import streamlit as st
import pandas as pd
import plotly.express as px
#import bar_chart_race as bcr
from raceplotly.plots import barplot



# Configuration and Constants
COUNTRY_MAPPING = {
    "Italy, San Marino and the Holy See": "Italy",
    "France and Monaco": "France",
    "Belgium and Luxembourg": "Belgium",
    "China (mainland)": "China",
    "United States of America": "United States",
    "United Kingdom of Great Britain and Northern Ireland": "United Kingdom",
    "Spain and Andorra": "Spain"
}

DEFAULT_COUNTRIES = ["Italy", "France", "Germany"]
YEAR_RANGE = (2000, 2020)

# Data Loading and Processing Functions
@st.cache_data
def load_data(sheet_name):
    df = pd.read_excel("dati/fossilco2emission.xlsx", sheet_name=sheet_name)
    df_mapped = df.copy()
    df_mapped['Country'] = df_mapped['Country'].replace(COUNTRY_MAPPING)
    
    year_cols = list(range(YEAR_RANGE[0], YEAR_RANGE[1] + 1))
    selected_cols = ['Country'] + year_cols
    return df_mapped[selected_cols].copy()

def process_data_for_line_plot(df, selected_countries, year_range):
    mask = df['Country'].isin(selected_countries)
    filtered_df = df[mask]
    
    df_melted = filtered_df.melt(
        id_vars=['Country'],
        value_vars=range(year_range[0], year_range[1] + 1),
        var_name='Year',
        value_name='Emissions'
    )
    df_melted['Year'] = pd.to_numeric(df_melted['Year'])
    return df_melted

# Visualization Functions
def create_line_plot(data):
    fig = px.line(
        data,
        x='Year',
        y='Emissions',
        color='Country',
        title='CO2 Emissions Over Time',
        labels={'Emissions': 'CO2 Emissions per Capita (Mton)'},
        hover_data={'Year': True, 'Emissions': ':.2f'}
    )
    fig.update_layout(height=600, hovermode='x unified')
    return fig

def create_animated_choropleth(data, start_year, end_year):
    df_map = data.melt(
        id_vars=['Country'],
        value_vars=range(start_year, end_year + 1),
        var_name='Year',
        value_name='Emissions'
    )
    
    fig_map = px.choropleth(
        df_map,
        locations='Country',
        locationmode='country names',
        color='Emissions',
        animation_frame='Year',
        title='CO2 Emissions per Capita Over Time',
        color_continuous_scale='Reds',
        range_color=[0, df_map['Emissions'].quantile(0.95)],
        labels={'Emissions': 'CO2 Emissions per Capita (Mton)'}
    )
    
    fig_map.update_layout(
        height=600,
        margin=dict(l=0, r=0, t=30, b=0),
        updatemenus=[{
            'type': 'buttons',
            'showactive': False,
            'buttons': [
                dict(label='Play',
                     method='animate',
                     args=[None, {'frame': {'duration': 500, 'redraw': True},
                                'fromcurrent': True}]),
                dict(label='Pause',
                     method='animate',
                     args=[[None], {'frame': {'duration': 0, 'redraw': False},
                                  'mode': 'immediate',
                                  'transition': {'duration': 0}}])
            ]
        }]
    )
    return fig_map


def create_race_plot(df, year_range):
    # Prepare data for race plot
    # Convert year columns to rows for raceplotly format
    df_race = df.melt(
        id_vars=['Country'],
        value_vars=range(year_range[0], year_range[1] + 1),
        var_name='Year',
        value_name='Emissions'
    )
    
    # Create the race plot
    race_plot = barplot(
        df_race,
        item_column='Country',
        value_column='Emissions',
        time_column='Year',
        top_entries=10,
    )
    
    # Plot with custom settings
    fig = race_plot.plot(
        title='Top 10 Countries by CO2 Emissions',
        orientation='horizontal',
        item_label='Country',
        value_label='CO2 Emissions (Mton)',
        time_label='Year: ',
        frame_duration=800
    )

    # fig.update_layout(
    # height=700,  # Make plot taller
    # font=dict(size=12),  # Increase base font size
    # title_font_size=20,  # Larger title
    # xaxis_title_font_size=16,  # Larger axis titles
    # yaxis_title_font_size=16,
    # yaxis_tickfont_size=14,  # Larger tick labels
    # xaxis_tickfont_size=14
    # )
    
    return fig


# Main App Function
def main():
    st.set_page_config(page_title="CO2 Emissions Dashboard", layout="wide")
    st.title("Global CO2 Emissions Dashboard")
    
    # Load Data
    df1 = load_data("fossil_CO2_per_capita_by_countr")
    df = load_data("fossil_CO2_totals_by_country")
    df = df[df['Country'] != 'International Shipping']
    df2 = df.copy()[:210]
    
    # Sidebar Controls
    #st.sidebar.header("Controls")

    #st.sidebar.markdown("---")
    st.sidebar.image("dati\SIAM-logo.jpg", width=150)

    visualization_type = st.sidebar.radio(
        "Choose Visualization",
        ["Time Series Plot", "Animated World Map", "Bar Chart Race"]
    )
    
    # Year range selector (common to both visualizations)
    year_range = st.sidebar.slider(
        "Select Year Range",
        min_value=YEAR_RANGE[0],
        max_value=YEAR_RANGE[1],
        value=YEAR_RANGE
    )
    
    # Conditional controls and display
    if visualization_type == "Time Series Plot":
        st.subheader("CO2 Emissions Time Series")
        
        # Show country selector only for time series
        countries = df1['Country'].unique().tolist()
        selected_countries = st.sidebar.multiselect(
            "Select countries to compare",
            options=countries,
            default=DEFAULT_COUNTRIES
        )
        
        # Process and display time series plot
        df_processed = process_data_for_line_plot(df1, selected_countries, year_range)
        fig = create_line_plot(df_processed)
        st.plotly_chart(fig, use_container_width=True)
        
    elif visualization_type == "Animated World Map":
        st.subheader("Global Emissions Map (Animated)")
        fig_map = create_animated_choropleth(df1, year_range[0], year_range[1])
        st.plotly_chart(fig_map, use_container_width=True)
    else:
        st.subheader("Top 10 CO2 Emitters Race")
        fig_race = create_race_plot(df2, year_range)
        st.plotly_chart(fig_race, use_container_width=True)
    
    st.sidebar.markdown("---")
    st.sidebar.markdown(""" GRUPPO 5 (EMANUELA, FULVIO, MARCO, TINSAE) """)

if __name__ == "__main__":
    main()