import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import streamlit as st
from streamlit_option_menu import option_menu
import pickle
import catboost
from sklearn.impute import SimpleImputer
import requests

# Custom CSS styles for the top bar
st.markdown(
    """
    <style>
    .top-bar {
        background-color: #FF4C1B;
        color: white;
        padding: 1rem;
        text-align: center;
    }
    .top-bar a {
        text-decoration: none;
        color: white;
        margin: 10px;
    }
    </style>
    """,
    unsafe_allow_html=True,
)

def home_page():
    # Page title and banner image
    st.title("Income Prediction App")
    st.image("https://i.ytimg.com/vi/WULwst0vW8g/maxresdefault.jpg")
    
    st.write("""
    The Income Prediction Challenge for Azubian is a machine learning project that aims to predict whether an individual's income falls above or below a specific income threshold. This information can be used to monitor income inequality and inform policy decisions.
    """)

    # The Problem of Income Inequality
    st.header("The Problem: Income Inequality 💸")
    st.write(
        """
        Income inequality, a pervasive challenge that hinders economic progress and social well-being, demands innovative solutions. The "Income Prediction Challenge for Azubian" tackles this issue head-on, harnessing the power of machine learning to predict individual income levels.

        **Key Challenges of Income Inequality:** ⚠

        1. **Limited Economic Mobility:** 📉

            Individuals from lower-income households often face barriers to education and professional growth, perpetuating income disparities.

        2. **Healthcare Disparities:** 🩺

            Income inequality often translates into unequal access to quality healthcare, leading to adverse health outcomes for lower-income individuals.

        3. **Education Gaps:** 📚

            Children from low-income households may have limited access to quality education, hindering their future opportunities.

        4. **Social Unrest:** 💢

            Extreme income inequality can fuel social unrest as individuals feel disenfranchised and discouraged.

        5. **Economic Impact:** 📉

            Income inequality impedes economic growth by reducing aggregate demand and creating economic instability.

        6. **Policymaking Challenges:** 🧩

            Policymakers require accurate data and insights to formulate effective strategies for reducing income inequality.
            """)

    
def solution():
    # Page title
    st.title("Income Prediction Solution")
    st.image("https://d2gg9evh47fn9z.cloudfront.net/1600px_COLOURBOX15103453.jpg")

    # Solution Overview
    st.header("Solution 💡: Combating Income Inequality with Data-Driven Solutions 📈 ")
    st.write("""

        The "Income Prediction Challenge for Azubian" utilizes machine learning to predict individual income levels, providing valuable data to policymakers for informed action. This data-driven approach offers several advantages:

        * **Cost-Effectiveness:** 💰

            Machine learning models are more cost-effective than traditional census methods.

        * **Timeliness:** ⏱️

            Income predictions can be generated frequently, enabling timely interventions.

        * **Scalability:** 🚀

            Machine learning models can be scaled to predict incomes for large populations, making them applicable to a wide range of scenarios.
        """)
    
    st.header("Objectives: 🎯")
    st.write("""
       1. **Income Prediction Model:** Develop a robust machine learning model to accurately predict individual income levels.

       2. **Economic Inequality Mitigation:** Empower policymakers with data-driven insights to effectively address income inequality.

       3. **Cost and Accuracy Improvement:** Enhance income-level monitoring through a cost-effective and accurate method compared to traditional census methods.

        Join us in tackling income inequality with data-driven solutions!
        """)

    # Model Description
    st.header("Model Description")
    st.write("""
    **Model Training:**
    *Trained on a dataset of demographic and socioeconomic factors influencing income levels 📊
    
    * A [CatBoost Classifier](https://catboost.ai/en/docs/concepts/python-reference_catboostclassifier) supervised learning algorithm used for model development ⚙️
    
    **Model Evaluation:**
    * Performance assessed using metrics like accuracy, precision, recall, and F1 score 📈📊
    
    * Metrics evaluate the model's ability to correctly classify individual income levels ☑️
    """)

    # Impact and Benefits
    st.header("Impact and Benefits 📈")
    st.write("""
    
    **Empowering Policymakers and Promoting Equitable Growth 📈**

    By providing accurate and timely insights into income distribution, we can empower policymakers to make informed decisions that:

    * Enhance understanding of income patterns 📊

    * Identify areas with high income inequality 📍

    * Target interventions to address income gaps 🎯

    * Effectively allocate resources to poverty reduction 💰

    * Promote economic mobility for individuals from low-income backgrounds ⬆️

    Overall, this tool has the potential to make a meaningful contribution to the fight against income inequality and promote a more just and equitable society. ⚖️
    """)


def perform_eda():
    st.title("Exploratory Data Analysis")
    st.write("""
        📊📈 Welcome to the Exploratory Data Analysis for the "Income Prediction" Project! 📈📊
        Dive into the wealth of data and uncover insights about income prediction. Explore the data and understand the factors that contribute to an individual's income level. Let's begin our data-driven journey! 💰🔍
        """)

    # Show the Power bi dashboard
    power_bi()

def power_bi():
    """
    Embeds the Power BI report with specified dimensions and full-screen height.
    """

    st.subheader("Exploring Income Data")
    st.write("Let's dive deeper into the data to understand income distribution and relationships between variables.")

    # Embed the Power BI iframe with specified dimensions
    power_bi_html = """
    <iframe title="Report Section" width="600" height="373.5" src="https://app.powerbi.com/view?r=eyJrIjoiZDNjMmExZjYtMWU2NS00NTBjLTk4Y2EtYmQ2MWU2OWMwODMyIiwidCI6IjQ0ODdiNTJmLWYxMTgtNDgzMC1iNDlkLTNjMjk4Y2I3MTA3NSJ9" frameborder="0" allowFullScreen="true"></iframe>
    """

    st.components.v1.html(power_bi_html)

    # Ensure full-screen height using CSS
    with st.empty():
        st.write("""
        <style>
            html, body {
              height: 100%;
              margin: 0;
              padding: 0;
            }

            iframe {
              width: 100%;
              height: 100vh;
            }
        </style>
        """, unsafe_allow_html=True)


def prediction():

    # Load the saved model and unique values:
    with open("model_and_key_components.pkl", "rb") as f:
        components = pickle.load(f)
    
    # Extract the individual components
    dt_model = components["model"]
    unique_values = components["unique_values"]
    
    
    st.image("https://i.ytimg.com/vi/WULwst0vW8g/maxresdefault.jpg")
    st.title("Income Prediction App")
    
    # Sidebar with input field descriptions
    st.sidebar.header("Description of the Required Input Fields")
    st.sidebar.markdown("**Age**: Enter the age of the individual (e.g., 25, 42, 57).")
    st.sidebar.markdown("**Gender**: Select the gender of the individual (e.g., Male, Female).")
    st.sidebar.markdown("**Education**: Choose the highest education level of the individual (e.g., Bachelors Degree, High School Graduate, Masters Degree).")
    st.sidebar.markdown("**Worker Class**: Select the class of worker for the individual (e.g., Private, Government, Self-employed).")
    st.sidebar.markdown("**Marital Status**: Choose the marital status of the individual (e.g., Married, Never married, Divorced).")
    st.sidebar.markdown("**Race**: Select the race of the individual (e.g., White, Black, Asian-Pac-Islander).")
    st.sidebar.markdown("**Hispanic Origin**: Choose the Hispanic origin of the individual (e.g., Mexican, Puerto Rican, Cuban).")
    st.sidebar.markdown("**Full/Part-Time Employment**: Select the employment status as full-time or part-time (e.g., Full-time schedules, Part-time schedules).")
    st.sidebar.markdown("**Wage Per Hour**: Enter the wage per hour of the individual (numeric value, e.g., 20.50).")
    st.sidebar.markdown("**Weeks Worked Per Year**: Specify the number of weeks the individual worked in a year (numeric value, e.g., 45).")
    st.sidebar.markdown("**Industry Code**: Choose the category code of the industry where the individual works (e.g., Category 1, Category 2).")
    st.sidebar.markdown("**Major Industry Code**: Select the major industry code of the individual's work (e.g., Industry A, Industry B).")
    st.sidebar.markdown("**Occupation Code**: Choose the category code of the occupation of the individual (e.g., Category X, Category Y).")
    st.sidebar.markdown("**Major Occupation Code**: Select the major occupation code of the individual (e.g., Occupation 1, Occupation 2).")
    st.sidebar.markdown("**Total Employed**: Specify the number of persons worked for the employer (numeric value, e.g., 3, 5).")
    st.sidebar.markdown("**Household Stat**: Choose the detailed household and family status of the individual (e.g., Single, Married-civilian spouse present).")
    st.sidebar.markdown("**Household Summary**: Select the detailed household summary (e.g., Child under 18 never married, Spouse of householder).")
    st.sidebar.markdown("**Veteran Benefits**: Choose whether the individual receives veteran benefits (Yes or No).")
    st.sidebar.markdown("**Tax Filer Status**: Select the tax filer status of the individual (e.g., Single, Joint both 65+).")
    st.sidebar.markdown("**Gains**: Specify any gains the individual has (numeric value, e.g., 1500.0).")
    st.sidebar.markdown("**Losses**: Specify any losses the individual has (numeric value, e.g., 300.0).")
    st.sidebar.markdown("**Dividends from Stocks**: Specify any dividends from stocks for the individual (numeric value, e.g., 120.5).")
    st.sidebar.markdown("**Citizenship**: Select the citizenship status of the individual (e.g., Native, Foreign Born- Not a citizen of U S).")
    st.sidebar.markdown("**Year of Migration**: Enter the year of migration for the individual (numeric value, e.g., 2005).")
    st.sidebar.markdown("**Country of Birth**: Choose the individual's birth country (e.g., United-States, Other).")
    st.sidebar.markdown("**Importance of Record**: Enter the weight of the instance (numeric value, e.g., 0.9).")
    
    # Create the input fields in the order of your DataFrame
    input_data = {
        'age': 0,  # Default values, you can change these as needed
        'gender': unique_values['gender'][0],
        'education': unique_values['education'][0],
        'worker_class': unique_values['worker_class'][0],
        'marital_status': unique_values['marital_status'][0],
        'race': unique_values['race'][0],
        'is_hispanic': unique_values['is_hispanic'][0],
        'employment_commitment': unique_values['employment_commitment'][0],
        'employment_stat': unique_values['employment_stat'][0],
        'wage_per_hour': 0,  # Default value
        'working_week_per_year': 0,  # Default value
        'industry_code': 0,  # Default value
        'industry_code_main': unique_values['industry_code_main'][0],
        'occupation_code': 0,  # Default value
        'occupation_code_main': unique_values['occupation_code_main'][0],
        'total_employed': 0,  # Default value
        'household_stat': unique_values['household_stat'][0],
        'household_summary': unique_values['household_summary'][0],
        'vet_benefit': 0,  # Default value
        'tax_status': unique_values['tax_status'][0],
        'gains': 0,  # Default value
        'losses': 0,  # Default value
        'stocks_status': 0,  # Default value
        'citizenship': unique_values['citizenship'][0],
        'mig_year': 0,
        'country_of_birth_own': 'United-States',
        'importance_of_record': 0.0  # Default value
    }
    
    # Create the input fields
    col1, col2, col3 = st.columns(3)
    
    with col1:
        input_data['age'] = st.number_input("Age", min_value=0, key='age')
        input_data['gender'] = st.selectbox("Gender", unique_values['gender'], key='gender')
        input_data['education'] = st.selectbox("Education", unique_values['education'], key='education')
        input_data['worker_class'] = st.selectbox("Class of Worker", unique_values['worker_class'], key='worker_class')
        input_data['marital_status'] = st.selectbox("Marital Status", unique_values['marital_status'], key='marital_status')
        input_data['race'] = st.selectbox("Race", unique_values['race'], key='race')
        input_data['is_hispanic'] = st.selectbox("Hispanic Origin", unique_values['is_hispanic'], key='is_hispanic')
        input_data['employment_commitment'] = st.selectbox("Full/Part-Time Employment", unique_values['employment_commitment'], key='employment_commitment')
        input_data['employment_stat'] = st.selectbox("Has Own Business Or Is Self Employed", unique_values['employment_stat'], key='employment_stat')
        input_data['wage_per_hour'] = st.number_input("Wage Per Hour", min_value=0, key='wage_per_hour')
    
    with col2:
        input_data['working_week_per_year'] = st.number_input("Weeks Worked Per Year", min_value=0, key='working_week_per_year')
        input_data['industry_code'] = st.selectbox("Category Code of Industry", unique_values['industry_code'], key='industry_code')
        input_data['industry_code_main'] = st.selectbox("Major Industry Code", unique_values['industry_code_main'], key='industry_code_main')
        input_data['occupation_code'] = st.selectbox("Category Code of Occupation", unique_values['occupation_code'], key='occupation_code')
        input_data['occupation_code_main'] = st.selectbox("Major Occupation Code", unique_values['occupation_code_main'], key='occupation_code_main')
        input_data['total_employed'] = st.number_input("Number of Persons Worked for Employer", min_value=0, key='total_employed')
        input_data['household_stat'] = st.selectbox("Detailed Household and Family Status", unique_values['household_stat'], key='household_stat')
        input_data['household_summary'] = st.selectbox("Detailed Household Summary", unique_values['household_summary'], key='household_summary')
        input_data['vet_benefit'] = st.selectbox("Veteran Benefits", unique_values['vet_benefit'], key='vet_benefit')
    
    with col3:
        input_data['tax_status'] = st.selectbox("Tax Filer Status", unique_values['tax_status'], key='tax_status')
        input_data['gains'] = st.number_input("Gains", min_value=0, key='gains')
        input_data['losses'] = st.number_input("Losses", min_value=0, key='losses')
        input_data['stocks_status'] = st.number_input("Dividends from Stocks", min_value=0, key='stocks_status')
        input_data['citizenship'] = st.selectbox("Citizenship", unique_values['citizenship'], key='citizenship')
        input_data['mig_year'] = st.selectbox("Migration Year", unique_values['mig_year'], key='migration_year')
        input_data['country_of_birth_own'] = st.selectbox("Country of Birth", unique_values['country_of_birth_own'], key='country_of_birth_own')
        input_data['importance_of_record'] = st.number_input("Importance of Record", min_value=0, key='importance_of_record')
    
    # Button to make predictions
    if st.button("Predict"):
        # Transform the input data to a DataFrame for prediction
        input_df = pd.DataFrame([input_data])
    
        # Make predictions
        prediction = dt_model.predict(input_df)
        prediction_proba = dt_model.predict_proba(input_df)
    
        # Display prediction result
        st.subheader("Prediction")
        if prediction[0] == 1:
            st.success("This individual is predicted to have an income of over $50K.")
        else:
            st.error("This individual is predicted to have an income of under $50K")
    
        # Show prediction probability
        st.subheader("Prediction Probability")
        st.write(f"The probability of the individual having an income over $50K is: {prediction_proba[0][1]:.2f}")
          

# Add navigation to the selected page
selected_page = st.selectbox("Select a page", ["Home", "Solution", "EDA", "Predict Income"])

if selected_page == "Home":
    home_page()
elif selected_page == "Solution":
    solution()   
elif selected_page == "EDA":
    perform_eda()
else:
    prediction()