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import streamlit as st
from comparateur import get_table_empreintes_detailed
from comparateur import *
import base64
import pandas as pd
import altair as alt
# Function to read and encode an SVG file to Base64
def load_svg_as_base64(file_path):
with open(file_path, "rb") as f:
svg_data = f.read()
return base64.b64encode(svg_data).decode()
def color_scale(val):
if val <= 1:
color = 'rgba(0,238,0,0.5)' # green with opacity
elif val <= 10:
color = 'rgba(110,238,0,0.5)' # light green with opacity
elif val <= 50:
color = 'rgba(255,255,0,0.5)' # light yellow with opacity
elif val <= 100:
color = 'rgba(255,165,0,0.5)' # light orange with opacity
else:
color = 'rgba(255,99,71,0.5)' # light red with opacity
return f'background-color: {color}'
def display_cf_comparison(stm: st):
svg_file_path = "feuille.svg"
svg_base64 = load_svg_as_base64(svg_file_path)
stm.markdown(
f"""
**Votre consommation Carbone**
<img src='data:image/svg+xml;base64,{svg_base64}' alt='svg' width='15' height='15' style='margin-left: 10px;'>
""",
unsafe_allow_html=True
)
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
stm.markdown(f"<div style='text-align: center; margin-bottom: 10px;'><b>{total_emission*1000:.3f}</b> g eq. CO2</div>", unsafe_allow_html=True)
stm.markdown("Dont :")
stm.markdown(f"- Empreinte serveur (via CodeCarbon) : **{serveur_emission*1000:.3f}** g eq. CO2 ({pourcentage_serveur:.2%})")
stm.write(f"- Empreinte IA (via EcoLogits) : **{emission_api*1000:.3f}** g eq. CO2 ({pourcentage_api:.2%})")
# stm.markdown("(avec l'outil CodeCarbon)")
c1,c2,c3 = stm.columns([1,1,1])
c2.write("---")
stm.markdown("**Votre équivalence**")
col1,col2,col3 = stm.columns([1,1,1])
display_comparaison(col1,total_emission,dict_comparaison_1kgCO2["eau en litre"][0]*1000,dict_comparaison_1kgCO2["eau en litre"][1],"ml")
display_comparaison(col2,total_emission,dict_comparaison_1kgCO2["tgv en km"][0],dict_comparaison_1kgCO2["tgv en km"][1],"km")
display_comparaison(col3,total_emission,dict_comparaison_1kgCO2["voiture en km"][0]*1000,dict_comparaison_1kgCO2["voiture en km"][1],"m")
stm.markdown("\n")
stm.markdown(
f"""
Powered by **ADEME**
<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;'>
""",
unsafe_allow_html=True
)
def display_carbon_footprint():
st.title("EMPREINTE ENERGETIQUE DE L'APPLICATION IA CARTO RSE")
display_cf_comparison(st)
table = get_table_empreintes_detailed()
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)'])
st.markdown("### DETAIL PAR TACHE")
st.table(styled_df)
with st.expander("Voir le détail de l'empreinte carbone"):
st.table(table)
st.markdown("### SYNTHESE (Dialogue IA et non IA)")
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({"Categorie": ["Identification + dessin","Dialogue avec IA"], "valeur": [pourcentage_serveur, pourcentage_api]})
base=alt.Chart(df).encode(
theta=alt.Theta(field="valeur", type="quantitative", stack=True),
color=alt.Color(field="Categorie", type="nominal")
)
pie = base.mark_arc(outerRadius=100)
text = base.mark_text(radius=115,fill= "black").encode(alt.Text(field="valeur", type="quantitative", format=",.1f"))
chart = alt.layer(pie, text, data=df).resolve_scale(theta="independent")
st.altair_chart(chart, use_container_width=True)
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