    MODEL.load_model('./model.cbm')
 
-    # Define inputs
-    input_layout()
-    
-    ## Create button to trigger poster generation
+    # Get inputs
+    session.input_data = get_input_data()
+
+    # Create button to trigger poster generation
     buffer1, col1, buffer2 = st.columns([1.3, 1, 1])
     is_clicked = col1.button(label="Generate predictions")
-    
+
     st.text("")
     st.text("")
-    
-    
-    ## Generate poster
+
+    # Generate poster
     if is_clicked:
-        st.write("Work in progress!")
-        # poster = generate_poster(data[data.title_year==session.selected_movie])
-        
+        prediction = generate_prediction(session.input_data)
+        st.write(prediction)
+
     st.text("")
     st.text("")