from flask import Flask, render_template, request, jsonify
import gradio as gr
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import warnings

# Ignore all warnings
warnings.filterwarnings("ignore")

# Initialize Flask app
app = Flask(__name__)

# Load Data
df_lending_data = pd.read_csv('Resources/lending_data.csv')

# Prepare Features and Labels
y = df_lending_data['loan_status']
X = df_lending_data.drop(columns=['loan_status'])

# Split Data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

# Train Logistic Regression Model
model = LogisticRegression(max_iter=200, random_state=1)
model.fit(X_train, y_train)

# Gradio Function for Prediction
def predict_loan_status(loan_size, interest_rate, borrower_income, debt_to_income, num_of_accounts, derogatory_marks, total_debt):
    input_data = pd.DataFrame({
        'loan_size': [loan_size],
        'interest_rate': [interest_rate],
        'borrower_income': [borrower_income],
        'debt_to_income': [debt_to_income],
        'num_of_accounts': [num_of_accounts],
        'derogatory_marks': [derogatory_marks],
        'total_debt': [total_debt]
    })
    prediction = model.predict(input_data)
    return "Healthy Loan (0)" if prediction[0] == 0 else "High-Risk Loan (1)"

# Flask route for home page
@app.route('/')
def home():
    return render_template('index.html')

# Flask route for prediction
@app.route('/predict', methods=['POST'])
def predict():
    # Get form data
    loan_size = float(request.form['loan_size'])
    interest_rate = float(request.form['interest_rate'])
    borrower_income = float(request.form['borrower_income'])
    debt_to_income = float(request.form['debt_to_income'])
    num_of_accounts = int(request.form['num_of_accounts'])
    derogatory_marks = int(request.form['derogatory_marks'])
    total_debt = float(request.form['total_debt'])

    # Prepare input data
    input_data = pd.DataFrame({
        'loan_size': [loan_size],
        'interest_rate': [interest_rate],
        'borrower_income': [borrower_income],
        'debt_to_income': [debt_to_income],
        'num_of_accounts': [num_of_accounts],
        'derogatory_marks': [derogatory_marks],
        'total_debt': [total_debt]
    })

    # Make prediction
    prediction = model.predict(input_data)
    result = "Healthy Loan (0)" if prediction[0] == 0 else "High-Risk Loan (1)"

    return render_template('index.html', prediction_text=f'Prediction: {result}')

# Flask route to serve Gradio interface
@app.route('/gradio')
def gradio_interface():
    # Create the Gradio interface
    interface = gr.Interface(
        fn=predict_loan_status,
        inputs=[
            gr.Number(label="Loan Size"),
            gr.Number(label="Interest Rate"),
            gr.Number(label="Borrower Income"),
            gr.Number(label="Debt-to-Income Ratio"),
            gr.Number(label="Number of Accounts"),
            gr.Number(label="Derogatory Marks"),
            gr.Number(label="Total Debt"),
        ],
        outputs="text",
        title="Loan Status Prediction",
        description="Input loan details to predict whether the loan is healthy or high-risk."
    )

    # Launch Gradio interface on a different port
    return interface.launch(share=True, inline=True)

# Run Flask app
if __name__ == "__main__":
    app.run(debug=True)