import gradio as gr
import pandas as pd
import pickle
from typing import List, Dict, Any

# CSS styles (unchanged)
css = """
body, html {
    height: 100%;
    margin: 0;
    color: white;
    background-color: black;
}
header {
    background: url('fraude.png') no-repeat top left;
    background-size: 120px; /* Adjust this value to the desired size of your image */
    padding-top: 130px; /* Adjust this value to provide space for the image */
}
"""

# Load model and configurations
def load_pickle(filename: str) -> Any:
    with open(filename, 'rb') as f:
        return pickle.load(f)

model = load_pickle('model/modelo_proyecto_final2.pkl')
ohe_columns = load_pickle('model/categories_ohe_without_fraudulent.pickle')
bins_order = load_pickle('model/saved_bins_order.pickle')
bins_transaction = load_pickle('model/saved_bins_transaction.pickle')

def predict(order_amount: float, order_state: str, payment_method_registration_failure: bool,
            payment_method_type: str, payment_method_provider: str, payment_method_issuer: str,
            transaction_amount: float, transaction_failed: bool, email_domain: str,
            email_provider: str, customer_ip_address_simplified: str, same_city: str) -> str:
    # Create input DataFrame
    data = {
        "orderAmount": [order_amount],
        "orderState": [order_state],
        "paymentMethodRegistrationFailure": [payment_method_registration_failure],
        "paymentMethodType": [payment_method_type],
        "paymentMethodProvider": [payment_method_provider],
        "paymentMethodIssuer": [payment_method_issuer],
        "transactionAmount": [transaction_amount],
        "transactionFailed": [transaction_failed],
        "emailDomain": [email_domain],
        "emailProvider": [email_provider],
        "customerIPAddressSimplified": [customer_ip_address_simplified],
        "sameCity": [same_city]
    }
    df = pd.DataFrame(data)
    
    # Fill null values with modes or medians
    for column in df.columns:
        if pd.api.types.is_numeric_dtype(df[column]):
            df[column].fillna(df[column].median(), inplace=True)
        else:
            df[column].fillna(df[column].mode().iloc[0], inplace=True)
    
    # Apply binning and one-hot encoding
    df["orderAmount"] = pd.cut(df["orderAmount"].astype(float), bins=bins_order, include_lowest=True)
    df["transactionAmount"] = pd.cut(df["transactionAmount"].astype(int), bins=bins_transaction, include_lowest=True)
    df_encoded = pd.get_dummies(df).reindex(columns=ohe_columns, fill_value=0)

    # Prediction
    prediction = model.predict(df_encoded)
    type_of_fraud = int(prediction[0])
    responses = ["OK", "FRAUD DETECTED", "Warning"]
    return responses[type_of_fraud] if type_of_fraud in [0, 1, 2] else "Error parsing value"

# Configure Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Number(label="Order Amount"),
        gr.Dropdown(choices=["pending", "fulfilled", "failed"], label="Order State"),
        gr.Checkbox(label="Payment Method Registration Failure"),
        gr.Dropdown(choices=["card", "bitcoin", "paypal"], label="Payment Method Type"),
        gr.Dropdown(choices=['JCB 16 digit', 'VISA 16 digit', 'Diners Club / Carte Blanche', 'Mastercard', 'American Express', 'Maestro', 'Discover', 'Voyager', 'VISA 13 digit', 'JCB 15 digit'], label="Payment Method Provider"),
        gr.Dropdown(choices=['Citizens First Banks', 'Solace Banks', 'Vertex Bancorp', 'His Majesty Bank Corp.', 'Bastion Banks', 'Her Majesty Trust', 'Fountain Financial Inc.', 'Grand Credit Corporation', 'weird', 'Bulwark Trust Corp.', 'Rose Bancshares'], label="Payment Method Issuer"),
        gr.Number(label="Transaction Amount"),
        gr.Checkbox(label="Transaction Failed"),
        gr.Dropdown(choices=["info","com","biz","org"], label="Email Domain"),
        gr.Dropdown(choices=["yahoo","gmail","hotmail","other"], label="Email Provider"),
        gr.Dropdown(choices=["only_letters", "digits_and_letters"], label="Customer IP Address Simplified"),
        gr.Dropdown(choices=["yes", "no"], label="Same City")
    ],
    outputs="text",
    title="API for Fraud Detection",
    description="APP to predict if a transaction is fraudulent.",
    css=css
)

if __name__ == "__main__":
    interface.launch()