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import streamlit as st
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, send_evaluation, get_comment_explanation
from utils import COL_NAMES, feature_texts
from utils import create_data_input_table, create_table, ChangeButtonColour, get_weights
logging.basicConfig(level=logging.INFO)
st.set_page_config(layout="wide")
st.title("Smart AML:tm:")
st.divider()
data = pd.read_pickle("data/preprocessed_data.pkl")
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
if "disabled" not in st.session_state:
st.session_state.disabled = False
def disabled():
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] {
background-color: #E0E0E0; ##E5E6EA
}
</style>
""", unsafe_allow_html=True)
with st.sidebar:
# Add deeploy logo
st.image("deeploy_logo.png", width=270)
# 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") # 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=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 = {
"host": host,
"deployment_token": deployment_token,
"workspace_id": workspace_id,
}
client = Client(**client_options)
if 'predict_button' not in st.session_state:
st.session_state.predict_button = False
if st.session_state.predict_button: # and not st.session_state.predict_button_clicked
st.session_state.predict_button_clicked = True
if 'got_explanation' not in st.session_state:
st.session_state.got_explanation = False
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
exp = client.explain(request_body=request_body, deployment_id=deployment_id)
# 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.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(
"Failed to retrieve the prediction or explanation."
+ "Check whether you are using the right model URL and Token. "
+ "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('AML Model Hit')
# st.success(f'{certainty}')
# st.metric(label='Model Certainty', value=certainty)
# style_metric_cards(border_left_color='#00052D', box_shadow=False)
# # st.markdown('#### Model Certainty')
st.metric(label='Model Certainty', value=certainty)
explainability_texts, sorted_indices = get_explainability_texts(shap_values, feature_texts)
weights = get_weights(shap_values, sorted_indices)
explainability_values = get_explainability_values(sorted_indices, datapoint_pd)
create_table(explainability_texts, explainability_values, weights, 'Important Suspicious Factors')
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', '#4C506C') #'#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', '#4C506C') # '#FFFFFF', "#46B071", '#FFFFFF', "#666666"
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]},
}
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_comment_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.warning("Feedback submitted successfully")
st.button("Next", key='next', use_container_width=True, on_click=rerun)
ChangeButtonColour("Next", '#FFFFFF', "#00052D") #'#FFFFFF', #F9B917" "#DD360C #457EA4
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