import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import gradio as gr

df = pd.read_csv("credit_risk_dataset.csv")

df = df.dropna()

df.columns

X =df.drop(["loan_status", "loan_percent_income"], axis = 1)
y = df['loan_status']

categorical_features = ["person_home_ownership", "loan_intent", "loan_grade", "cb_person_default_on_file"]
X = pd.get_dummies(X, categorical_features)
X.columns

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)


from sklearn.preprocessing import StandardScaler
scaler_normal = StandardScaler()
def scaler(data, runtime = False):
    normal_col = ['person_income','person_age','person_emp_length', 'loan_amnt','loan_int_rate','cb_person_cred_hist_length']
    if(runtime == False):
        data.loc[:,normal_col] = scaler_normal.fit_transform(data.loc[:,normal_col])
    else:
        data.loc[:,normal_col] = scaler_normal.transform(data.loc[:,normal_col])
    return data

X_train = scaler(X_train)
X_test = scaler(X_test, True)

rf_model = RandomForestClassifier(max_depth = 5)
rf_model.fit(X_train, y_train)

y_predict = rf_model.predict(X_test)
y_predict


features = {
    "person_home_ownership": ['MORTGAGE', 'OTHER','OWN', 'RENT',],
    "loan_intent": ['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],
    "loan_grade": ['A','B', 'C', 'D', 'E','F', 'G'],
    "cb_person_default_on_file": ['N', 'Y']
}
def preprocess(model_input):
    for feature in features:
        for option in features[feature]:
            selection = model_input[feature]
            if option is selection:
                model_input[f'{feature}_{option}'] = 1
            else:
                model_input[f'{feature}_{option}'] = 0

    model_input.drop([_ for _ in features], inplace = True, axis = 1)
    return model_input


def credit_run(person_age, person_emp_length,person_home_ownership,cb_person_default_on_file,loan_intent,loan_grade,person_income, loan_amnt,
       loan_int_rate, cb_person_cred_hist_length):
    model_input = preprocess(
    pd.DataFrame( { 'person_age': person_age,
                   'person_income': person_income,
                   'person_home_ownership': person_home_ownership,
                   'person_emp_length': person_emp_length,
                   'loan_intent': loan_intent,
                   'loan_grade': loan_grade,
                   'loan_amnt': loan_amnt,
                   'loan_int_rate': loan_int_rate,
                   'cb_person_default_on_file': cb_person_default_on_file,
                   'cb_person_cred_hist_length': cb_person_cred_hist_length
                  }, index = [0]
    ))
    out = rf_model.predict(model_input)
    return "High risk of defaulting" if out[0] == 1 else "Low risk of defaulting"
           
import gradio as gr
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    with gr.Row():
        with gr.Column(scale=1,min_width=400):
            gr.Image("Non_Payment_Logo.png").style(height='5')
        with gr.Column(scale=1,min_width=600):
            person_age=gr.Slider(label="Customer Age(In Years)", minimum=18, maximum=90, step=1)
            Person_Emp_Length=gr.Slider(label="Customer Employement Length(In Years)", minimum=0, maximum=60, step=1)

        with gr.Column(scale=2,min_width=600):
            with gr.Row():
                with gr.Column(scale=1,min_width=500):
                    Home_Ownership_Status=gr.Radio(['MORTGAGE', 'OTHER','OWN', 'RENT'],label="Home Ownership Status")
                with gr.Column(scale=2,min_width=100):
                    Person_Defaulted_in_History=gr.Radio(['N', 'Y'],label="Missed Payment in History")

            with gr.Row():
                with gr.Column(scale=3,min_width=300):
                    Credit_Intent=gr.Dropdown(['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],label="Intent")
                with gr.Column(scale=4,min_width=300):
                    Type_Of_Credit=gr.Dropdown(['A','B', 'C', 'D', 'E','F', 'G'],label="Type Of Credit")
            with gr.Row():
                with gr.Column(scale=3,min_width=300):
                    Person_Income=gr.Number(label="Customer Income(per month)")
                with gr.Column(scale=4,min_width=300):
                    Credit_Amount=gr.Number(label="Premium Amount")
            with gr.Row():
                with gr.Column(scale=3,min_width=300):
                    Interest_Rate=gr.Number(label="Interest Rate")
                with gr.Column(scale=4,min_width=300):
                    Person_Credit_History_Length=gr.Number(label="Customer's Credit History Length")
            with gr.Row():
                with gr.Column():
                    default= gr.Radio(['Low risk of defaulting', 'High risk of defaulting'],label="Chances Of Defaulting")

            btn = gr.Button("PREDICT")
            btn.click(fn=credit_run, inputs=[person_age,Person_Emp_Length,Home_Ownership_Status,Person_Defaulted_in_History,Credit_Intent,Type_Of_Credit,Person_Income,Credit_Amount,Interest_Rate,Person_Credit_History_Length], outputs=[default])
            gr.Examples(
                [["23","2","RENT","N","EDUCATION","A","12000","30000","8.9","6"],
                 ["35","10","OWN","N","MEDICAL","C","20000","40000","8.3","8"],
                 ["28","6","RENT","N","VENTURE","B","32000","30000","8.2","6"],
                 ["32","10","MORTGAGE","Y","HOMEIMPROVEMENT","E","20000","600000","8.6","8"],
                 ["41","18","OWN","Y","PERSONAL","A","10000","300000","14.3","4"],
                 ["30","7","OTHER","Y","MEDICAL","C","13000","1000000","9.5","10"]],
                 
                inputs=[person_age,Person_Emp_Length,Home_Ownership_Status,Person_Defaulted_in_History,Credit_Intent,Type_Of_Credit,Person_Income,Credit_Amount,Interest_Rate,Person_Credit_History_Length]
                )
demo.launch(debug=True)