main.py

from fastapi import FastAPI, Request, Form
from fastapi.responses import HTMLResponse, JSONResponse
import joblib
import numpy as np
from pydantic import BaseModel
from fastapi.staticfiles import StaticFiles

app = FastAPI()

# Load trained model
clf = joblib.load("loan_prediction_model.pkl")

# Mount static files for Bootstrap and JavaScript
# app.mount("/static", StaticFiles(directory="static"), name="static")

# Define input schema
class LoanApplication(BaseModel):
    income: float
    age: int
    experience: int
    marital_status: int
    house_ownership: int
    car_ownership: int
    profession: int
    city: int
    current_job_years: int
    current_house_years: int

# Serve HTML UI at root ("/")
@app.get("/", response_class=HTMLResponse)
async def serve_home():
    with open("index.html", "r", encoding="utf-8") as file:
        return HTMLResponse(content=file.read())

# API endpoint to predict risk
@app.post("/predict")
async def predict_risk(data: LoanApplication):
    input_data = np.array([[data.income, data.age, data.experience, data.marital_status,
                             data.house_ownership, data.car_ownership, data.profession,
                             data.city, data.current_job_years, data.current_house_years]])
    prediction = clf.predict(input_data)[0]
    return JSONResponse(content={"prediction": int(prediction)})