import streamlit as st
import pandas as pd

dict_comparaison_1kgCO2 = {
    "eau en litre":[374/100,"https://impactco2.fr/icons/eauenbouteille.svg"],
    "smartphones": [1.16/100,"https://impactco2.fr/icons/smartphone.svg"],
    "voiture en km": [460/100,"https://impactco2.fr/icons/voiturethermique.svg"],
    "tgv en km":[34130/100,"https://impactco2.fr/icons/tgv.svg"],
}

def compare(value_init, ratio_equivalent):
    return ratio_equivalent * value_init if ratio_equivalent else None

def display_comparaison(container,value_init, ratio_equivalent,icon, unit):
    #value_init must be in kgCO2
    link_url = f"https://impactco2.fr/outils/comparateur?value={value_init}&comparisons=tgv,eauenbouteille,voiturethermique"
    container.markdown(f"<div style='text-align: center;'><a href='{link_url}' target='_blank'><img src='{icon}' alt='{unit}' width='50'></div>", unsafe_allow_html=True)
    container.markdown(f"<div style='text-align: center;'><b>{compare(value_init, ratio_equivalent):.2f}</b> {unit}</div>", unsafe_allow_html=True)

def custom_cumsum(ecologits_series, codecarbon_series):
    cumsum = 0
    result = []
    for eco, code in zip(ecologits_series, codecarbon_series):
        if code == 0:
            result.append(0)
        else:
            cumsum += eco
            result.append(cumsum)
    return pd.Series(result, index=ecologits_series.index)


def get_table_empreintes_detailed() -> pd.DataFrame:
    emissions_data = st.session_state["partial_emissions"]
    emissions_df = pd.DataFrame(emissions_data).T  # Transpose to match the desired format

    # Rename columns and index values
    emissions_df = emissions_df.rename(columns={"cc": "CodeCarbon (Cumulative)", "el": "EcoLogits (par requete)"})
    emissions_df = emissions_df.applymap(lambda x: x * 1000)
    emissions_df = emissions_df.round(2)
    emissions_df = emissions_df.rename(index={
        "Scrapping": "Collecte de documents et scrapping",
        "extraction_pp": "Extraction des parties prenantes (serveur et IA)",
        "cartographie": "Sauvegarde de la cartographie",
        "chatbot": "Dialogue avec chatBot IA",
        "download_rapport": "Téléchargement cartographie"
    })

    # Make Ecologits column cumulative
    emissions_df["EcoLogits (Cumulative)"] = custom_cumsum(emissions_df["EcoLogits (par requete)"], emissions_df["CodeCarbon (Cumulative)"])

    emissions_df['Consommation Totale'] = emissions_df["CodeCarbon (Cumulative)"] + emissions_df["EcoLogits (Cumulative)"]
    emissions_df = emissions_df.round(2)
    return emissions_df