import gradio as gr
import pandas as pd
import pickle

import os
MAIN_FOLDER = os.path.dirname(__file__)

# Define params names
PARAMS_NAME = [
    "orderAmount",
    "orderState",
    "paymentMethodRegistrationFailure",
    "paymentMethodType",
    "paymentMethodProvider",
    "paymentMethodIssuer",
    "transactionAmount",
    "transactionFailed",
    "emailDomain",
    "emailProvider",
    "customerIPAddressSimplified",
    "sameCity"
]

# Load model
with open("model/modelo_proyecto_final.pkl", "rb") as f:
    model = pickle.load(f)

# Columnas
COLUMNS_PATH = "model/categories_ohe_without_fraudulent.pickle"
with open(COLUMNS_PATH, 'rb') as handle:
    ohe_tr = pickle.load(handle)

BINS_ORDER = os.path.join(MAIN_FOLDER, "model/saved_bins_order.pickle")
with open(BINS_ORDER, 'rb') as handle:
    new_saved_bins_order = pickle.load(handle)

BINS_TRANSACTION = os.path.join(MAIN_FOLDER, "model/saved_bins_transaction.pickle")
with open(BINS_TRANSACTION, 'rb') as handle:
    new_saved_bins_transaction = pickle.load(handle)

def predict(*args):
    answer_dict = {}

    for i in range(len(PARAMS_NAME)):
        answer_dict[PARAMS_NAME[i]] = [args[i]]

    # Crear dataframe
    single_instance = pd.DataFrame.from_dict(answer_dict)

    # Manejar puntos de corte o bins
    single_instance["orderAmount"] = single_instance["orderAmount"].astype(float)
    single_instance["orderAmount"] = pd.cut(single_instance['orderAmount'],
                                 bins=new_saved_bins_order, 
                                 include_lowest=True)

    single_instance["transactionAmount"] = single_instance["transactionAmount"].astype(int)
    single_instance["transactionAmount"] = pd.cut(single_instance['transactionAmount'],
                                 bins=new_saved_bins_order, 
                                 include_lowest=True)

    # One hot encoding
    single_instance_ohe = pd.get_dummies(single_instance).reindex(columns = ohe_tr).fillna(0)

    prediction = model.predict(single_instance_ohe)

    # Cast numpy.int64 to just a int
    type_of_fraud = int(prediction[0])

    # Adaptación respuesta
    response = "Error parsing value"
    if type_of_fraud == 0:
        response = "Falso"
    if type_of_fraud == 1:
        response = "Verdadero"
    if type_of_fraud == 2:
        response = "Dudoso"

    return response


with gr.Blocks() as demo:
    gr.Markdown(
        """
        # Predictor  fraude
        """
    )

    with gr.Row():
        with gr.Column():

            gr.Markdown(
                
            )
            

            transactionFailed = gr.Dropdown(
                label="Transaction failed",
                choices=["False", "True"],
                value="False"
                )

            orderAmount = gr.Slider(label="Order amount", minimum=0, maximum=354, step=6, randomize=False)

            transactionAmount = gr.Slider(label="Transaction amount", minimum=0, maximum=354, step=6, randomize=False)

            orderState = gr.Radio(
                label="Order state",
                choices=[ "failed","fulfilled", "pending"],
                value="failed"
                )
            
            emailDomain = gr.Dropdown(
                label="Email domain",
                choices=["biz","com","info","net","org","weird"],
                value="biz"
                )

            emailProvider = gr.Dropdown(
                label="Email provider",
                choices=["gmail", "hotmail", "yahoo", "weird", "other"],
                value="gmail"
                )

            customerIPAddressSimplified = gr.Dropdown(
                label="Customer IP Address",
                choices=["only_letters", "digits_and_letters"],
                value="only_letters"
                )

            sameCity = gr.Radio(
                label="Same city",
                choices=[ "no", "yes","unknown"],
                value="no"
                )

            paymentMethodRegistrationFailure = gr.Dropdown(
                label="Payment method registration failure",
                choices=[ "True","False",],
                value="True"
                )

            paymentMethodType = gr.Dropdown(
                label="Payment method type",
                choices=["apple pay","bitcoin","card","paypal"],
                value="apple pay"
                )

            paymentMethodProvider = gr.Dropdown(
                label="Payment method provider",
                choices=["American Express", "Diners Club / Carte Blanche","Discover","JCB 15 digit" ,"JCB 16 digit","Maestro" , "Mastercard", "VISA 13 digit", "VISA 16 digit", "Voyager"],
                multiselect=False,
                value="American Express"
                )
            
            paymentMethodIssuer = gr.Dropdown(
                label="Payment method issuer",
                choices=["Bastion Banks","Bulwark Trust Corp.","Citizens First Banks","Fountain Financial Inc.","Grand Credit Corporation","Her Majesty Trust","His Majesty Bank Corp.","Rose Bancshares","Solace Banks","Vertex Bancorp","weird"],
                multiselect=False,
                value="Bastion Banks"
                )


        with gr.Column():

            gr.Markdown(
                """
                ## Prediccion
                """
            )

            label = gr.Label(label="Resultado")
            predict_btn = gr.Button(value="Analizar")
            predict_btn.click(
            predict,
            inputs=[
                orderAmount,
                orderState,
                paymentMethodRegistrationFailure,
                paymentMethodType,
                paymentMethodProvider,
                paymentMethodIssuer,
                transactionAmount,
                transactionFailed,
                emailDomain,
                emailProvider,
                customerIPAddressSimplified,
                sameCity,
            ],
            outputs=[label],
            api_name="prediccion"
            )
   

demo.launch()