Update request body logic

app.py

@@ -3,6 +3,7 @@ import streamlit.components.v1 as components
 import streamlit as st
 import pandas as pd
 import logging
+import time
 from deeploy import Client
 from constants import (
     relationship_dict,
@@ -75,6 +76,7 @@ with st.sidebar:
     # Ask for model URL and token
     host = st.text_input("Host (Changing is optional)", "app.deeploy.ml")
     model_url, workspace_id, deployment_id = get_model_url()
+    st.session_state.deployment_id = deployment_id
     deployment_token = st.text_input("Deeploy Model Token", "my-secret-token")
     if deployment_token == "my-secret-token":
         st.warning("Please enter Deeploy API token.")
@@ -101,13 +103,55 @@ if "evaluation_submitted" not in st.session_state:
 if "predict_button_clicked" not in st.session_state:
     st.session_state.predict_button_clicked = False
 
+if "request_body" not in st.session_state:
+    st.session_state.request_body = None
+
+if "deployment_id" not in st.session_state:
+    st.session_state.deployment_id = None
+
 if "exp" not in st.session_state:
     st.session_state.exp = None
 
-def set_prediction_view():
+def form_request_body():
+    """Create the request body for the prediction endpoint"""
+    marital_status_id = marital_status_dict[st.session_state.marital_status]
+    native_country_id = countries_dict[st.session_state.native_country]
+    relationship_id = relationship_dict[st.session_state.relationship]
+    occupation_id = occupation_dict[st.session_state.occupation]
+    education_id = education_dict[st.session_state.education]
+    type_of_work_id = type_of_work_dict[st.session_state.type_of_work]
+    return {
+        "instances": [
+            [
+                st.session_state.age,
+                type_of_work_id,
+                education_id,
+                marital_status_id,
+                occupation_id,
+                relationship_id,
+                st.session_state.capital_gain,
+                st.session_state.capital_loss,
+                st.session_state.hours_per_week,
+                native_country_id,
+            ]
+        ]
+    }
+
+
+def predict_callback():
+    """Callback function to call the prediction endpoint"""
+    request_body = form_request_body()
+    st.session_state.exp = None
+    with st.spinner("Loading prediction and explanation..."):
+        # Call the explain endpoint as it also includes the prediction
+        exp = client.explain(
+            request_body=request_body, deployment_id=st.session_state.deployment_id
+        )
+        st.session_state.exp = exp
+        time.sleep(0.5)
+
     st.session_state.predict_button_clicked = True
     st.session_state.evaluation_submitted = False    
-    hide_expander()
 
 def hide_expander():
     st.session_state.expander_toggle = False
@@ -142,51 +186,44 @@ def submit_and_clear(evaluation: str):
 #     st.write(st.session_state)
 
 # Attributes
-st.subheader("Loan Application")
-with st.expander("Application form", expanded=st.session_state.expander_toggle):
+with st.expander("**Loan application form**", expanded=st.session_state.expander_toggle):
     # Split view in 2 columns
     col1, col2 = st.columns(2)
     with col1:
         # Create input fields for attributes from constant dicts
-        age = st.number_input("Age", min_value=0, max_value=100, value=30)
-        marital_status = st.selectbox("Marital Status", marital_status_dict.keys())
-        marital_status_id = marital_status_dict[marital_status]
+        age = st.number_input("Age", min_value=10, max_value=100, value=30, key="age", on_change=predict_callback)
+        marital_status = st.selectbox("Marital Status", marital_status_dict.keys(), key="marital_status", on_change=predict_callback,)
         native_country = st.selectbox(
-            "Native Country", countries_dict.keys(), index=len(countries_dict) - 1
+            "Native Country", countries_dict.keys(), index=len(countries_dict) - 1, key="native_country",on_change=predict_callback
         )
-        native_country_id = countries_dict[native_country]
-        relationship = st.selectbox("Family situation", relationship_dict.keys())
-        relationship_id = relationship_dict[relationship]
-        occupation = st.selectbox("Occupation", occupation_dict.keys(), index=1)
-        occupation_id = occupation_dict[occupation]
+        relationship = st.selectbox("Family situation", relationship_dict.keys(), key="relationship", on_change=predict_callback)
+        occupation = st.selectbox("Occupation", occupation_dict.keys(), index=1, key="occupation", on_change=predict_callback)
 
     with col2:
-        education = st.selectbox("Highest education level", education_dict.keys(), index=4)
-        education_id = education_dict[education]
-        type_of_work = st.selectbox("Type of work", type_of_work_dict.keys())
-        type_of_work_id = type_of_work_dict[type_of_work]
+        education = st.selectbox("Highest education level", education_dict.keys(), key="education", index=4, on_change=predict_callback)
+        type_of_work = st.selectbox("Type of work", type_of_work_dict.keys(), key="type_of_work", on_change=predict_callback)
         hours_per_week = st.number_input(
-            "Working hours per week", min_value=0, max_value=100, value=40
+            "Working hours per week", min_value=0, max_value=100, value=40, key="hours_per_week", on_change=predict_callback,
         )
         capital_gain = st.number_input(
-            "Yearly income [€]", min_value=0, max_value=1000000, value=70000
+            "Yearly income [€]", min_value=0, max_value=10000000, value=70000, key="capital_gain", on_change=predict_callback,
         )
         capital_loss = st.number_input(
-            "Yearly expenditures [€]", min_value=0, max_value=1000000, value=60000
+            "Yearly expenditures [€]", min_value=0, max_value=10000000, value=60000, key="capital_loss", on_change=predict_callback,
         )
     data_df = pd.DataFrame(
         [
             [
-                age,
-                type_of_work,
-                education,
-                marital_status,
-                occupation,
-                relationship,
-                capital_gain,
-                capital_loss,
-                hours_per_week,
-                native_country,
+                st.session_state.age,
+                st.session_state.type_of_work,
+                st.session_state.education,
+                st.session_state.marital_status,
+                st.session_state.occupation,
+                st.session_state.relationship,
+                st.session_state.capital_gain,
+                st.session_state.capital_loss,
+                st.session_state.hours_per_week,
+                st.session_state.native_country,
             ]
         ],
         columns=[
@@ -203,35 +240,12 @@ with st.expander("Application form", expanded=st.session_state.expander_toggle):
         ],
     )
     data_df_t = data_df.T
-    request_body = {
-        "instances": [
-            [
-                age,
-                type_of_work_id,
-                education_id,
-                marital_status_id,
-                occupation_id,
-                relationship_id,
-                capital_gain,
-                capital_loss,
-                hours_per_week,
-                native_country_id,
-            ]
-        ]
-    }
 
 # Show predict button if token is set
 if deployment_token != "my-secret-token" and st.session_state.exp is None:
     predict_button = st.button(
-        "Send loan application", key="predict_button", help="Click to get the AI prediction.", on_click=set_prediction_view
-    )
-    if predict_button:
-        with st.spinner("Loading prediction and explanation..."):
-            # Call the explain endpoint as it also includes the prediction
-            exp = client.explain(
-                request_body=request_body, deployment_id=deployment_id
-            )
-            st.session_state.exp = exp
+        "Send loan application", key="predict_button", help="Click to get the AI prediction.", on_click=predict_callback,
+    )        
 
 if st.session_state.evaluation_submitted:
     st.success("Evaluation submitted successfully!")
@@ -257,10 +271,11 @@ elif st.session_state.predict_button_clicked and st.session_state.exp is not Non
         exp_df_t = data_df_t.merge(exp_df_t, left_index=True, right_index=True)
 
         weight_feat = "Weight"
-        exp_df_t.columns = ["Feature value", weight_feat]
+        feat_val_col = "Value"
+        exp_df_t.columns = [feat_val_col, weight_feat]
         exp_df_t["Feature"] = exp_df_t.index
-        exp_df_t = exp_df_t[["Feature", "Feature value", weight_feat]]
-        exp_df_t["Feature value"] = exp_df_t["Feature value"].astype(str)
+        exp_df_t = exp_df_t[["Feature", feat_val_col, weight_feat]]
+        exp_df_t[feat_val_col] = exp_df_t[feat_val_col].astype(str)
         
         # Filter values below 0.01
         exp_df_t = exp_df_t[
@@ -279,14 +294,14 @@ elif st.session_state.predict_button_clicked and st.session_state.exp is not Non
         pos_feats = pos_exp_df_t[weight_feat].nlargest(3).index.tolist()
         # For feature, get feature value and concatenate into a single string
         pos_feats = [
-            f"{feat}: {pos_exp_df_t.loc[feat, 'Feature value']}"
+            f"{feat}: {pos_exp_df_t.loc[feat, feat_val_col]}"
             for feat in pos_feats
         ]
         # Get 3 features with highest negative relevance score
         neg_feats = neg_exp_df_t[weight_feat].nlargest(3).index.tolist()
         # For feature, get feature value and concatenate into a single string
         neg_feats = [
-            f"{feat}: {neg_exp_df_t.loc[feat, 'Feature value']}"
+            f"{feat}: {neg_exp_df_t.loc[feat, feat_val_col]}"
             for feat in neg_feats
         ]
         if predictions[0]:
@@ -304,7 +319,7 @@ elif st.session_state.predict_button_clicked and st.session_state.exp is not Non
                     + "  \n- ".join(pos_feats)
                 )
             with col2:
-                st.warning(
+                st.error(
                     "However, the following features weight against the loan applicant:  \n - "
                     + "  \n- ".join(neg_feats)
                     # + "  \n For more details, see full explanation of the credit assessment below.",
@@ -315,18 +330,20 @@ elif st.session_state.predict_button_clicked and st.session_state.exp is not Non
             with col1:
                 # If prediction is negative, first show negative features, then positive features
                 st.error(
-                    "The most important characteristics in favor of loan rejection are:  \n - "
+                    "The most important reasons for loan rejection are:  \n - "
                     + "  \n - ".join(neg_feats)
                 )
             with col2:
-                st.warning(
+                st.success(
                     "However, the following factors weigh in favor of the loan applicant:  \n - "
                     + "  \n - ".join(pos_feats)
                 )
         try:
             # Show explanation
-            col_pos, col_neg = st.columns(2)
-
+            if predictions[0]:
+                col_pos, col_neg = st.columns(2)
+            else:
+                col_neg, col_pos = st.columns(2) # Swap columns if prediction is negative
             with col_pos:
                 st.subheader("Factors :green[in favor] of loan approval")
                 # st.success("**Factors in favor of loan approval**")