import streamlit as st
import pandas as pd
import altair as alt
import base64
import pdfkit
import io
from comparateur import *

def load_svg_as_base64(svg_file_path):
    with open(svg_file_path, "rb") as svg_file:
        return base64.b64encode(svg_file.read()).decode()

def save_pdf(html_content):
    pdf = pdfkit.from_string(html_content, False)
    return pdf

def display_comparaison_html(value_init, ratio_equivalent, icon, unit):
    link_url = f"https://impactco2.fr/outils/comparateur?value={value_init}&comparisons=tgv,eauenbouteille,voiturethermique"
    html = f"""
    <div style='text-align: center;'>
        <a href='{link_url}' target='_blank'><img src='{icon}' alt='{unit}' width='50'></a>
    </div>
    <div style='text-align: center;'>
        <b>{compare(value_init, ratio_equivalent):.2f}</b> {unit}
    </div>
    """
    return html

def display_cf_comparison():
    svg_file_path = "feuille.svg"
    svg_base64 = load_svg_as_base64(svg_file_path)
    
    html_content = f"""
    <div style='display: flex; align-items: center;'>
        <h2 style='margin: 0;'>Votre consommation Carbone</h2>
        <img src='data:image/svg+xml;base64,{svg_base64}' alt='svg' width='15' height='15' style='margin-left: 10px;'>
    </div>
    <br>
    """
    
    serveur_emission = st.session_state['emission'].stop()
    emission_api = sum([value["el"] for value in st.session_state["partial_emissions"].values()])

    total_emission = serveur_emission + emission_api

    pourcentage_api = emission_api / total_emission
    pourcentage_serveur = serveur_emission / total_emission

    html_content += f"<div style='text-align: center; margin-bottom: 10px;'><b>{total_emission*1000:.3f}</b> g eq. CO2</div>"
    html_content += f"<p>Dont :</p>"
    html_content += f"<p>- Empreinte serveur (via CodeCarbon) : <b>{serveur_emission*1000:.3f}</b> g eq. CO2 ({pourcentage_serveur:.2%})</p>"
    html_content += f"<p>- Empreinte IA (via EcoLogits) : <b>{emission_api*1000:.3f}</b> g eq. CO2 ({pourcentage_api:.2%})</p>"

    html_content += "<h2>Votre équivalence</h2>"
    html_content += """
    <div style='display: flex; justify-content: space-around;'>
    """

    html_content += f"""
    <div>
        {display_comparaison_html(total_emission, dict_comparaison_1kgCO2["eau en litre"][0]*1000, dict_comparaison_1kgCO2["eau en litre"][1], "ml")}
    </div>
    <div>
        {display_comparaison_html(total_emission, dict_comparaison_1kgCO2["tgv en km"][0], dict_comparaison_1kgCO2["tgv en km"][1], "km")}
    </div>
    <div>
        {display_comparaison_html(total_emission, dict_comparaison_1kgCO2["voiture en km"][0]*1000, dict_comparaison_1kgCO2["voiture en km"][1], "m")}
    </div>
    """

    html_content += "</div><br>"


    html_content += f"""
    <br>
    <div style='display: flex; align-items: center;'>
        <p>Powered by <b>ADEME</b></p>
        <a href='https://www.ademe.fr' target='_blank'><img src='https://www.ademe.fr/wp-content/uploads/2022/11/ademe-logo-2022-1.svg' alt='svg' width='30' height='30' style='margin-left: 10px;'></a>
    </div>
    <br>
    """
    
    #st.markdown(html_content, unsafe_allow_html=True)
    return html_content

def color_scale(val):
    if val == '-':
        return 'background-color: white'
    elif val <= 1:
        return 'background-color: rgba(0,100,0,0.5)'  # dark green with opacity
    elif val <= 10:
        return 'background-color: rgba(0,128,0,0.5)'  # green with opacity
    elif val <= 50:
        return 'background-color: rgba(255,255,0,0.5)'  # yellow with opacity
    elif val <= 100:
        return 'background-color: rgba(255,165,0,0.5)'  # orange with opacity
    else:
        return 'background-color: rgba(255,0,0,0.5)'  # red with opacity


def get_carbon_footprint_html():

    html_content = ""
    html_content += display_cf_comparison()
    
    table = get_table_empreintes_detailed()
    table.replace({0.00: '-'}, inplace=True)
    styled_df = table[['Consommation Totale']].rename(columns={'Consommation Totale': 'Consommation Cumulée (g eqCo2)'})
    styled_df = styled_df.style.applymap(color_scale, subset=['Consommation Cumulée (g eqCo2)'])
    

    html_content += """
    <h2>DÉTAIL PAR TÂCHE</h2>
    """
    html_content += styled_df.to_html()

    serveur_emission = st.session_state['emission'].stop()
    emission_api = sum([value["el"] for value in st.session_state["partial_emissions"].values()])

    total_emission = serveur_emission + emission_api

    pourcentage_api = emission_api / total_emission
    pourcentage_serveur = serveur_emission / total_emission

    df = pd.DataFrame({"Catégorie": ["Identification + dessin", "IA (extraction pp + dialogue)"], "valeur": [pourcentage_serveur, pourcentage_api]})
    base = alt.Chart(df).encode(
        theta=alt.Theta(field="valeur", type="quantitative", stack=True),
        color=alt.Color(field="Catégorie", type="nominal")
    )

    pie = base.mark_arc(outerRadius=100)
    text = base.mark_text(radius=115, fill="black").encode(alt.Text(field="valeur", type="quantitative", format=".2%"))

    chart = alt.layer(pie, text, data=df).resolve_scale(theta="independent")

    
    html_content += """
    <h2>SYNTHESE (Dialogue IA et non IA)</h2>
    """
    chart.save("chart.png")
    with open("chart.png", "rb") as image_file:
        encoded_image = base64.b64encode(image_file.read()).decode()

    html_content += f'<img src="data:image/png;base64,{encoded_image}" alt="Pie chart">'
    
    return html_content