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import gradio as gr
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import requests
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from bs4 import BeautifulSoup
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import tiktoken
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import matplotlib
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import matplotlib.pyplot as plt
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from ecologits.tracers.utils import compute_llm_impacts, _avg
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from ecologits.impacts.llm import compute_llm_impacts as compute_llm_impacts_expert
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from ecologits.impacts.llm import IF_ELECTRICITY_MIX_GWP, IF_ELECTRICITY_MIX_ADPE, IF_ELECTRICITY_MIX_PE
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from ecologits.model_repository import models
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from src.assets import custom_css
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from src.electricity_mix import COUNTRY_CODES, find_electricity_mix
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from src.content import (
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HERO_TEXT,
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ABOUT_TEXT,
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CITATION_LABEL,
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CITATION_TEXT,
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LICENCE_TEXT, METHODOLOGY_TEXT
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)
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from src.constants import (
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PROVIDERS,
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OPENAI_MODELS,
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ANTHROPIC_MODELS,
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COHERE_MODELS,
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META_MODELS,
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MISTRALAI_MODELS,
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PROMPTS,
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CLOSED_SOURCE_MODELS,
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MODELS,
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)
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from src.utils import (
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format_impacts,
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format_energy_eq_physical_activity,
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PhysicalActivity,
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format_energy_eq_electric_vehicle,
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format_gwp_eq_streaming, format_energy_eq_electricity_production, EnergyProduction,
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format_gwp_eq_airplane_paris_nyc, format_energy_eq_electricity_consumption_ireland
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)
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CUSTOM = "Custom"
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tokenizer = tiktoken.get_encoding('cl100k_base')
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def model_list(provider: str) -> gr.Dropdown:
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if provider == "openai":
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return gr.Dropdown(
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OPENAI_MODELS,
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label="Model",
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value=OPENAI_MODELS[0][1],
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filterable=True,
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)
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elif provider == "anthropic":
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return gr.Dropdown(
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ANTHROPIC_MODELS,
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label="Model",
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value=ANTHROPIC_MODELS[0][1],
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filterable=True,
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)
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elif provider == "cohere":
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return gr.Dropdown(
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COHERE_MODELS,
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label="Model",
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value=COHERE_MODELS[0][1],
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filterable=True,
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)
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elif provider == "huggingface_hub/meta":
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return gr.Dropdown(
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META_MODELS,
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label="Model",
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value=META_MODELS[0][1],
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filterable=True,
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)
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elif provider == "mistralai":
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return gr.Dropdown(
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MISTRALAI_MODELS,
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label="Model",
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value=MISTRALAI_MODELS[0][1],
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filterable=True,
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)
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def custom():
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return CUSTOM
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def tiktoken_len(text):
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tokens = tokenizer.encode(
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text,
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disallowed_special=()
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)
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return len(tokens)
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def model_active_params_fn(model_name: str, n_param: float):
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if model_name == CUSTOM:
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return n_param
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provider, model_name = model_name.split('/', 1)
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model = models.find_model(provider=provider, model_name=model_name)
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return model.active_parameters or _avg(model.active_parameters_range)
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def model_total_params_fn(model_name: str, n_param: float):
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if model_name == CUSTOM:
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return n_param
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provider, model_name = model_name.split('/', 1)
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model = models.find_model(provider=provider, model_name=model_name)
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return model.total_parameters or _avg(model.total_parameters_range)
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def mix_fn(country_code: str, mix_adpe: float, mix_pe: float, mix_gwp: float):
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if country_code == CUSTOM:
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return mix_adpe, mix_pe, mix_gwp
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return find_electricity_mix(country_code)
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with gr.Blocks(css=custom_css) as demo:
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gr.Markdown(HERO_TEXT)
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with gr.Tab("🧮 Calculator"):
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with gr.Row():
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gr.Markdown("# Estimate the environmental impacts of LLM inference")
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with gr.Row():
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input_provider = gr.Dropdown(
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PROVIDERS,
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label="Provider",
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value=PROVIDERS[0][1],
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filterable=True,
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)
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input_model = gr.Dropdown(
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OPENAI_MODELS,
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label="Model",
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value=OPENAI_MODELS[0][1],
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filterable=True,
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)
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input_provider.change(model_list, input_provider, input_model)
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input_prompt = gr.Dropdown(
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PROMPTS,
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label="Example prompt",
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value=400,
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)
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@gr.render(inputs=[input_provider, input_model, input_prompt])
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def render_simple(provider, model, prompt):
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if provider.startswith("huggingface_hub"):
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provider = provider.split("/")[0]
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if models.find_model(provider, model) is not None:
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impacts = compute_llm_impacts(
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provider=provider,
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model_name=model,
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output_token_count=prompt,
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request_latency=100000
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)
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impacts = format_impacts(impacts)
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with gr.Blocks():
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if f"{provider}/{model}" in CLOSED_SOURCE_MODELS:
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with gr.Row():
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gr.Markdown("""<p> ⚠️ You have selected a closed-source model. Please be aware that
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some providers do not fully disclose information about such models. Consequently, our
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estimates have a lower precision for closed-source models. For further details, refer to
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our FAQ in the About section.
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</p>""", elem_classes="warning-box")
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with gr.Row():
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gr.Markdown("""
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## Environmental impacts
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To understand how the environmental impacts are computed go to the 📖 Methodology tab.
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""")
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with gr.Row():
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with gr.Column(scale=1, min_width=220):
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gr.Markdown(f"""
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<h2 align="center">⚡️ Energy</h2>
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$$ \Large {impacts.energy.magnitude:.3g} \ \large {impacts.energy.units} $$
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<p align="center"><i>Evaluates the electricity consumption<i></p><br>
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""")
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with gr.Column(scale=1, min_width=220):
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gr.Markdown(f"""
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<h2 align="center">🌍️ GHG Emissions</h2>
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$$ \Large {impacts.gwp.magnitude:.3g} \ \large {impacts.gwp.units} $$
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<p align="center"><i>Evaluates the effect on global warming<i></p><br>
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""")
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with gr.Column(scale=1, min_width=220):
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gr.Markdown(f"""
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<h2 align="center">🪨 Abiotic Resources</h2>
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$$ \Large {impacts.adpe.magnitude:.3g} \ \large {impacts.adpe.units} $$
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<p align="center"><i>Evaluates the use of metals and minerals<i></p><br>
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""")
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with gr.Column(scale=1, min_width=220):
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gr.Markdown(f"""
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<h2 align="center">⛽️ Primary Energy</h2>
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$$ \Large {impacts.pe.magnitude:.3g} \ \large {impacts.pe.units} $$
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<p align="center"><i>Evaluates the use of energy resources<i></p><br>
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""")
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with gr.Blocks():
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with gr.Row():
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gr.Markdown("""
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---
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## That's equivalent to...
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Making this request to the LLM is equivalent to the following actions.
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""")
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with gr.Row():
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physical_activity, distance = format_energy_eq_physical_activity(impacts.energy)
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if physical_activity == PhysicalActivity.WALKING:
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physical_activity = "🚶 " + physical_activity.capitalize()
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if physical_activity == PhysicalActivity.RUNNING:
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physical_activity = "🏃 " + physical_activity.capitalize()
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with gr.Column(scale=1, min_width=300):
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gr.Markdown(f"""
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<h2 align="center">{physical_activity} $$ \Large {distance.magnitude:.3g}\ {distance.units} $$ </h2>
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<p align="center"><i>Based on energy consumption<i></p><br>
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""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
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ev_eq = format_energy_eq_electric_vehicle(impacts.energy)
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with gr.Column(scale=1, min_width=300):
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gr.Markdown(f"""
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<h2 align="center">🔋 Electric Vehicle $$ \Large {ev_eq.magnitude:.3g}\ {ev_eq.units} $$ </h2>
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<p align="center"><i>Based on energy consumption<i></p><br>
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""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
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streaming_eq = format_gwp_eq_streaming(impacts.gwp)
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with gr.Column(scale=1, min_width=300):
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gr.Markdown(f"""
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<h2 align="center">⏯️ Streaming $$ \Large {streaming_eq.magnitude:.3g}\ {streaming_eq.units} $$ </h2>
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<p align="center"><i>Based on GHG emissions<i></p><br>
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""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
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with gr.Blocks():
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with gr.Row():
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gr.Markdown("""
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## What if 1% of the planet does this request everyday for 1 year?
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If this use case is largely deployed around the world the equivalent impacts would be. (The
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impacts of this request x 1% of 8 billion people x 365 days in a year.)
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""")
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with gr.Row():
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electricity_production, count = format_energy_eq_electricity_production(impacts.energy)
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if electricity_production == EnergyProduction.NUCLEAR:
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emoji = "☢️"
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name = "Nuclear power plants"
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if electricity_production == EnergyProduction.WIND:
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emoji = "💨️ "
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name = "Wind turbines"
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with gr.Column(scale=1, min_width=300):
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gr.Markdown(f"""
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<h2 align="center">{emoji} $$ \Large {count.magnitude:.0f} $$ {name} <span style="font-size: 12px">(yearly)</span></h2>
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<p align="center"><i>Based on electricity consumption<i></p><br>
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""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
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ireland_count = format_energy_eq_electricity_consumption_ireland(impacts.energy)
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with gr.Column(scale=1, min_width=300):
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gr.Markdown(f"""
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<h2 align="center">🇮🇪 $$ \Large {ireland_count.magnitude:.2g} $$ x Ireland <span style="font-size: 12px">(yearly ⚡️ cons.)</span></h2>
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<p align="center"><i>Based on electricity consumption<i></p><br>
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""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
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paris_nyc_airplane = format_gwp_eq_airplane_paris_nyc(impacts.gwp)
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with gr.Column(scale=1, min_width=300):
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gr.Markdown(f"""
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<h2 align="center">✈️ $$ \Large {paris_nyc_airplane.magnitude:,.0f} $$ Paris ↔ NYC </h2>
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<p align="center"><i>Based on GHG emissions<i></p><br>
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""", latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
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with gr.Tab("🤓 Expert Mode"):
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with gr.Row():
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gr.Markdown("# 🤓 Expert mode")
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model = gr.Dropdown(
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MODELS + [CUSTOM],
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label="Model name",
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value="openai/gpt-3.5-turbo",
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filterable=True,
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interactive=True
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)
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input_model_active_params = gr.Number(
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label="Number of billions of active parameters",
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value=45.0,
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interactive=True
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)
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input_model_total_params = gr.Number(
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label="Number of billions of total parameters",
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value=45.0,
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interactive=True
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)
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model.change(fn=model_active_params_fn,
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inputs=[model, input_model_active_params],
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outputs=[input_model_active_params])
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model.change(fn=model_total_params_fn,
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inputs=[model, input_model_total_params],
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outputs=[input_model_total_params])
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input_model_active_params.input(fn=custom, outputs=[model])
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input_model_total_params.input(fn=custom, outputs=[model])
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input_tokens = gr.Number(
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label="Output tokens",
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value=100
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)
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mix = gr.Dropdown(
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COUNTRY_CODES + [CUSTOM],
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label="Location",
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value="WOR",
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filterable=True,
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interactive=True
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)
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input_mix_gwp = gr.Number(
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label="Electricity mix - GHG emissions [kgCO2eq / kWh]",
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value=IF_ELECTRICITY_MIX_GWP,
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interactive=True
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)
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input_mix_adpe = gr.Number(
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label="Electricity mix - Abiotic resources [kgSbeq / kWh]",
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value=IF_ELECTRICITY_MIX_ADPE,
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interactive=True
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)
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input_mix_pe = gr.Number(
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label="Electricity mix - Primary energy [MJ / kWh]",
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value=IF_ELECTRICITY_MIX_PE,
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interactive=True
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)
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mix.change(fn=mix_fn,
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inputs=[mix, input_mix_adpe, input_mix_pe, input_mix_gwp],
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outputs=[input_mix_adpe, input_mix_pe, input_mix_gwp])
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input_mix_gwp.input(fn=custom, outputs=mix)
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input_mix_adpe.input(fn=custom, outputs=mix)
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input_mix_pe.input(fn=custom, outputs=mix)
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@gr.render(inputs=[
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input_model_active_params,
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input_model_total_params,
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input_tokens,
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input_mix_gwp,
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input_mix_adpe,
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input_mix_pe
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])
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def render_expert(
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model_active_params,
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model_total_params,
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tokens,
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mix_gwp,
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mix_adpe,
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mix_pe
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):
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impacts = compute_llm_impacts_expert(
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model_active_parameter_count=model_active_params,
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model_total_parameter_count=model_total_params,
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output_token_count=tokens,
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request_latency=100000,
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if_electricity_mix_gwp=mix_gwp,
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if_electricity_mix_adpe=mix_adpe,
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if_electricity_mix_pe=mix_pe
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)
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impacts = format_impacts(impacts)
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with gr.Blocks():
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with gr.Row():
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gr.Markdown(f"""
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<h2 align = "center">Environmental impacts</h2>
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""")
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with gr.Row():
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with gr.Column(scale=1, min_width=220):
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gr.Markdown(f"""
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<h2 align="center">⚡️ Energy</h2>
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$$ \Large {impacts.energy.magnitude:.3g} \ \large {impacts.energy.units} $$
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<p align="center"><i>Evaluates the electricity consumption<i></p><br>
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""")
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with gr.Column(scale=1, min_width=220):
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gr.Markdown(f"""
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<h2 align="center">🌍️ GHG Emissions</h2>
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$$ \Large {impacts.gwp.magnitude:.3g} \ \large {impacts.gwp.units} $$
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<p align="center"><i>Evaluates the effect on global warming<i></p><br>
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""")
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with gr.Column(scale=1, min_width=220):
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gr.Markdown(f"""
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<h2 align="center">🪨 Abiotic Resources</h2>
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$$ \Large {impacts.adpe.magnitude:.3g} \ \large {impacts.adpe.units} $$
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<p align="center"><i>Evaluates the use of metals and minerals<i></p><br>
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""")
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with gr.Column(scale=1, min_width=220):
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gr.Markdown(f"""
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<h2 align="center">⛽️ Primary Energy</h2>
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$$ \Large {impacts.pe.magnitude:.3g} \ \large {impacts.pe.units} $$
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<p align="center"><i>Evaluates the use of energy resources<i></p><br>
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""")
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with gr.Blocks():
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with gr.Row():
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gr.Markdown(f"""
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<h2 align="center">How can location impact the footprint ?</h2>
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""")
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with gr.Row():
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def create_static_bar_plot():
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categories = ['Sweden', 'France', 'Canada', 'USA', 'China', 'Australia', 'India']
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values = [46, 81, 238, 679, 1057, 1123, 1583]
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def addlabels(x,y):
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for i in range(len(x)):
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plt.text(i, y[i], y[i], ha = 'center')
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fig, ax = plt.subplots(figsize=(15,5), facecolor='#1F2937')
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ax.bar(categories, values)
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ax.set_ylabel('GHG emissions (gCO2eq) for 1kWh')
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ax.set_title('GWP emissions for 1 kWh of electricity consumption')
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ax.set_facecolor("#0B0F19")
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addlabels(categories, values)
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font = {'family' : 'monospace',
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'weight' : 'normal',
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'size' : 14}
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|
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matplotlib.rc('font', **font)
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|
matplotlib.rcParams.update({'text.color':'white',
|
|
'axes.labelcolor':'white',
|
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'xtick.color':'white',
|
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'ytick.color':'white'})
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return fig
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|
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static_plot = gr.Plot(value=create_static_bar_plot())
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|
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with gr.Tab("🔍 Evaluate your own usage"):
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with gr.Row():
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gr.Markdown("""
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|
# 🔍 Evaluate your own usage
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|
⚠️ For now, only ChatGPT conversation import is available.
|
|
You can always try out other models - however results might be inaccurate due to fixed parameters, such as tokenization method.
|
|
""")
|
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|
|
def process_input(text):
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|
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r = requests.get(text, verify=False)
|
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|
|
soup = BeautifulSoup(r.text, "html.parser")
|
|
list_text = str(soup).split('parts":["')
|
|
s = ''
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|
for item in list_text[1:int(len(list_text)/2)]:
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if list_text.index(item)%2 == 1:
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s = s + item.split('"]')[0]
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|
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amout_token = tiktoken_len(s)
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return amout_token
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|
|
def compute_own_impacts(amount_token, model):
|
|
provider = model.split('/')[0].lower()
|
|
model = model.split('/')[1]
|
|
impacts = compute_llm_impacts(
|
|
provider=provider,
|
|
model_name=model,
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|
output_token_count=amount_token,
|
|
request_latency=100000
|
|
)
|
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|
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impacts = format_impacts(impacts)
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|
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energy = f"""
|
|
<h2 align="center">⚡️ Energy</h2>
|
|
$$ \Large {impacts.energy.magnitude:.3g} \ \large {impacts.energy.units} $$
|
|
<p align="center"><i>Evaluates the electricity consumption<i></p><br>
|
|
"""
|
|
|
|
gwp = f"""
|
|
<h2 align="center">🌍️ GHG Emissions</h2>
|
|
$$ \Large {impacts.gwp.magnitude:.3g} \ \large {impacts.gwp.units} $$
|
|
<p align="center"><i>Evaluates the effect on global warming<i></p><br>
|
|
"""
|
|
|
|
adp = f"""
|
|
<h2 align="center">🪨 Abiotic Resources</h2>
|
|
$$ \Large {impacts.adpe.magnitude:.3g} \ \large {impacts.adpe.units} $$
|
|
<p align="center"><i>Evaluates the use of metals and minerals<i></p><br>
|
|
"""
|
|
|
|
pe = f"""
|
|
<h2 align="center">⛽️ Primary Energy</h2>
|
|
$$ \Large {impacts.pe.magnitude:.3g} \ \large {impacts.pe.units} $$
|
|
<p align="center"><i>Evaluates the use of energy resources<i></p><br>
|
|
"""
|
|
|
|
return energy, gwp, adp, pe
|
|
|
|
def combined_function(text, model):
|
|
n_token = process_input(text)
|
|
energy, gwp, adp, pe = compute_own_impacts(n_token, model)
|
|
return n_token, energy, gwp, adp, pe
|
|
|
|
with gr.Blocks():
|
|
|
|
text_input = gr.Textbox(label="Paste the URL here (must be on https://chatgpt.com/share/xxxx format)")
|
|
model = gr.Dropdown(
|
|
MODELS,
|
|
label="Model name",
|
|
value="openai/gpt-4o",
|
|
filterable=True,
|
|
interactive=True
|
|
)
|
|
|
|
process_button = gr.Button("Estimate this usage footprint")
|
|
|
|
with gr.Accordion("ℹ️ Infos", open=False):
|
|
n_token = gr.Textbox(label="Total amount of tokens :")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=1, min_width=220):
|
|
energy = gr.Markdown()
|
|
with gr.Column(scale=1, min_width=220):
|
|
gwp = gr.Markdown()
|
|
with gr.Column(scale=1, min_width=220):
|
|
adp = gr.Markdown()
|
|
with gr.Column(scale=1, min_width=220):
|
|
pe = gr.Markdown()
|
|
|
|
process_button.click(
|
|
fn=combined_function,
|
|
inputs=[text_input, model],
|
|
outputs=[n_token, energy, gwp, adp, pe]
|
|
)
|
|
|
|
with gr.Tab("📖 Methodology"):
|
|
gr.Markdown(METHODOLOGY_TEXT,
|
|
elem_classes="descriptive-text",
|
|
latex_delimiters=[
|
|
{"left": "$$", "right": "$$", "display": True},
|
|
{"left": "$", "right": "$", "display": False}
|
|
])
|
|
|
|
with gr.Tab("ℹ️ About"):
|
|
gr.Markdown(ABOUT_TEXT, elem_classes="descriptive-text",)
|
|
|
|
with gr.Accordion("📚 Citation", open=False):
|
|
gr.Textbox(
|
|
value=CITATION_TEXT,
|
|
label=CITATION_LABEL,
|
|
interactive=False,
|
|
show_copy_button=True,
|
|
lines=len(CITATION_TEXT.split('\n')),
|
|
)
|
|
|
|
|
|
gr.Markdown(LICENCE_TEXT)
|
|
|
|
if __name__ == '__main__':
|
|
demo.launch()
|
|
|
