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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
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")
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
# Initialize disabled for form_submit_button to False
if "disabled" not in st.session_state:
st.session_state.disabled = False
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")
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
) #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)
# 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
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