Update displayed values

app.py

@@ -8,7 +8,7 @@ from constants import (
     relationship_dict,
     occupation_dict,
     education_dict,
-    workclass_dict,
+    type_of_work_dict,
     countries_dict,
     marital_status_dict,
 )
@@ -130,30 +130,30 @@ with st.expander("Application form", expanded=False):
             "Native Country", countries_dict.keys(), index=len(countries_dict) - 1
         )
         native_country_id = countries_dict[native_country]
-        relationship = st.selectbox("Relative", relationship_dict.keys(), index=1)
+        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]
 
     with col2:
-        education = st.selectbox("Education", education_dict.keys(), index=4)
+        education = st.selectbox("Highest education level", education_dict.keys(), index=4)
         education_id = education_dict[education]
-        workclass = st.selectbox("Workclass", workclass_dict.keys())
-        workclass_id = workclass_dict[workclass]
+        type_of_work = st.selectbox("Type of work", type_of_work_dict.keys())
+        type_of_work_id = type_of_work_dict[type_of_work]
         hours_per_week = st.number_input(
-            "Hours per week", min_value=0, max_value=100, value=40
+            "Working hours per week", min_value=0, max_value=100, value=40
         )
         capital_gain = st.number_input(
-            "Yearly income [€]", min_value=0, max_value=1000000, value=70000
+            "Yearly income", min_value=0, max_value=1000000, value=70000
         )
         capital_loss = st.number_input(
-            "Yearly expenditures [€]", min_value=0, max_value=1000000, value=60000
+            "Yearly expenditures", min_value=0, max_value=1000000, value=60000
         )
     data_df = pd.DataFrame(
         [
             [
                 age,
-                workclass,
+                type_of_work,
                 education,
                 marital_status,
                 occupation,
@@ -166,14 +166,14 @@ with st.expander("Application form", expanded=False):
         ],
         columns=[
             "Age",
-            "Workclass",
-            "Education",
+            "Type of work",
+            "Highest education level",
             "Marital Status",
             "Occupation",
-            "Relative",
+            "Family situation",
             "Yearly Income [€]",
             "Yearly expenditures [€]",
-            "Hours per week",
+            "Working hours per week",
             "Native Country",
         ],
     )
@@ -182,7 +182,7 @@ with st.expander("Application form", expanded=False):
         "instances": [
             [
                 age,
-                workclass_id,
+                type_of_work_id,
                 education_id,
                 marital_status_id,
                 occupation_id,
@@ -273,28 +273,25 @@ if st.session_state.selected == "Loan Decision":
 
             # If prediction is positive, first show positive features, then negative features
             st.success(
-                "**Positive credit suitability**. This is primarily attributed to:  \n - "
+                "**Positive creditworthiness**. This is primarily attributed to:  \n - "
                 + "  \n- ".join(pos_feats)
             )
             st.warning(
                 "However, the following features weight ***against*** the loan application:  \n - "
                 + "  \n- ".join(neg_feats)
-                + "  \n See explanation below for more details.",
-                icon="⚠️",
+                + "  \n For more details, see full explanation of the credit assessment below.",
             )
         else:
             st.subheader("Loan Decision: :red[NEGATIVE]", divider="red")
             # If prediction is negative, first show negative features, then positive features
             st.error(
-                "**Negative credit suitability**. This is primarily attributed to:  \n - "
+                "**Negative creditworthiness**. This is primarily attributed to:  \n - "
                 + "  \n - ".join(neg_feats)
-                + "."
             )
             st.warning(
                 "However, the following factors weigh ***in favor*** of the loan applicant:  \n - "
                 + "  \n - ".join(pos_feats)
-                + ".  \n See below for more details.",
-                icon="⚠️",
+                + "  \n For more details, see full explanation of the credit assessment below.",
             )
         explanation_expander = st.expander("Show explanation")
         with explanation_expander:
@@ -340,9 +337,9 @@ if st.session_state.selected == "Loan Decision":
         st.divider()
         
         # Add prediction evaluation
-        st.subheader("Prediction Evaluation: Do you agree with the AI prediction?")
+        st.subheader("Evaluation: Do you agree with the predicted creditworthiness?")
         st.info(
-            "AI model predictions always come with a certain level of uncertainty. Evaluate the correctness of the prediction based on your expertise and experience."
+            "AI model predictions always come with a certain level of uncertainty.  \nEvaluate the correctness of the prediction based on your expertise and experience."
         )
         cols = st.columns(4)
         col_yes, col_no = cols[:2]
@@ -387,15 +384,18 @@ if st.session_state.selected == "Loan Decision":
             logging.debug(
                 "Selected feedback:" + str(st.session_state.evaluation_input)
             )
-            if st.button("Submit", key="submit_button"):
-                st.session_state.eval_selected = False
-                success = send_evaluation(
-                    client,
-                    deployment_id,
-                    request_log_id,
-                    prediction_log_id,
-                    st.session_state.evaluation_input,
-                )
+        if st.button("Submit", key="submit_button"):
+            # FIXME: Hide comment box after submission
+            st.session_state.eval_selected = False
+            yes_button = False
+            no_button = False
+            success = send_evaluation(
+                client,
+                deployment_id,
+                request_log_id,
+                prediction_log_id,
+                st.session_state.evaluation_input,
+            )
         if success:
             st.session_state.eval_selected = False
             st.success("Feedback submitted successfully.")

constants.py

@@ -44,11 +44,10 @@ countries_dict = {
 
 relationship_dict = {
     "Unmarried": 5,
-    "Not in Family": 3,
     "Wife": 0,
-    "Own child": 1,
     "Husband": 2,
-    "Other Relative": 4,
+    "Single parent": 1,
+    "Other dependant": 4,
 }
 
 occupation_dict = {
@@ -87,7 +86,7 @@ education_dict = {
     "Preschool": 15,
 }
 
-workclass_dict = {
+type_of_work_dict = {
     "Private": 0,
     "Self Employed Not Incorporated": 1,
     "Self Employed Incorporated": 2,
@@ -99,11 +98,8 @@ workclass_dict = {
 }
 
 marital_status_dict = {
-    "Never-married": 2,
-    "Married-civ-spouse": 0,
+    "Single": 2,
+    "Married": 0,
     "Divorced": 1,
-    "Separated": 3,
     "Widowed": 4,
-    "Married-spouse-absent": 5,
-    "Married-AF-spouse": 6,
 }

requirements.txt

@@ -4,7 +4,7 @@ dill==0.3.6
 matplotlib==3.7.0
 boto3==1.28.0
 joblib==1.3.2
-streamlit==1.29
+streamlit==1.29.0
 scipy==1.10.1
 shap==0.42.0
 plotly==5.18.0