import streamlit as st
import numpy as np
import pickle
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load Model
model = pickle.load(open("life_expectancy_model.pkl", "rb"))

# Set Page Config
st.set_page_config(
    page_title="Life Expectancy Prediction",
    page_icon="📊",
    layout="centered",
)

# Styling
st.markdown("""
    <style>
        .stApp {
            background-color: #E3F2FD;  /* Light Blue Background */
        }
        .title {
            text-align: center;
            font-size: 28px;
            font-weight: bold;
            color: #2C3E50;
        }
        .subtitle {
            text-align: center;
            font-size: 30px;
            font-weight: bold;
            color: #1E88E5;
            margin-top: 15px;
        }
        .stButton > button {
            width: 100%;
            background-color: #1E88E5;
            color: white;
            font-size: 16px;
            font-weight: bold;
            border-radius: 6px;
            padding: 8px;
            transition: 0.3s;
        }
        .stButton > button:hover {
            background-color: #1565C0; /* Darker Blue on Hover */
        }
        .result-box {
            text-align: center;
            font-size: 22px;
            font-weight: bold;
            color: white;
            padding: 15px;
            border-radius: 8px;
            margin-top: 20px;
            background-color: #388E3C;
            }
    </style>
""", unsafe_allow_html=True)

# Navigation State
if "current_page" not in st.session_state:
    st.session_state.current_page = "Model Report"

# Function to switch pages
def switch_page(page):
    st.session_state.current_page = page

# Sidebar Navigation
st.sidebar.title("Navigation")
if st.sidebar.button("Model Report"):
    switch_page("Model Report")
if st.sidebar.button("Hands-on Model"):
    switch_page("Hands-on Model")

## Importing Data
data = pd.read_csv("Life Expectancy Data.csv") 
data.columns = data.columns.str.strip()

# Model Report Page
if st.session_state.current_page == "Model Report":
    st.markdown("<h1 class='title'>Model Report</h1>", unsafe_allow_html=True)

    st.image("images/Life_Expectanccy.webp", 
         caption="Life Expectancy Prediction Overview", 
         use_container_width=True)
    
    st.markdown("<p class='subtitle'>Explore different stages of the Life Expectancy project</p>", unsafe_allow_html=True)

    if st.button("▶ **Problem Statement**"):
        switch_page("Problem Statement")
    if st.button("▶ **Data Collection**"):
        switch_page("Data Collection")
    if st.button("▶ **Simple EDA**"):
        switch_page("Simple EDA")
    if st.button("▶ **Data Pre-processing**"):
        switch_page("Data Pre-processing")
    if st.button("▶ **Exploratory Data Analysis**"):
        switch_page("EDA")
    if st.button("▶ **Model Building**"):
        switch_page("Model Building")
    if st.button("▶ **Final Model**"):
        switch_page("Final Model")


# Individual Sections
elif st.session_state.current_page == "Problem Statement":
    st.markdown("<h1 class='title'>Problem Statement</h1>", unsafe_allow_html=True)

    st.markdown("""
        <h5 style="text-align: center; margin-top: 20px;">
            The Goal of this project is to build a predictive model that estimates the Life Expectancy of a country 
            based on multiple influencing factors such as health indicators, economic conditions, and social parameters.
        </h5>
    """, unsafe_allow_html=True)

    st.markdown("<br>", unsafe_allow_html=True) 

    st.image("images/problem_statement.png", 
         caption="Life Expectancy Prediction Overview", 
         use_container_width=True)


    if st.button("🔙 Go Back to Model Report"):
        switch_page("Model Report")

elif st.session_state.current_page == "Data Collection":
    st.markdown("<h1 class='title'>Data Collection</h1>", unsafe_allow_html=True)

    st.markdown("""
        <h5 style="text-align: center; margin-top: 20px;">
            The dataset used in this project is sourced from Kaggle, containing information on life expectancy across 
            different countries along with various health, economic, and demographic factors.
        </h5>
    """, unsafe_allow_html=True)

    st.markdown("<br>", unsafe_allow_html=True)  

    st.markdown("""
        <h5 style="text-align: center; margin-top: 10px;">
            📌 <a href="https://www.kaggle.com/datasets/kumarajarshi/life-expectancy-who" target="_blank" style="font-weight: bold; color: #007BFF; text-decoration: none;">
            Click here to access the dataset on Kaggle</a>
        </h5>
    """, unsafe_allow_html=True)


    st.markdown("<h2 class='subtitle' style='text-align: center; margin-top: 20px;'>Dataset Overview</h2>", unsafe_allow_html=True)
    
    st.markdown("""
        <h5 style="text-align: center; margin-top: 15px; margin-bottom: 20px;">
            The dataset consists of <b>2938 rows</b> and <b>22 columns</b>, capturing crucial indicators such as life expectancy, 
            mortality rates, GDP, schooling, immunization rates, and more. Below is a summary of the dataset features:
        </h5>
    """, unsafe_allow_html=True)

    st.markdown("<br>", unsafe_allow_html=True)  

    data_info = """
        <div style= "font-size: 16px; background-color: #F5F5F5; padding: 15px; border-radius: 10px;">
            • <b>Country:</b> Name of the country (Categorical)<br>
            • <b>Year:</b> Year of observation (Numerical)<br>
            • <b>Status:</b> Developing or Developed country (Categorical)<br>
            • <b>Life Expectancy:</b> Average age a person is expected to live (Numerical)<br>
            • <b>Adult Mortality:</b> Probability of dying between 15-60 years per 1000 population (Numerical)<br>
            • <b>Infant Deaths:</b> Number of infant deaths per 1000 live births (Numerical)<br>
            • <b>Alcohol:</b> Alcohol consumption per capita (Numerical)<br>
            • <b>Percentage Expenditure:</b> Government expenditure on health as a percentage of GDP (Numerical)<br>
            • <b>Hepatitis B:</b> Immunization coverage for Hepatitis B (Numerical)<br>
            • <b>Measles:</b> Number of reported measles cases per year (Numerical)<br>
            • <b>BMI:</b> Average Body Mass Index of the population (Numerical)<br>
            • <b>Under-five Deaths:</b> Number of deaths under the age of five per 1000 live births (Numerical)<br>
            • <b>Polio:</b> Immunization coverage for Polio (Numerical)<br>
            • <b>Total Expenditure:</b> Total health expenditure as a percentage of GDP (Numerical)<br>
            • <b>Diphtheria:</b> Immunization coverage for Diphtheria (Numerical)<br>
            • <b>HIV/AIDS:</b> Death rate due to HIV/AIDS per 100,000 people (Numerical)<br>
            • <b>GDP:</b> Gross Domestic Product per capita (Numerical)<br>
            • <b>Population:</b> Total population of the country (Numerical)<br>
            • <b>Thinness 1-19 Years:</b> Percentage of thin individuals aged 1-19 years (Numerical)<br>
            • <b>Thinness 5-9 Years:</b> Percentage of thin individuals aged 5-9 years (Numerical)<br>
            • <b>Income Composition:</b> Human development index based on income composition (Numerical)<br>
            • <b>Schooling:</b> Average number of years of schooling (Numerical)<br>
        </div>
    """
    
    st.markdown(data_info, unsafe_allow_html=True)

    st.markdown("<br>", unsafe_allow_html=True)  

    if st.button("🔙 Go Back to Model Report"):
        switch_page("Model Report")

       
        
elif st.session_state.current_page == "Simple EDA":
    st.markdown("<h1 class='title'>Simple Exploratory Data Analysis</h1>", unsafe_allow_html=True)

    st.markdown("""
        <h5 style="text-align: center; margin-top: 20px;">
            Exploratory Data Analysis (EDA) helps in understanding the structure, patterns, and missing values in the dataset.
            Below is an initial preview of the data, followed by a missing values summary.
        </h5>
    """, unsafe_allow_html=True)

    st.markdown("<br>", unsafe_allow_html=True)

    # Display dataset sample
    st.markdown("<h3 class='subtitle' style='text-align: center;'>Sample Dataset</h3>", unsafe_allow_html=True)
    st.dataframe(data.head()) 

    st.markdown("<br>", unsafe_allow_html=True)

    # Display missing values count
    st.markdown("<h3 class='subtitle' style='text-align: center;'>Missing Values Summary</h3>", unsafe_allow_html=True)
    
    missing_values = data.isna().sum().reset_index()
    missing_values.columns = ["Column Name", "Missing Values"]
    
    col1, col2, col3 = st.columns([1, 2, 1])

    with col2:  
        st.dataframe(missing_values)

    st.markdown("<br>", unsafe_allow_html=True)

    # Display missing values count
    st.markdown("<h3 class='subtitle' style='text-align: center;'>Data Description</h3>", unsafe_allow_html=True)
    
    st.dataframe(data.describe()) 

    st.markdown("<br>", unsafe_allow_html=True)

    # Add Boxplot Visualizations
    st.markdown("<h3 class='subtitle' style='text-align: center;'>Boxplots for Data Distribution</h3>", unsafe_allow_html=True)

    # Define columns for visualization
    columns = ['Life expectancy', 'Adult Mortality',
               'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',
               'Measles', 'BMI', 'under-five deaths', 'Polio', 'Total expenditure',
               'Diphtheria', 'HIV/AIDS', 'GDP', 'Population', 'thinness  1-19 years',
               'thinness 5-9 years', 'Income composition of resources', 'Schooling']

    # Matplotlib figure setup
    fig, axes = plt.subplots(nrows=10, ncols=2, figsize=(12, 30))  # Adjust grid size
    axes = axes.flatten()  

    for i, col in enumerate(columns):
        sns.boxplot(x=data[col], ax=axes[i], color="skyblue")  # Create boxplots
        axes[i].set_title(f'Boxplot of {col}', fontsize=12)  
        axes[i].set_xlabel("")  

    plt.tight_layout()  
    st.pyplot(fig) 

    st.markdown("<br>", unsafe_allow_html=True)
    

    if st.button("🔙 Go Back to Model Report"):
        switch_page("Model Report")    

# Hands-on Model Page
elif st.session_state.current_page == "Hands-on Model":
    st.title("Hands-on Model")
    st.write("Provide inputs to predict Life Expectancy.")

    col1, col2 = st.columns(2)
    with col1:
        year = st.slider("Year", 2000, 2015, 2008)
        status = st.radio("Status", ["Developing", "Developed"], horizontal=True)
        status = 1 if status == "Developed" else 0
        adult_mortality = st.slider("Adult Mortality Rate", 1, 723, 144)
        infant_deaths = st.slider("Infant Deaths", 0, 1800, 3)
        alcohol = st.slider("Alcohol Consumption", 0.01, 17.87, 4.55)
        percentage_expenditure = st.slider("Percentage Expenditure", 0.0, 19479.91, 738.25)
        hepatitis_b = st.slider("Hepatitis B Immunization (%)", 1, 99, 83)
        measles = st.slider("Measles Cases", 0, 212183, 2419)
        bmi = st.slider("BMI", 1.0, 87.3, 38.3)
        polio = st.slider("Polio Immunization (%)", 3, 99, 82)

    with col2:
        under_five_deaths = st.slider("Under-Five Deaths", 0, 2500, 4)
        total_expenditure = st.slider("Total Healthcare Expenditure (%)", 0.37, 17.6, 5.92)
        diphtheria = st.slider("Diphtheria Immunization (%)", 2, 99, 82)
        hiv_aids = st.slider("HIV/AIDS Prevalence Rate", 0.1, 50.6, 1.74)
        gdp = st.slider("GDP per Capita", 1.68, 119172.7, 6611.52)
        population = st.slider("Population", 34, 1293859000, 10230850)
        thinness_1_19 = st.slider("Thinness 1-19 years (%)", 0.1, 27.7, 4.83)
        thinness_5_9 = st.slider("Thinness 5-9 years (%)", 0.1, 28.6, 4.86)
        income_composition = st.slider("Income Composition of Resources", 0.0, 0.948, 0.63)
        schooling = st.slider("Schooling (Years)", 0.0, 20.7, 11.99)

    if st.button("Predict Life Expectancy"):
        features = np.array([[year, status, adult_mortality, infant_deaths, alcohol, percentage_expenditure,
                              hepatitis_b, measles, bmi, under_five_deaths, polio, total_expenditure,
                              diphtheria, hiv_aids, gdp, population, thinness_1_19, thinness_5_9,
                              income_composition, schooling]])
        
        prediction = model.predict(features)[0]

        st.markdown(
            f"""
            <div class="result-box">
                Predicted Life Expectancy: <b>{prediction:.2f} years</b>
            </div>
            """,
            unsafe_allow_html=True,
        )

    if st.button("⬅ **Back to Model Report**"):
        switch_page("Model Report")