import streamlit as st
import pandas as pd 
import joblib
import numpy as np
from sklearn.cluster import KMeans
import time


Home, Eda, App = st.tabs(['Home', 'Eda', 'App'])

with Home:
  st.title("Milestone 2 - Create Model Loan Prediction")
  st.subheader("Problem Statement")
  st.write('Develop a robust machine learning model for loan prediction that accurately classifies loan applications as either approved or rejected, while also segmenting the approved loans into three risk categories: low risk, moderate risk, and high risk. The model should leverage historical loan data, applicant information, and credit analysis to make informed decisions, enabling the lending institution to streamline the loan approval process and mitigate potential credit risks effectively.')
  st.image('loan.jpg')
  bisnis = st.container()
  with bisnis:
    st.subheader("Business Implcation")
    st.write("**1. Enhanced Loan Approval Process:**")
    st.write("Enhancing the loan approval process involves using advanced predictive models to automate and optimize the decision-making process for loan applications. By implementing a loan prediction model, lending institutions can accurately assess the creditworthiness of applicants, predict loan approval outcomes, and expedite the approval process. This results in reduced manual reviews and faster loan decisions, improving operational efficiency and customer experience. The importance of an enhanced loan approval process lies in its ability to streamline operations, save time and resources, and increase the institution's capacity to handle a higher volume of loan applications effectively.")
    st.write("**2. Risk Mitigation and Improved Decision Making:**")
    st.write("Risk mitigation is a critical aspect of lending institutions' operations. The use of predictive models, such as credit risk assessment models, enables lenders to identify high-risk borrowers accurately. By assessing credit risk more effectively, lending institutions can make informed decisions regarding loan approvals, interest rates, and loan terms. Improved decision-making leads to a reduced risk of default and potential losses, enhancing the overall financial stability of the institution. The importance of risk mitigation lies in safeguarding the institution's financial health and ensuring responsible lending practices.")
    st.write("**3. Customer-Centric Approach:**")
    st.write("A customer-centric approach emphasizes meeting the needs and preferences of borrowers. Implementing a loan prediction model can lead to faster loan approvals and more personalized loan terms based on borrowers' credit profiles. Low-risk borrowers may receive more favorable interest rates, encouraging responsible borrowing behavior. The importance of a customer-centric approach is twofold: it enhances customer satisfaction and loyalty while also reducing the institution's exposure to high-risk borrowers, resulting in improved loan portfolio quality.")
    st.write("**4. Improved Portfolio Management:**")
    st.write("Effective portfolio management is crucial for lending institutions to maintain a balanced and diversified loan portfolio. Loan prediction models aid in categorizing loans into different risk segments (low, moderate, high), allowing institutions to optimize their portfolios by strategically allocating resources to various risk categories. A well-managed portfolio reduces the risk of concentration in high-risk loans and increases the potential for profitable returns. The importance of improved portfolio management lies in ensuring long-term financial stability and maximizing returns on investment.")
    st.write("**5. Compliance and Regulatory Alignment:**")
    st.write("Lending institutions operate within a complex regulatory landscape. Predictive models can be designed to incorporate fairness and transparency, aligning lending decisions with regulatory guidelines. This helps prevent discriminatory practices, ensures compliance with fair lending laws, and enhances the institution's reputation. The importance of compliance and regulatory alignment is to protect the institution from legal risks, maintain trust with customers and regulators, and uphold ethical lending standards.")
    st.write("**6. Competitive Advantage:**")
    st.write("Implementing advanced predictive models for loan approvals can provide lending institutions with a competitive advantage in the market. Faster loan processing, accurate risk assessment, and personalized loan offerings attract more borrowers and improve the institution's market positioning. A competitive advantage enables the institution to capture a larger market share, achieve higher customer retention rates, and ultimately drive business growth and profitability.attract more borrowers and enhance the institution's reputation.")

with Eda:
  st.title("Exploratory Data Analysis")
  dataset = st.container()
  analysis = st.container()
  conclusion = st.container()
  
  with dataset:
    st.subheader("Dataset")
    st.text("https://www.kaggle.com/datasets/architsharma01/loan-approval-prediction-dataset")
    df = pd.read_csv('loan_approval_dataset.csv')
    st.dataframe(df)
    st.write('Default Curreny : INR')
  with analysis:  
    st.subheader("Data Overview")
    st.image('data overview.PNG')
    st.subheader("Varibles")
    st.image('variables 1.PNG')
    st.image('variables 2.PNG')
    st.image('variables 3.PNG')
    st.image('variables 4.PNG')
    st.image('variables 5.PNG')
    st.image('variables 6.PNG')
    st.image('variables 7.PNG')
    st.image('variables 8.PNG')
    st.image('variables 9.PNG')
    st.image('variables 10.PNG')
    st.image('variables 11.PNG')
    st.image('variables 12.PNG')
    st.image('variables 13.PNG')
    st.subheader("Interacations")
    st.image('intercation.PNG')
    st.subheader("Correlations")
    st.image('correlation.PNG')
    st.subheader("Missing Value")
    st.image('correlation.PNG')
  with conclusion:
    st.subheader("Conclusion")
    st.image('conclusion.PNG')

with App:
  st.subheader("New user model prediction")
  cluster = joblib.load('cluster.pkl')
  model = joblib.load('all_process.pkl')  
  
  df = pd.read_csv('loan_approval_dataset.csv')
  num_col = [' income_annum', ' loan_amount', ' loan_term', ' cibil_score', ' residential_assets_value', ' commercial_assets_value', ' luxury_assets_value', ' bank_asset_value']
  cluster_data = df.drop(columns=[col for col in df.columns if col not in num_col])
  cluster_df = cluster.fit_transform(cluster_data)
  k_3 = KMeans(n_clusters=3)
  label3 = k_3.fit_transform(cluster_df)
  label = pd.DataFrame(label3)
  df = pd.concat([label.reset_index(drop=True), df], axis=1)
  df.rename(columns={0: "cluster"}, inplace=True)
    
  income_annum = st.slider('income_annum', 0,9999999)
  loan_amount = st.slider('loan_amount', 0,9999999)
  loan_term = st.slider('loan_term', 2, 24)
  cibil_score = st.slider('cibil_score', 0,900)
  residential_assets_value = st.slider('residential_assets_value', 0, 9999999)
  commercial_assets_value = st.slider('commercial_assets_value',0,9999999)
  luxury_assets_value = st.slider('luxury_assets_value',0,9999999)
  bank_asset_value = st.slider('bank_asset_value',0,9999999)
  education = st.selectbox('education',[' Not Graduate', ' Graduate'])
  data = {
    ' income_annum': income_annum,
    ' loan_amount': loan_amount,
    ' loan_term': loan_term,
    ' cibil_score': cibil_score,
    ' residential_assets_value': residential_assets_value,
    ' commercial_assets_value': commercial_assets_value,
    ' luxury_assets_value': luxury_assets_value,
    ' bank_asset_value': bank_asset_value,
    ' education': education,
  }
  input = pd.DataFrame(data, index=[0])
  st.subheader('User Input')
  st.write(input)
  if st.button('Predict'):
    progress_bar = st.progress(0)
    for perc_completed in range(100):
        time.sleep(0.05)
        progress_bar.progress(perc_completed+1)
    num_col = [' income_annum', ' loan_amount', ' loan_term', ' cibil_score', ' residential_assets_value', ' commercial_assets_value', ' luxury_assets_value', ' bank_asset_value']
    def_cluster = input.drop(columns=[col for col in input.columns if col not in num_col])
    def_pca = cluster.transform(def_cluster)
    labelz = k_3.predict(def_pca)
    cluster_names = {
    0: 'Moderate-Risk Customers',
    1: 'Low-Risk Customers',
    2: 'High-Risk Customers'
    }
    cluster_labels = [cluster_names[label] for label in labelz]
    input['cluster'] = cluster_labels
    
    if np.all(labelz == 0):
        st.write('Based on user input, the placement cluster: Moderate-Risk Customers')
    elif np.all(labelz == 1):
        st.write('Based on user input, the placement cluster: Low-Risk Customers')
    elif np.all(labelz == 2):
        st.write('Based on user input, the placement cluster: High-Risk Customers')
    
    prediction = model.predict(input)
    
    if prediction == 0:
        prediction = ' Rejected'
    else:
        prediction = ' Approved'

    st.write('The placement model predicted: ',prediction)