app in progress

app.py

@@ -2,52 +2,25 @@ import streamlit as st
 import pandas as pd
 import logging
 from deeploy import Client
-from utils import ChangeButtonColour
-from utils import get_input_values, get_texts, feature_texts, example_input, response, first_five_posneg_indices
+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 COL_NAMES, feature_texts
+from visual_components import create_data_input_table, create_table, ChangeButtonColour
 
 logging.basicConfig(level=logging.INFO)
 
 st.set_page_config(layout="wide")
 
-st.title("Observing potential fraudulent transactions")
-st.divider()
+data = pd.read_pickle("data/preprocessed_data.pkl")
+# data = data.drop('isFraud', axis=1)
 
-st.write(
-    "Fill in left hand side and click on button to observe a potential fraudulent transaction"
-)
+    # Disable the submit button after it is clicked
+def disable():
+    st.session_state.disabled = True
 
-def send_evaluation(client, deployment_id, request_log_id, prediction_log_id, evaluation_input):
-    """Send evaluation to Deeploy."""
-    try:
-        with st.spinner("Submitting response..."):
-            # Call the explain endpoint as it also includes the prediction
-            client.evaluate(deployment_id, request_log_id, prediction_log_id, evaluation_input)
-        return True
-    except Exception as e:
-        logging.error(e)
-        st.error(
-            "Failed to submit feedback."
-            + "Check whether you are using the right model URL and Token. "
-            + "Contact Deeploy if the problem persists."
-        )
-        st.write(f"Error message: {e}")
-
-
-def get_model_url():
-    """Get model url and retrieve workspace id and deployment id from it"""
-    model_url = st.text_area(
-        "Model URL (default is the demo deployment)",
-        "https://api.app.deeploy.ml/workspaces/708b5808-27af-461a-8ee5-80add68384c7/deployments/1785f8b8-c5a6-4f55-9a83-df8bdb0b9977/",
-        height=125,
-    )
-    elems = model_url.split("/")
-    try:
-        workspace_id = elems[4]
-        deployment_id = elems[6]
-    except IndexError:
-        workspace_id = ""
-        deployment_id = ""
-    return model_url, workspace_id, deployment_id
+# Initialize disabled for form_submit_button to False
+if "disabled" not in st.session_state:
+    st.session_state.disabled = False
 
 st.markdown("""
     <style>
@@ -65,9 +38,16 @@ with st.sidebar:
     model_url, workspace_id, deployment_id = get_model_url()
     deployment_token = st.text_input("Deeploy Model Token", "my-secret-token")
     if deployment_token == "my-secret-token":
-        button_clicked = st.button("Get suspicious transaction", key="get1", help="Click to get a suspicious transaction", use_container_width=True, on_click=lambda: st.experimental_rerun())
+        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
+    ) #on_click=lambda: st.experimental_rerun()
+        ChangeButtonColour("Get suspicious transaction", '#FFFFFF', "#00052D")#'#FFFFFF', "#00052D"
+    
 
-    # define client options and instantiate client
+    # define client optsions and instantiate client
     client_options = {
     "host": host,
     "deployment_token": deployment_token,
@@ -75,50 +55,97 @@ with st.sidebar:
     }
     client = Client(**client_options)
 
+    # st.text(client_options)
+    # st.text(deployment_id)
+
+if "predict_button" not in st.session_state:
+    st.session_state.predict_button = False
+    st.title("Money Laundering System")
+    st.divider()
+    st.info(
+    "Fill in left hand side and click on button to observe a potential fraudulent transaction"
+    )
+if st.session_state.predict_button:
+    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
+            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:
+            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.divider()
+
+    
+                # Add prediction evaluation
+        st.subheader("Prediction Evaluation")
+        st.write("Do you agree with the prediction?")
+
+        yes_button = st.button("Yes :thumbsup:", key="yes_button")
+        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
+            }
+        no_button = st.button("No :thumbsdown:", key="no_button")
+        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.")
+        
+   
+
+
+    
+    except Exception as e:
+        logging.error(e)
+        st.error(
+            "Failed to retrieve the prediction or explanation."
+            + "Check whether you are using the right model URL and Token. "
+            + "Contact Deeploy if the problem persists."
+        )
+
+
+
+
+
+st.session_state.successful_call = False
+
+
+   
 
-ChangeButtonColour("Get suspicious transaction", '#FFFFFF', "#00052D")#'#FFFFFF', "#00052D"
-
-
-positive_and_negative_indices = first_five_posneg_indices(response)
-positive_texts, negative_texts = get_texts(positive_and_negative_indices, feature_texts)
-positive_vals, negative_vals = get_input_values(positive_and_negative_indices, example_input)
-
-# Create a function to generate a table
-def create_table(texts, values, title):
-    # TODO: change color dataframe header -> tried many different options but none worked see below
-    # header_style = '''
-    # <style>
-    #     th{
-    #         background-color: #00052D;
-    #     }
-    # </style>
-    # '''
-    df = pd.DataFrame({"Feature Explanation": texts, 'Value': values})
-    # df = df.style.set_properties(**{
-    #        'selector': 'th',
-    #        'props': [
-    #            ('background-color', 'black'),
-    #            ('color', 'cyan')]
-    #    })
-    # df = df.style.set_properties(**{'background-color': 'black',
-    #                        'color': 'green'})
-
-    # headers = {
-    #     'selector': 'th',
-    #     'props': [('background-color', '#67c5a4')]#'background-color: #000066; color: white;'
-    #     }
-    # df = df.style.set_table_styles([headers])
-    st.markdown(f'#### {title}')  # Markdown for styling
-    st.dataframe(df, hide_index=True)  # Display a simple table
-    # st.markdown(header_style, unsafe_allow_html=True)
-
-# Arrange tables horizontally using Streamlit columns
-col1, col2 = st.columns(2,gap="small")
-
-# Display tables in Streamlit columns
-with col1:
-    create_table(positive_texts, positive_vals, 'Important Suspicious Variables')
-
-with col2:
-    create_table(negative_texts, negative_vals, 'Important Unsuspicious Variables')
+