app.py

import streamlit as st
import pandas as pd
import numpy as np

from src.data_loader import load_data
from src.preprocessing import feature_engineering, encode_data
from src.model import train_model
from src.predict import make_prediction
from src.utils import calculate_emi

# -----------------------------------------------------------------------------
# PAGE CONFIG
# -----------------------------------------------------------------------------
st.set_page_config(page_title="Loan Prediction System", page_icon="🏦", layout="centered")

# -----------------------------------------------------------------------------
# NAVIGATION STATE
# -----------------------------------------------------------------------------
if 'page' not in st.session_state:
    st.session_state['page'] = 'Home'

c1, c2, c3, c4, c5 = st.columns([2,1,1,1,2])
with c2:
    if st.button("Home"): st.session_state['page'] = 'Home'
with c3:
    if st.button("Predict"): st.session_state['page'] = 'Predict'
with c4:
    if st.button("About"): st.session_state['page'] = 'About'

st.markdown("---")

# -----------------------------------------------------------------------------
# LOAD + TRAIN
# -----------------------------------------------------------------------------
DATA_PATH = "data/train.csv"

df = load_data(DATA_PATH)

if df is not None:
    df = feature_engineering(df)
    df, encoders, target_encoder = encode_data(df)

    X = df.drop("Loan_Status", axis=1)
    y = df["Loan_Status"]

    feature_columns = X.columns.tolist()
    model = train_model(X, y)
else:
    st.error("Dataset not found")

# -----------------------------------------------------------------------------
# HOME PAGE
# -----------------------------------------------------------------------------
if st.session_state['page'] == "Home":
    st.title("Loan Approval Prediction System")
    st.subheader("Using Machine Learning")

    st.markdown("### About This Application")
    st.info("""
    Welcome to the next generation of banking technology. This application utilizes advanced 
    **Machine Learning** algorithms to automate the loan eligibility assessment process. 
    By analyzing key financial indicators—such as **Income, Credit History, and Loan Term**—our 
    system provides an instant, objective, and data-driven prediction.
    """)

    st.markdown("### Why Use This System?")
    col1, col2 = st.columns(2)

    with col1:
        st.success("⚡ **Real-Time Analysis**\n\nGet instant results without manual verification.")
        st.success("🛡️ **Financial Guardrails**\n\nDetects risky applications automatically.")

    with col2:
        st.success("🎯 **High Accuracy**\n\nUses Random Forest model for reliable prediction.")
        st.success("💡 **Smart Suggestions**\n\nGives tips to improve approval chances.")

    st.markdown("---")

    st.markdown("""
    ### How It Works
    1. Click Predict
    2. Fill details
    3. Click Predict Status
    4. Get result instantly
    """)

# -----------------------------------------------------------------------------
# PREDICT PAGE
# -----------------------------------------------------------------------------
elif st.session_state['page'] == "Predict":
    st.title("📋 Loan Application Form")

    if df is None:
        st.error("Dataset not found")
    else:
        with st.form("prediction_form"):
            st.subheader("Applicant Details")

            c1, c2 = st.columns(2)

            with c1:
                gender = st.selectbox("Gender", ["Male", "Female"])
                married = st.selectbox("Married", ["No", "Yes"])
                dependents = st.selectbox("Dependents", ["0", "1", "2", "3+"])
                education = st.selectbox("Education", ["Graduate", "Not Graduate"])
                self_employed = st.selectbox("Self Employed", ["No", "Yes"])

            with c2:
                applicant_income = st.number_input("Applicant Income (Monthly ₹)", value=5000)
                coapplicant_income = st.number_input("Co-Applicant Income (Monthly ₹)", value=0)
                loan_amount = st.number_input("Loan Amount (₹)", value=100000)
                loan_term_years = st.number_input("Loan Term (Years)", value=15)
                property_area = st.selectbox("Property Area", ["Urban", "Semiurban", "Rural"])
                cibil_score = st.number_input("CIBIL Score", 300, 900, 750)

            submit = st.form_submit_button("Predict Status")

        if submit:
            loan_amt_k = loan_amount / 1000
            loan_term_m = loan_term_years * 12
            total_income = applicant_income + coapplicant_income

            model_emi = (loan_amt_k * 1000) / loan_term_m
            balance_income = total_income - model_emi

            credit_history = 1.0 if cibil_score >= 600 else 0.0

            input_data = {
                "Gender": gender,
                "Married": married,
                "Dependents": dependents,
                "Education": education,
                "Self_Employed": self_employed,
                "ApplicantIncome": applicant_income,
                "CoapplicantIncome": coapplicant_income,
                "LoanAmount": loan_amt_k,
                "Loan_Amount_Term": loan_term_m,
                "Credit_History": credit_history,
                "Property_Area": property_area,
                "Total_Income": total_income,
                "EMI": model_emi,
                "Balance_Income": balance_income
            }

            result, confidence = make_prediction(
                input_data, model, encoders, target_encoder, feature_columns
            )

            st.markdown("### Result")

            if result == "Y":
                st.success(f"✅ Approved ({confidence:.2f}%)")
            else:
                st.error(f"❌ Rejected ({confidence:.2f}%)")

# -----------------------------------------------------------------------------
# ABOUT PAGE
# -----------------------------------------------------------------------------
elif st.session_state['page'] == "About":
    st.title("About the Project")

    # 1. PROBLEM
    st.error("""
    **The Problem: Manual Underwriting**
    
    Traditionally, banks relied on manual verification processes which had major disadvantages:
    - **High Turnaround Time:** It took days or weeks to process a single application.
    - **Human Bias:** Decisions often varied from officer to officer.
    - **Static Rules:** Simple rules failed to see the bigger picture.
    """)

    st.write("")

    # 2. SOLUTION
    st.success("""
    **The Solution: Intelligent Automation**
    
    This project replaces the manual process with a **Hybrid Machine Learning Architecture**. 
    It combines strict financial logic (Guardrails) with AI pattern recognition (Random Forest) 
    to make safer, faster decisions.
    """)

    st.write("")

    # 3. WORKFLOW
    st.markdown("### 🔄 Project Workflow")
    st.info("""
    This system was built in **4 key stages**:
    
    1. **Data Analysis (Jupyter Notebook):** Data cleaning and preprocessing  
    2. **Model Training:** Random Forest model (~81% accuracy)  
    3. **Backend Logic:** Financial guardrails implementation  
    4. **Frontend:** Streamlit UI for user interaction  
    """)

    st.divider()

    # 4. TECH SPECS
    st.subheader("🛠️ Technical Architecture")

    c1, c2 = st.columns(2)

    with c1:
        st.markdown("**Machine Learning:**")
        st.caption("""
        - Algorithm: Random Forest Classifier  
        - Trees: 200 Estimators  
        - Accuracy: ~81%  
        """)

    with c2:
        st.markdown("**Tech Stack:**")
        st.caption("""
        - Python  
        - Streamlit  
        - Pandas  
        - Scikit-learn  
        """)