Spaces:
Running
Running
File size: 6,419 Bytes
197f5ec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
import streamlit as st
import pandas as pd
from ecologits.impacts.llm import compute_llm_impacts
from src.utils import format_impacts, average_range_impacts, format_impacts_expert, model_active_params_fn, model_total_params_fn
from src.impacts import display_impacts
#from src.constants import PROVIDERS, MODELS
from src.electricity_mix import COUNTRY_CODES, find_electricity_mix, dataframe_electricity_mix
from ecologits.model_repository import models
import plotly.express as px
def reset_model():
model = 'CUSTOM'
def expert_mode():
st.markdown("### 🤓 Expert mode")
with st.container(border = True):
########## Model info ##########
# col1, col2, col3 = st.columns(3)
# with col1:
# provider = st.selectbox(label = 'Provider expert',
# options = [x[0] for x in PROVIDERS],
# index = 0)
# provider = [x[1] for x in PROVIDERS if x[0] == provider][0]
# if 'huggingface_hub' in provider:
# provider = 'huggingface_hub'
# with col2:
# model = st.selectbox('Model expert', [x[0] for x in MODELS if provider in x[1]])
# model = [x[1] for x in MODELS if x[0] == model][0].split('/', 1)[1]
########## Model parameters ##########
col11, col22, col33 = st.columns(3)
with col11:
# st.write(provider, model)
# st.write(models.find_model(provider, model))
# st.write(model_active_params_fn(provider, model, 45))
active_params = st.number_input('Active parameters (B)', 0, None, 45)
with col22:
total_params = st.number_input('Total parameters (B)', 0, None, 45)
with col33:
output_tokens = st.number_input('Output completion tokens', 100)
########## Electricity mix ##########
location = st.selectbox('Location', [x[0] for x in COUNTRY_CODES])
col4, col5, col6 = st.columns(3)
with col4:
mix_gwp = st.number_input('Electricity mix - GHG emissions [kgCO2eq / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[2], format="%0.6f")
#disp_ranges = st.toggle('Display impact ranges', False)
with col5:
mix_adpe = st.number_input('Electricity mix - Abiotic resources [kgSbeq / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[0], format="%0.13f")
with col6:
mix_pe = st.number_input('Electricity mix - Primary energy [MJ / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[1], format="%0.3f")
impacts = compute_llm_impacts(model_active_parameter_count=active_params,
model_total_parameter_count=total_params,
output_token_count=output_tokens,
request_latency=100000,
if_electricity_mix_gwp=mix_gwp,
if_electricity_mix_adpe=mix_adpe,
if_electricity_mix_pe=mix_pe
)
impacts, usage, embodied = format_impacts(impacts)
with st.container(border = True):
st.markdown('<h3 align="center">Environmental Impacts</h2>', unsafe_allow_html = True)
display_impacts(impacts)
with st.expander('⚖️ Usage vs Embodied'):
st.markdown('<h3 align="center">Embodied vs Usage comparison</h2>', unsafe_allow_html = True)
st.markdown('The usage impacts account for the electricity consumption of the model while the embodied impacts account for resource extraction (e.g., minerals and metals), manufacturing, and transportation of the hardware.')
col_ghg_comparison, col_adpe_comparison, col_pe_comparison = st.columns(3)
with col_ghg_comparison:
fig_gwp = px.pie(
values = [average_range_impacts(usage.gwp.value), average_range_impacts(embodied.gwp.value)],
names = ['usage', 'embodied'],
title = 'GHG emissions',
color_discrete_sequence=["#636EFA", "#00CC96"],
width = 100
)
fig_gwp.update_layout(showlegend=False, title_x=0.5)
st.plotly_chart(fig_gwp)
with col_adpe_comparison:
fig_adpe = px.pie(
values = [average_range_impacts(usage.adpe.value), average_range_impacts(embodied.adpe.value)],
names = ['usage', 'embodied'],
title = 'Abiotic depletion',
color_discrete_sequence=["#00CC96","#636EFA"],
width = 100)
fig_adpe.update_layout(
showlegend=True,
legend=dict(yanchor="bottom", x = 0.35, y = -0.1),
title_x=0.5)
st.plotly_chart(fig_adpe)
with col_pe_comparison:
fig_pe = px.pie(
values = [average_range_impacts(usage.pe.value), average_range_impacts(embodied.pe.value)],
names = ['usage', 'embodied'],
title = 'Primary energy',
color_discrete_sequence=["#636EFA", "#00CC96"],
width = 100)
fig_pe.update_layout(showlegend=False, title_x=0.5)
st.plotly_chart(fig_pe)
with st.expander('🌍️ Location impact'):
st.markdown('<h4 align="center">How can location impact the footprint ?</h4>', unsafe_allow_html = True)
countries_to_compare = st.multiselect(
label = 'Countries to compare',
options = [x[0] for x in COUNTRY_CODES],
default = ["🇫🇷 France", "🇺🇸 United States", "🇨🇳 China"]
)
try:
df = dataframe_electricity_mix(countries_to_compare)
impact_type = st.selectbox(
label='Select an impact type to compare',
options=[x for x in df.columns if x!='country'],
index=1)
df.sort_values(by = impact_type, inplace = True)
fig_2 = px.bar(df, x = df.index, y = impact_type, text = impact_type, color = impact_type)
st.plotly_chart(fig_2)
except:
st.warning("Can't display chart with no values.") |