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