working version

app.py

@@ -3,7 +3,7 @@ import pandas as pd
 import logging
 from deeploy import Client
 from utils import get_request_body, get_fake_certainty, get_model_url, get_random_suspicious_transaction
-from utils import get_explainability_texts, get_explainability_values
+from utils import get_explainability_texts, get_explainability_values, send_evaluation, get_explanation
 from utils import COL_NAMES, feature_texts
 from visual_components import create_data_input_table, create_table, ChangeButtonColour
 
@@ -15,16 +15,30 @@ st.title("Money Laundering System")
 st.divider()
 
 data = pd.read_pickle("data/preprocessed_data.pkl")
-# data = data.drop('isFraud', axis=1)
 
-    # Disable the submit button after it is clicked
-def disable():
-    st.session_state.disabled = True
+if 'predict_button_clicked' not in st.session_state:
+    st.session_state.predict_button_clicked = False
+
+
+if "submitted_disabled" not in st.session_state:
+    st.session_state.submitted_disabled = False
 
-# Initialize disabled for form_submit_button to False
 if "disabled" not in st.session_state:
     st.session_state.disabled = False
 
+def disabled():
+    # st.session_state.predict_button_clicked = True
+    st.session_state.disabled = True
+
+def rerun():
+    st.session_state.predict_button_clicked = True
+    st.session_state.submitted_disabled = False
+
+
+def submitted_disabled():
+    st.session_state.submitted_disabled = True
+    
+
 st.markdown("""
     <style>
         [data-testid=stSidebar] {
@@ -39,16 +53,16 @@ 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()
-    deployment_token = st.text_input("Deeploy Model Token", "my-secret-token")
+    deployment_token = st.text_input("Deeploy Model Token", "DPTjVXffSvkAATY1KEeNRGghUEd0iugjYBGKTLTIjtZa3aheZcF1esLBP4F6mYhvk48hF8vy8uZmC8MzwUVFwE7JIPeNCSEjxTCqHOjzsGzNW0ydFA4W") # my-secret-token
     if deployment_token == "my-secret-token":
         st.warning(
             "Please enter Deeploy API token."
         )
-    else:
-        st.button("Get suspicious transaction", key="predict_button", help="Click to get a suspicious transaction", use_container_width=True,  on_click=disable, disabled=st.session_state.disabled
+    else: 
+        st.button("Get suspicious transaction", key="predict_button", help="Click to get a suspicious transaction", use_container_width=True,  on_click=disabled, disabled=st.session_state.disabled
     ) #on_click=lambda: st.experimental_rerun()
         ChangeButtonColour("Get suspicious transaction", '#FFFFFF', "#00052D")#'#FFFFFF', "#00052D"
-    
+
 
     # define client optsions and instantiate client
     client_options = {
@@ -58,82 +72,31 @@ with st.sidebar:
     }
     client = Client(**client_options)
 
-    # st.text(client_options)
-    # st.text(deployment_id)
+if st.session_state.predict_button: #  and not st.session_state.predict_button_clicked
+    st.session_state.predict_button_clicked = True
+
+# if 'explanation' not in st.session_state:
+#     st.session_state.explanation = ""
+if 'got_explanation' not in st.session_state:
+    st.session_state.got_explanation = False
 
-if "predict_button" not in st.session_state:
-    st.session_state.predict_button = False
-    st.info(
-    "Fill in left hand side and click on button to observe a potential fraudulent transaction"
-    )
-if st.session_state.predict_button:
+if st.session_state.predict_button_clicked:
     try:
         with st.spinner("Loading..."):
             datapoint_pd = get_random_suspicious_transaction(data)
             request_body = get_request_body(datapoint_pd)
-        # Call the explain endpoint as it also includes the prediction
+            # Call the explain endpoint as it also includes the prediction
             exp = client.explain(request_body=request_body, deployment_id=deployment_id)
-            shap_values = exp['explanations'][0]['shap_values']
-          
-        
-        col1, col2 = st.columns(2)
-
-        with col1:
-
-            create_data_input_table(datapoint_pd, COL_NAMES)
-        with col2:
-            st.subheader('Prediction Explanation: ')
+            # request_log_id = exp["requestLogId"]
+            # prediction_log_id = exp["predictionLogIds"][0]
+            st.session_state.shap_values = exp['explanations'][0]['shap_values']
+            st.session_state.request_log_id = exp["requestLogId"]
+            st.session_state.prediction_log_id = exp["predictionLogIds"][0]
+            st.session_state.datapoint_pd = datapoint_pd
             certainty = get_fake_certainty()
-            st.metric(label='#### Model Certainty', value=certainty)
-
-            explainability_texts, sorted_indices = get_explainability_texts(shap_values, feature_texts)
-            explainability_values = get_explainability_values(sorted_indices, datapoint_pd)
-            create_table(explainability_texts, explainability_values, 'Important Suspicious Variables: ')
-
-        st.subheader("")
-        # st.markdown("<h2 style='text-align: center; white: red;'>Evaluation</h2>", unsafe_allow_html=True)
-
-        col3, col4 = st.columns(2)
-        
-        with col3:
-            st.button("Send to FIU", key="yes_button", use_container_width=True)
-            ChangeButtonColour("Send to FIU", '#FFFFFF', "#DD360C")
-            if 'eval_selected' not in st.session_state:
-                st.session_state['eval_selected'] = False
-            # if yes_button:
-            #     st.session_state.eval_selected = True
-            #     st.session_state.evaluation_input = {
-            #         "result": 0 # Agree with the prediction
-            #     }
-        
-        with col4:
-            st.button("Not money laundering", key="no_button", use_container_width=True)
-            ChangeButtonColour("Not money laundering", '#FFFFFF', "#46B071")
-            # if no_button:
-            #     st.session_state.eval_selected = True
-                # desired_output = not predictions[0]
-                # st.session_state.evaluation_input = {
-                #     "result": 1, # Disagree with the prediction
-                #     "value": {"predictions": [desired_output]},
-                # }
-
-        success = False
-        if st.session_state.eval_selected:
-            comment = st.text_input("Would you like to add a comment?")
-            if comment:
-                st.session_state.evaluation_input["explanation"] = comment
-            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 success:
-            st.session_state.eval_selected = False
-            st.success("Feedback submitted successfully.")
-        
-   
-
-
-    
+            st.session_state.certainty = certainty
+            st.session_state.got_explanation = True
+            st.session_state.predict_button_clicked = False
     except Exception as e:
         logging.error(e)
         st.error(
@@ -142,14 +105,93 @@ if st.session_state.predict_button:
             + "Contact Deeploy if the problem persists."
         )
 
+if not st.session_state.got_explanation:
+        st.info(
+        "Fill in left hand side and click on button to observe a potential fraudulent transaction"
+        )
+if st.session_state.got_explanation:
+    shap_values = st.session_state.shap_values
+    request_log_id = st.session_state.request_log_id
+    prediction_log_id = st.session_state.prediction_log_id
+    datapoint_pd = st.session_state.datapoint_pd
+    certainty = st.session_state.certainty
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        create_data_input_table(datapoint_pd, COL_NAMES)
+
+    with col2:
+        st.subheader('Prediction Explanation: ')
+        # st.metric(label='#### Model Certainty', value=certainty)
+        st.markdown('#### Model Certainty')
+        st.success(f'{certainty}')
+
+        explainability_texts, sorted_indices = get_explainability_texts(shap_values, feature_texts)
+        explainability_values = get_explainability_values(sorted_indices, datapoint_pd)
+        create_table(explainability_texts, explainability_values, 'Important Suspicious Variables: ')
+
+    st.subheader("")
+    # st.markdown("<h2 style='text-align: center; white: red;'>Evaluation</h2>", unsafe_allow_html=True)
+
+    if 'eval_selected' not in st.session_state:
+        st.session_state['eval_selected'] = False
+
+
+    col3, col4 = st.columns(2)
+    with col3:
+        eval1 = st.empty()
+        eval1.button("Send to FIU", key="yes_button", use_container_width=True, disabled=st.session_state.submitted_disabled)
+        ChangeButtonColour("Send to FIU", '#FFFFFF', "#DD360C")
+        st.session_state.yes_button_clicked = False
+
+    if st.session_state.yes_button:
+        st.session_state.eval_selected = True
+        st.session_state.evaluation_input = {
+            "result": 0 # Agree with the prediction
+        }
+        
+    with col4:
+        eval2 = st.empty()
+        eval2.button("Not money laundering", key="no_button", use_container_width=True, disabled=st.session_state.submitted_disabled)
+        ChangeButtonColour("Not money laundering", '#FFFFFF', "#46B071")
+        st.session_state.no_button_clicked = False
+
+
+    if st.session_state.no_button:
+        st.session_state.no_button_clicked = True
+    if st.session_state.no_button_clicked:
+        st.session_state.eval_selected = True
+        desired_output = 1
+        st.session_state.evaluation_input = {
+            "result": 1, # Disagree with the prediction
+            "value": {"predictions": [desired_output]},
+        }
 
-
-
-
-st.session_state.successful_call = False
-
-
-   
-
-  
-
+    success = False
+    if st.session_state.eval_selected:
+        # st.write('after eval')
+        # st.write(st.session_state)
+        if st.session_state.yes_button:
+            explanation = get_explanation(certainty, explainability_texts, explainability_values)
+            comment = st.text_area("Reason for evaluation:", explanation)
+            st.session_state.evaluation_input["explanation"] = comment
+        if st.session_state.no_button:
+            comment = st.text_area("Reason for evaluation:", "I don't think this transaction is money laundering because...")
+            st.session_state.evaluation_input["explanation"] = comment
+        logging.debug("Selected feedback:" + str(st.session_state.evaluation_input))
+        eval3 = st.empty()
+        eval3.button("Submit", key="submit_button", use_container_width=True, on_click=submitted_disabled, disabled=st.session_state.submitted_disabled)
+        ChangeButtonColour("Submit", '#FFFFFF', "#00052D")
+        if st.session_state.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 success:
+        st.session_state.eval_selected = False
+        st.session_state.submitted = True
+        eval1.empty()
+        eval2.empty()
+        eval3.empty()
+        st.success("Feedback submitted successfully.")
+        st.button("Next", key='next', use_container_width=True, on_click=rerun)
+        ChangeButtonColour("Next", '#FFFFFF', "#F9B917")

utils.py

@@ -120,3 +120,10 @@ def get_model_url():
         deployment_id = ""
     return model_url, workspace_id, deployment_id
 
+def get_explanation(certainty, explainability_texts, explainability_values):
+    cleaned = [x.replace(':', '') for x in explainability_texts]
+    fi = [f'{explainability_texts[i]} is {x}' for i,x in enumerate(cleaned)]
+    fi.insert(0, 'Important suspicious features: ')
+    result = '\n'.join(fi)
+    comment = f"Model certainty is {certainty}" + '\n''\n' + result
+    return comment

visual_components.py

@@ -24,22 +24,27 @@ def create_table(texts, values, title):
     # df = df.style.set_properties(**{
     #        'selector': 'th',
     #        'props': [
-    #            ('background-color', 'black'),
+    #            ('background-color', 'black'),``
     #            ('color', 'cyan')]
     #    })
     # df = df.style.set_properties(**{'background-color': 'black',
     #                        'color': 'green'})
 
     st.markdown(f'#### {title}')  # Markdown for styling
-    headers = {
-        'selector': 'th',
-        'props': 'background-color' '#67c5a4'#'background-color: #000066; color: white;'
-        }
+    # headers = {
+    #     'selector': 'th',
+    #     'props': 'background-color' '#67c5a4'#'background-color: #000066; color: white;'
+    #     }
+    header = {
+      'selector': 'th',
+    'props': 'background-color: #000066; color: white;'
+}
     
-    df = df.style.set_table_styles({
-            ('Feature Explanation', 'Value'): [{'selector': 'th', 'props': 'border-left: 1px solid white'},
-                                    {'selector': 'td', 'props': 'border-left: 1px solid #000066'}]
-        }, axis=0)
+    # df = df.style.set_table_styles({
+    #         ('Feature Explanation', 'Value'): [{'selector': 'th', 'props': 'border-left: 1px solid white'},
+    #                                 {'selector': 'td', 'props': 'border-left: 1px solid #000066'}]
+    #     }, axis=0)
+    df = df.style.set_table_styles([header])
     
     # df.style.set_table_styles([headers])
     st.dataframe(df, hide_index=True, width=450)  # Display a simple table