feat : massive update (streamlit migration, integration of ecologits 0.6.1, dataviz, ...)

.gitattributes

@@ -1,35 +1,35 @@
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,303 +1 @@
-### VisualStudioCode template
-.vscode/
-
-### JetBrains template
-.idea/
-
-### Linux template
-*~
-
-# temporary files which can be created if a process still has a handle open of a deleted file
-.fuse_hidden*
-
-# KDE directory preferences
-.directory
-
-# Linux trash folder which might appear on any partition or disk
-.Trash-*
-
-# .nfs files are created when an open file is removed but is still being accessed
-.nfs*
-
-### Windows template
-# Windows thumbnail cache files
-Thumbs.db
-Thumbs.db:encryptable
-ehthumbs.db
-ehthumbs_vista.db
-
-# Dump file
-*.stackdump
-
-# Folder config file
-[Dd]esktop.ini
-
-# Recycle Bin used on file shares
-$RECYCLE.BIN/
-
-# Windows Installer files
-*.cab
-*.msi
-*.msix
-*.msm
-*.msp
-
-# Windows shortcuts
-*.lnk
-
-### Archives template
-# It's better to unpack these files and commit the raw source because
-# git has its own built in compression methods.
-*.7z
-*.jar
-*.rar
-*.zip
-*.gz
-*.gzip
-*.tgz
-*.bzip
-*.bzip2
-*.bz2
-*.xz
-*.lzma
-*.xar
-
-# Packing-only formats
-*.iso
-*.tar
-
-# Package management formats
-*.dmg
-*.xpi
-*.gem
-*.egg
-*.deb
-*.rpm
-*.txz
-
-### JupyterNotebooks template
-# gitignore template for Jupyter Notebooks
-# website: http://jupyter.org/
-
-.ipynb_checkpoints
-*/.ipynb_checkpoints/*
-
-# IPython
-profile_default/
-ipython_config.py
-
-# Remove previous ipynb_checkpoints
-#   git rm -r .ipynb_checkpoints/
-
-### macOS template
-# General
-.DS_Store
-.AppleDouble
-.LSOverride
-
-# Icon must end with two \r
-Icon
-
-# Thumbnails
-._*
-
-# Files that might appear in the root of a volume
-.DocumentRevisions-V100
-.fseventsd
-.Spotlight-V100
-.TemporaryItems
-.Trashes
-.VolumeIcon.icns
-.com.apple.timemachine.donotpresent
-
-# Directories potentially created on remote AFP share
-.AppleDB
-.AppleDesktop
-Network Trash Folder
-Temporary Items
-.apdisk
-
-### Python template
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
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-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-
-# IPython
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
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-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
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-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-### SublimeText template
-# Cache files for Sublime Text
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-# *.sublime-project
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-sftp-config.json
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-Package Control.last-run
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-Package Control.ca-bundle
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-Package Control.user-ca-bundle
-oscrypto-ca-bundle.crt
-bh_unicode_properties.cache
-
-# Sublime-github package stores a github token in this file
-# https://packagecontrol.io/packages/sublime-github
-GitHub.sublime-settings
-
+.venv

.python-version

@@ -0,0 +1 @@
+3.12

LICENSE

@@ -1,427 +0,0 @@
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Makefile

@@ -1,6 +0,0 @@
-
-install:
-	pip install -U -r requirements-dev.txt
-
-start:
-	gradio app.py

README.md

@@ -3,37 +3,9 @@ title: EcoLogits Calculator
 emoji: 🧮
 colorFrom: green
 colorTo: indigo
-sdk: gradio
-sdk_version: 4.36.1
+sdk: streamlit
+sdk_version: 1.40.2
 app_file: app.py
 pinned: true
 license: cc-by-sa-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-
-To do:
-
-- [ ] Add a tab with basic documentation
-    - [X] Definition of each indicator (check the use of tool tip in gradio ?)
-    - [ ] Basic explanation of the methodology 
-        - Why it depends on the number of tokens? (What is a token?)
-        - What is taken into account? (usage, manufacturing for inference != training)
-        - What hypotheses have we made?
-    - [X] Explain how to reduce the impacts? What are good strategies? (Valentin)
-- [ ] Add more indicators like (km by car, kg of concrete, ...) we can use [impactco2.fr](https://impactco2.fr/). (Valentin)
-    - [X] First version
-    - [ ] To enrich
-- [ ] Call to actions: (Valentin) 
-    - [X] Follow us on LinkedIn
-    - [ ] Share the results of a simulation (e.g. export an image generated with plotly for instance?)
-- [X] Add an advanced/expert tab
-    - [X] True number of tokens
-    - [X] Expose more inputs like the electricity mix
-    - [X] Examples of electricity mixes
-    - [X] Custom number of parameters
-- [x] Live reload mode
-- [ ] Idea : "estimate a given prompt impact" function which allows to enter a prompt in a text field and estimate its impacts
-- [ ] Idea: copy-paste a ChatGPT conversation link and estimate the impacts.
-- [ ] Idea: compare with country electricity consumption
+---

__init__.py

@@ -0,0 +1,8 @@
+from .content import *
+from .constants import *
+from .expert import expert_mode
+from .utils import *
+from .calculator import calculator_mode
+from .impacts import get_impacts, display_impacts
+from .models import load_models
+from .electricity_mix import *

app.py

@@ -1,539 +1,46 @@
-import gradio as gr
-
-from ecologits.tracers.utils import llm_impacts, _avg
-from ecologits.impacts.llm import compute_llm_impacts as compute_llm_impacts_expert
-from ecologits.model_repository import models
-
-from src.assets import custom_css
-from src.electricity_mix import COUNTRY_CODES, find_electricity_mix
-from src.content import (
-    HERO_TEXT,
-    ABOUT_TEXT,
-    CITATION_LABEL,
-    CITATION_TEXT,
-    LICENCE_TEXT, METHODOLOGY_TEXT
-)
-from src.constants import (
-    PROVIDERS,
-    OPENAI_MODELS,
-    ANTHROPIC_MODELS,
-    COHERE_MODELS,
-    META_MODELS,
-    MISTRALAI_MODELS,
-    PROMPTS,
-    CLOSED_SOURCE_MODELS,
-    MODELS,
-)
-from src.utils import (
-    format_impacts,
-    format_impacts_expert,
-    format_energy_eq_physical_activity,
-    PhysicalActivity,
-    format_energy_eq_electric_vehicle,
-    format_gwp_eq_streaming,
-    format_energy_eq_electricity_production,
-    EnergyProduction,
-    format_gwp_eq_airplane_paris_nyc,
-    format_energy_eq_electricity_consumption_ireland,
-    df_elec_mix_for_plot
-)
-from src.scrapper import process_input
-
-CUSTOM = "Custom"
-
-def model_list(provider: str) -> gr.Dropdown:
-    if provider == "openai":
-        return gr.Dropdown(
-            OPENAI_MODELS,
-            label="Model",
-            value=OPENAI_MODELS[0][1],
-            filterable=True,
-        )
-    elif provider == "anthropic":
-        return gr.Dropdown(
-            ANTHROPIC_MODELS,
-            label="Model",
-            value=ANTHROPIC_MODELS[0][1],
-            filterable=True,
-        )
-    elif provider == "cohere":
-        return gr.Dropdown(
-            COHERE_MODELS,
-            label="Model",
-            value=COHERE_MODELS[0][1],
-            filterable=True,
-        )
-    elif provider == "huggingface_hub/meta":
-        return gr.Dropdown(
-            META_MODELS,
-            label="Model",
-            value=META_MODELS[0][1],
-            filterable=True,
-        )
-    elif provider == "mistralai":
-        return gr.Dropdown(
-            MISTRALAI_MODELS,
-            label="Model",
-            value=MISTRALAI_MODELS[0][1],
-            filterable=True,
-        )
-
-
-def custom():
-    return CUSTOM
-
-def model_active_params_fn(model_name: str, n_param: float):
-    if model_name == CUSTOM:
-        return n_param
-    provider, model_name = model_name.split('/', 1)
-    model = models.find_model(provider=provider, model_name=model_name)
-    try: #moe with range
-        return model.architecture.parameters.active.max
-    except:
-        try: #moe without range
-            return model.architecture.parameters.active
-        except:
-            try: #dense with range
-                return model.architecture.parameters.max
-            except: #dense without range
-                return model.architecture.parameters
-
-def model_total_params_fn(model_name: str, n_param: float):
-    if model_name == CUSTOM:
-        return n_param
-    provider, model_name = model_name.split('/', 1)
-    model = models.find_model(provider=provider, model_name=model_name)
-    try: #moe
-        return model.architecture.parameters.total.max
-    except:
-        try: #dense with range
-            return model.architecture.parameters.max
-        except: #dense without range
-            try:
-                return model.architecture.parameters.total
-            except:
-                return model.architecture.parameters
-
-def mix_fn(country_code: str, mix_adpe: float, mix_pe: float, mix_gwp: float):
-    if country_code == CUSTOM:
-        return mix_adpe, mix_pe, mix_gwp
-    return find_electricity_mix(country_code)
-
-with gr.Blocks(css=custom_css) as demo:
-    gr.Markdown(HERO_TEXT)
-
-    with gr.Tab("🧮 Calculator"):
-        with gr.Row():
-            gr.Markdown("# Estimate the environmental impacts of LLM inference")
-        with gr.Row():
-            input_provider = gr.Dropdown(
-                PROVIDERS,
-                label="Provider",
-                value=PROVIDERS[0][1],
-                filterable=True,
-            )
-
-            input_model = gr.Dropdown(
-                OPENAI_MODELS,
-                label="Model",
-                value=OPENAI_MODELS[0][1],
-                filterable=True,
-            )
-            input_provider.change(model_list, input_provider, input_model)
-
-            input_prompt = gr.Dropdown(
-                PROMPTS,
-                label="Example prompt",
-                value=400,
-            )
-
-
-        @gr.render(inputs=[input_provider, input_model, input_prompt])
-        def render_simple(provider, model, prompt):
-            if provider.startswith("huggingface_hub"):
-                provider = provider.split("/")[0]
-            if models.find_model(provider, model) is not None:
-                impacts = llm_impacts(
-                    provider=provider,
-                    model_name=model,
-                    output_token_count=prompt,
-                    request_latency=100000
-                )
-                impacts = format_impacts(impacts)
-
-                # Inference impacts
-                with gr.Blocks():
-                    if f"{provider}/{model}" in CLOSED_SOURCE_MODELS:
-                        with gr.Row():
-                            gr.Markdown("""<p> ⚠️ You have selected a closed-source model. Please be aware that 
-                            some providers do not fully disclose information about such models. Consequently, our 
-                            estimates have a lower precision for closed-source models. For further details, refer to 
-                            our FAQ in the About section.
-                            </p>""", elem_classes="warning-box")
-
-                    with gr.Row():
-                        gr.Markdown("""
-                        ## Environmental impacts
-                        
-                        To understand how the environmental impacts are computed go to the 📖 Methodology tab. 
-                        """)
-                    with gr.Row():
-                        with gr.Column(scale=1, min_width=220):
-                            gr.Markdown(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>
-                            """)
-                        with gr.Column(scale=1, min_width=220):
-                            gr.Markdown(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>
-                            """)
-                        with gr.Column(scale=1, min_width=220):
-                            gr.Markdown(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>
-                            """)
-                        with gr.Column(scale=1, min_width=220):
-                            gr.Markdown(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>
-                            """)
-
-                # Impacts equivalents
-                with gr.Blocks():
-                    with gr.Row():
-                        gr.Markdown("""
-                        ---
-                        ## That's equivalent to...
-                        
-                        Making this request to the LLM is equivalent to the following actions.
-                        """)
-                    with gr.Row():
-                        physical_activity, distance = format_energy_eq_physical_activity(impacts.energy)
-                        if physical_activity == PhysicalActivity.WALKING:
-                            physical_activity = "🚶 " + physical_activity.capitalize()
-                        if physical_activity == PhysicalActivity.RUNNING:
-                            physical_activity = "🏃 " + physical_activity.capitalize()
-                        with gr.Column(scale=1, min_width=300):
-                            gr.Markdown(f"""
-                            <h2 align="center">{physical_activity} $$ \Large {distance.magnitude:.3g}\ {distance.units} $$ </h2>
-                            <p align="center"><i>Based on energy consumption<i></p><br>
-                            """, latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
-
-                        ev_eq = format_energy_eq_electric_vehicle(impacts.energy)
-                        with gr.Column(scale=1, min_width=300):
-                            gr.Markdown(f"""
-                            <h2 align="center">🔋 Electric Vehicle $$ \Large {ev_eq.magnitude:.3g}\ {ev_eq.units} $$ </h2>
-                            <p align="center"><i>Based on energy consumption<i></p><br>
-                            """, latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
-
-                        streaming_eq = format_gwp_eq_streaming(impacts.gwp)
-                        with gr.Column(scale=1, min_width=300):
-                            gr.Markdown(f"""
-                            <h2 align="center">⏯️ Streaming $$ \Large {streaming_eq.magnitude:.3g}\ {streaming_eq.units} $$ </h2>
-                            <p align="center"><i>Based on GHG emissions<i></p><br>
-                            """, latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
-
-                # Bigger scale impacts equivalent
-                with gr.Blocks():
-                    with gr.Row():
-                        gr.Markdown("""
-                        ## What if 1% of the planet does this request everyday for 1 year?
-                        
-                        If this use case is largely deployed around the world the equivalent impacts would be. (The 
-                        impacts of this request x 1% of 8 billion people x 365 days in a year.)
-                        """)
-                    with gr.Row():
-                        electricity_production, count = format_energy_eq_electricity_production(impacts.energy)
-                        if electricity_production == EnergyProduction.NUCLEAR:
-                            emoji = "☢️"
-                            name = "Nuclear power plants"
-                        if electricity_production == EnergyProduction.WIND:
-                            emoji = "💨️ "
-                            name = "Wind turbines"
-                        with gr.Column(scale=1, min_width=300):
-                            gr.Markdown(f"""
-                            <h2 align="center">{emoji} $$ \Large {count.magnitude:.0f} $$ {name} <span style="font-size: 12px">(yearly)</span></h2>
-                            <p align="center"><i>Based on electricity consumption<i></p><br>
-                            """, latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
-
-                        ireland_count = format_energy_eq_electricity_consumption_ireland(impacts.energy)
-                        with gr.Column(scale=1, min_width=300):
-                            gr.Markdown(f"""
-                            <h2 align="center">🇮🇪 $$ \Large {ireland_count.magnitude:.2g} $$ x Ireland <span style="font-size: 12px">(yearly ⚡️ cons.)</span></h2>
-                            <p align="center"><i>Based on electricity consumption<i></p><br>
-                            """, latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
-
-                        paris_nyc_airplane = format_gwp_eq_airplane_paris_nyc(impacts.gwp)
-                        with gr.Column(scale=1, min_width=300):
-                            gr.Markdown(f"""
-                            <h2 align="center">✈️ $$ \Large {paris_nyc_airplane.magnitude:,.0f} $$ Paris ↔ NYC </h2>
-                            <p align="center"><i>Based on GHG emissions<i></p><br>
-                            """, latex_delimiters=[{"left": "$$", "right": "$$", "display": False}])
-
-    with gr.Tab("🤓 Expert Mode"):
-
-        with gr.Row():
-            gr.Markdown("# 🤓 Expert mode")
-
-        model = gr.Dropdown(
-            MODELS + [CUSTOM],
-            label="Model name",
-            value="openai/gpt-3.5-turbo",
-            filterable=True,
-            interactive=True
-        )
-        input_model_active_params = gr.Number(
-            label="Number of billions of active parameters",
-            value=45.0,
-            interactive=True
-        )
-        input_model_total_params = gr.Number(
-            label="Number of billions of total parameters",
-            value=45.0,
-            interactive=True
-        )
-
-        model.change(fn=model_active_params_fn,
-                     inputs=[model, input_model_active_params],
-                     outputs=[input_model_active_params])
-        model.change(fn=model_total_params_fn,
-                     inputs=[model, input_model_total_params],
-                     outputs=[input_model_total_params])
-        input_model_active_params.input(fn=custom, outputs=[model])
-        input_model_total_params.input(fn=custom, outputs=[model])
-
-        input_tokens = gr.Number(
-            label="Output tokens",
-            value=100
-        )
-
-        mix = gr.Dropdown(
-            COUNTRY_CODES + [CUSTOM],
-            label="Location",
-            value="WOR",
-            filterable=True,
-            interactive=True
-        )
-        input_mix_gwp = gr.Number(
-            label="Electricity mix - GHG emissions [kgCO2eq / kWh]",
-            value=find_electricity_mix('WOR')[2],
-            interactive=True
-        )
-        input_mix_adpe = gr.Number(
-            label="Electricity mix - Abiotic resources [kgSbeq / kWh]",
-            value=find_electricity_mix('WOR')[0],
-            interactive=True
-        )
-        input_mix_pe = gr.Number(
-            label="Electricity mix - Primary energy [MJ / kWh]",
-            value=find_electricity_mix('WOR')[1],
-            interactive=True
-        )
-
-        mix.change(fn=mix_fn,
-                   inputs=[mix, input_mix_adpe, input_mix_pe, input_mix_gwp],
-                   outputs=[input_mix_adpe, input_mix_pe, input_mix_gwp])
-        input_mix_gwp.input(fn=custom, outputs=mix)
-        input_mix_adpe.input(fn=custom, outputs=mix)
-        input_mix_pe.input(fn=custom, outputs=mix)
-
-
-        @gr.render(inputs=[
-            input_model_active_params,
-            input_model_total_params,
-            input_tokens,
-            input_mix_gwp,
-            input_mix_adpe,
-            input_mix_pe
-        ])
-        def render_expert(
-                model_active_params,
-                model_total_params,
-                tokens,
-                mix_gwp,
-                mix_adpe,
-                mix_pe
-        ):
-            impacts = compute_llm_impacts_expert(
-                model_active_parameter_count=model_active_params,
-                model_total_parameter_count=model_total_params,
-                output_token_count=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_expert(impacts)
-
-            with gr.Blocks():
-				
-                with gr.Row():
-                    gr.Markdown(f"""
-                                <h2 align = "center">Environmental impacts</h2>
-                                """)
-                
-                with gr.Row():
-                    with gr.Column(scale=1, min_width=220):
-                        gr.Markdown(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>
-                        """)
-                        
-                    with gr.Column(scale=1, min_width=220):
-                        gr.Markdown(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>
-                        $$ \Large {100*usage.gwp.value / (usage.gwp.value + embodied.gwp.value):.3} $$
-                        <p align="center"><i>% of GWP by usage (vs embodied)<i></p><br>
-                        """)
-                        
-                    with gr.Column(scale=1, min_width=220):
-                        gr.Markdown(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>
-                        $$ \Large {100*usage.adpe.value / (usage.adpe.value + embodied.adpe.value):.3} $$
-                        <p align="center"><i>% of ADPE by usage (vs embodied)<i></p><br>
-                        """)
-                        
-                    with gr.Column(scale=1, min_width=220):
-                        gr.Markdown(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>
-                        $$ \Large {100*usage.pe.value / (usage.pe.value + embodied.pe.value):.3} $$
-                        <p align="center"><i>% of PE by usage (vs embodied)<i></p><br>
-                        """)
-
-        with gr.Row():                
-            gr.Markdown(f"""
-                        <h2 align="center">How can location impact the footprint ?</h2>
-                        """)
-        
-        with gr.Row():     
-            gr.BarPlot(df_elec_mix_for_plot,
-                       x='country',
-                       y='electricity_mix',
-                       sort='y',
-                       scale=1,
-                       height=250,
-                       min_width=800,
-                       x_title=None,
-                       y_title='electricity mix in gCO2eq / kWh')
-
-    # with gr.Tab("🔍 Evaluate your own usage"):
-
-    #     with gr.Row():
-    #         gr.Markdown("""
-    #                     # 🔍 Evaluate your own usage
-    #                     ⚠️ 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.
-    #                     """)
-        
-    #     def compute_own_impacts(amount_token, model):
-    #         provider = model.split('/')[0].lower()
-    #         model = model.split('/')[1]
-    #         impacts = llm_impacts(
-    #                 provider=provider,
-    #                 model_name=model,
-    #                 output_token_count=amount_token,
-    #                 request_latency=100000
-    #             )
-            
-    #         impacts = format_impacts(impacts)
-
-    #         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')),
-        )
-
-    # License
-    gr.Markdown(LICENCE_TEXT)
-
-if __name__ == '__main__':
-    demo.launch()
+import streamlit as st
+
+from src.content import (
+    HERO_TEXT,
+    ABOUT_TEXT,
+    CITATION_LABEL,
+    CITATION_TEXT,
+    LICENCE_TEXT,
+    INTRO_TEXT,
+    METHODOLOGY_TEXT
+)
+
+from src.expert import expert_mode
+from src.calculator import calculator_mode
+
+st.set_page_config(layout="wide",
+                   page_title="ECOLOGITS",
+                   page_icon='💬')
+
+st.html(HERO_TEXT)
+
+st.markdown(INTRO_TEXT)
+
+tab_calculator, tab_expert, tab_method, tab_about = st.tabs(['🧮 Calculator', '🤓 Expert Mode', '📖 Methodology', 'ℹ️ About'])
+
+with tab_calculator:
+
+    calculator_mode()
+
+with tab_expert:
+
+    expert_mode()
+
+with tab_method:
+
+    st.write(METHODOLOGY_TEXT)
+
+with tab_about:
+
+    st.write(ABOUT_TEXT)
+
+with st.expander('📚 Citation'):
+    st.html(CITATION_LABEL)
+    st.html(CITATION_TEXT)
+
+st.html(LICENCE_TEXT)

calculator.py

@@ -0,0 +1,67 @@
+import streamlit as st
+import pandas as pd
+
+from ecologits.tracers.utils import llm_impacts
+from src.impacts import get_impacts, display_impacts, display_equivalent
+from src.utils import format_impacts
+from src.content import WARNING_CLOSED_SOURCE, WARNING_MULTI_MODAL, WARNING_BOTH
+from src.models import load_models, clean_models_data
+
+from src.constants import PROMPTS
+
+def calculator_mode():
+
+    with st.container(border=True):
+        
+        df = load_models()
+        
+        col1, col2, col3 = st.columns(3)
+
+        with col1:
+            provider = st.selectbox(label = 'Provider',
+                                    options = [x for x in df['provider_clean'].unique()],
+                                    index = 9)
+            provider_raw = df[df['provider_clean'] == provider]['provider'].values[0]
+
+        with col2:
+            model = st.selectbox('Model', [x for x in df['name_clean'].unique() if x in df[df['provider_clean'] == provider]['name_clean'].unique()])
+            model_raw = df[(df['provider_clean'] == provider) & (df['name_clean'] == model)]['name'].values[0]
+
+        with col3:
+            output_tokens = st.selectbox('Example prompt', [x[0] for x in PROMPTS])
+            
+        # WARNING DISPLAY
+        
+        df_filtered = df[(df['provider_clean'] == provider) & (df['name_clean'] == model)]
+
+        if df_filtered['warning_arch'].values[0] and not df_filtered['warning_multi_modal'].values[0]:
+            st.warning(WARNING_CLOSED_SOURCE)
+        if df_filtered['warning_multi_modal'].values[0] and not df_filtered['warning_arch'].values[0]:
+            st.warning(WARNING_MULTI_MODAL)
+        if df_filtered['warning_arch'].values[0] and df_filtered['warning_multi_modal'].values[0]:
+            st.warning(WARNING_BOTH)
+            
+    try:
+        impacts = llm_impacts(
+                        provider=provider_raw,
+                        model_name=model_raw,
+                        output_token_count=[x[1] for x in PROMPTS if x[0] == output_tokens][0],
+                        request_latency=100000
+                    )
+
+        impacts, _, _ = format_impacts(impacts)
+        
+        with st.container(border=True):
+
+            st.markdown('<h3 align = "center">Environmental impacts</h3>', unsafe_allow_html=True)
+            st.markdown('<p align = "center">To understand how the environmental impacts are computed go to the 📖 Methodology tab.</p>', unsafe_allow_html=True)
+            display_impacts(impacts)
+        
+        with st.container(border=True):
+            
+            st.markdown('<h3 align = "center">That\'s equivalent to ...</h3>', unsafe_allow_html=True)
+            st.markdown('<p align = "center">Making this request to the LLM is equivalent to the following actions :</p>', unsafe_allow_html=True)
+            display_equivalent(impacts)
+            
+    except Exception as e:
+        st.error('Could not find the model in the repository. Please try another model.')

constants.py

@@ -0,0 +1,106 @@
+PROVIDERS = [
+    ("OpenAI", "openai"),
+    ("Anthropic", "anthropic"),
+    ("Cohere", "cohere"),
+    ("Meta", "huggingface_hub/meta"),
+    ("Mistral AI", "mistralai"),
+]
+
+OPENAI_MODELS = [
+    ("GPT-4o", "gpt-4o"),
+    ("GPT-4-Turbo", "gpt-4-turbo"),
+    ("GPT-4", "gpt-4"),
+    ("GPT-3.5-Turbo", "gpt-3.5-turbo"),
+]
+
+ANTHROPIC_MODELS = [
+    ("Claude 3 Opus", "claude-3-opus-20240229"),
+    ("Claude 3 Sonnet", "claude-3-sonnet-20240229"),
+    ("Claude 3 Haiku", "claude-3-haiku-20240307"),
+    ("Claude 2.1", "claude-2.1"),
+    ("Claude 2.0", "claude-2.0"),
+    ("Claude Instant 1.2", "claude-instant-1.2"),
+]
+
+COHERE_MODELS = [
+    ("Command Light", "command-light"),
+    ("Command", "command"),
+    ("Command R", "command-r"),
+    ("Command R+", "command-r-plus"),
+]
+
+META_MODELS = [
+    ("Llama 3 8B", "meta-llama/Meta-Llama-3-8B"),
+    ("Llama 3 70B", "meta-llama/Meta-Llama-3-70B"),
+    ("Llama 2 7B", "meta-llama/Llama-2-7b-hf"),
+    ("Llama 2 13B", "meta-llama/Llama-2-13b-hf"),
+    ("Llama 2 70B", "meta-llama/Llama-2-70b-hf"),
+]
+
+MISTRALAI_MODELS = [
+    ("Mistral 7B", "open-mistral-7b"),
+    ("Mixtral 8x7B", "open-mixtral-8x7b"),
+    ("Mixtral 8x22B", "open-mixtral-8x22b"),
+    ("Tiny", "mistral-tiny-2312"),
+    ("Small", "mistral-small-2402"),
+    ("Medium", "mistral-medium-2312"),
+    ("Large", "mistral-large-2402"),
+]
+
+PROMPTS = [
+    ("Write a Tweet", 50),
+    ("Write an email", 170),
+    ("Write an article summary", 250),
+    ("Small conversation with a chatbot", 400),
+    ("Write a report of 5 pages", 5000),
+    ("Write the code for this app", 10000)
+]
+PROMPTS = [(s + f" ({v} output tokens)", v) for (s, v) in PROMPTS]
+
+CLOSED_SOURCE_MODELS = {
+    "openai/gpt-4o",
+    "openai/gpt-4-turbo",
+    "openai/gpt-4",
+    "openai/gpt-3.5-turbo",
+    "anthropic/claude-3-opus-20240229",
+    "anthropic/claude-3-sonnet-20240229",
+    "anthropic/claude-3-haiku-20240307",
+    "anthropic/claude-2.1",
+    "anthropic/claude-2.0",
+    "anthropic/claude-instant-1.2",
+    "mistralai/mistral-tiny-2312",
+    "mistralai/mistral-small-2402",
+    "mistralai/mistral-medium-2312",
+    "mistralai/mistral-large-2402",
+}
+
+MODELS = [
+    ("OpenAI / GPT-4o", "openai/gpt-4o"),
+    ("OpenAI / GPT-4-Turbo", "openai/gpt-4-turbo"),
+    ("OpenAI / GPT-4", "openai/gpt-4"),
+    ("OpenAI / GPT-3.5-Turbo", "openai/gpt-3.5-turbo"),
+    ("Anthropic / Claude 3 Opus", "anthropic/claude-3-opus-20240229"),
+    ("Anthropic / Claude 3 Sonnet", "anthropic/claude-3-sonnet-20240229"),
+    ("Anthropic / Claude 3 Haiku", "anthropic/claude-3-haiku-20240307"),
+    ("Anthropic / Claude 2.1", "anthropic/claude-2.1"),
+    ("Anthropic / Claude 2.0", "anthropic/claude-2.0"),
+    ("Anthropic / Claude Instant 1.2", "anthropic/claude-instant-1.2"),
+    ("Mistral AI / Mistral 7B", "mistralai/open-mistral-7b"),
+    ("Mistral AI / Mixtral 8x7B", "mistralai/open-mixtral-8x7b"),
+    ("Mistral AI / Mixtral 8x22B", "mistralai/open-mixtral-8x22b"),
+    ("Mistral AI / Tiny", "mistralai/mistral-tiny-2312"),
+    ("Mistral AI / Small", "mistralai/mistral-small-2402"),
+    ("Mistral AI / Medium", "mistralai/mistral-medium-2312"),
+    ("Mistral AI / Large", "mistralai/mistral-large-2402"),
+    ("Meta / Llama 3 8B", "huggingface_hub/meta-llama/Meta-Llama-3-8B"),
+    ("Meta / Llama 3 70B", "huggingface_hub/meta-llama/Meta-Llama-3-70B"),
+    ("Meta / Llama 2 7B", "huggingface_hub/meta-llama/Llama-2-7b-hf"),
+    ("Meta / Llama 2 13B", "huggingface_hub/meta-llama/Llama-2-13b-hf"),
+    ("Meta / Llama 2 70B", "huggingface_hub/meta-llama/Llama-2-70b-hf"),
+    ("Cohere / Command Light", "cohere/command-light"),
+    ("Cohere / Command", "cohere/command"),
+    ("Cohere / Command R", "cohere/command-r"),
+    ("Cohere / Command R+", "cohere/command-r-plus"),
+]
+
+MODEL_REPOSITORY_URL = "https://raw.githubusercontent.com/genai-impact/ecologits/refs/heads/main/ecologits/data/models.json"

content.py

@@ -0,0 +1,232 @@
+HERO_TEXT = """
+<div align="center">
+  <a href="https://ecologits.ai/">
+    <img style="max-height: 80px" alt="EcoLogits" src="https://raw.githubusercontent.com/genai-impact/ecologits/main/docs/assets/logo_light.png">
+  </a>
+</div>
+<h1 align="center">🧮 EcoLogits Calculator</h1>
+<div align="center">
+  <p style="max-width: 500px; text-align: center">
+    <i><b>EcoLogits</b> is a python library that tracks the <b>energy consumption</b> and <b>environmental 
+    footprint</b> of using <b>generative AI</b> models through APIs.</i>
+  </p>
+</div>
+<br>
+"""
+
+INTRO_TEXT = """
+This tool is developed and maintained by [GenAI Impact](https://genai-impact.org/) non-profit. Learn more about 
+🌱 EcoLogits by reading the documentation on [ecologits.ai](https://ecologits.ai).
+ 
+🩷 Support us by giving a ⭐️ on our [GitHub repository](https://github.com/genai-impact/ecologits) and by following our [LinkedIn page](https://www.linkedin.com/company/genai-impact/).
+"""
+
+WARNING_CLOSED_SOURCE = """
+⚠️ The model architecture has not been publicly released, expect lower precision of estimations.
+"""
+
+WARNING_MULTI_MODAL = """
+⚠️ The model architecture is multimodal, expect lower precision of estimations.
+"""
+
+WARNING_BOTH = """
+⚠️ The model architecture has not been publicly released and is multimodal, expect lower precision of estimations.
+"""
+
+ABOUT_TEXT = r"""
+## 🎯 Our goal
+**The main goal of the EcoLogits Calculator is to raise awareness on the environmental impacts of LLM inference.**
+The rapid evolution of generative AI is reshaping numerous industries and aspects of our daily lives. While these 
+advancements offer some benefits, they also **pose substantial environmental challenges that cannot be overlooked**. 
+Plus the issue of AI's environmental footprint has been mainly discussed at training stage but rarely at the inference 
+stage. That is an issue because **inference impacts for LLMs can largely overcome the training impacts when deployed 
+at large scales**.
+At **[GenAI Impact](https://genai-impact.org/) we are dedicated to understanding and mitigating the environmental 
+impacts of generative AI** through rigorous research, innovative tools, and community engagement. Especially, in early 
+2024 we have launched an new open-source tool called [EcoLogits](https://github.com/genai-impact/ecologits) that tracks
+the energy consumption and environmental footprint of using generative AI models through APIs.
+## 🙋 FAQ 
+**How we assess the impacts of closed-source models?**
+Environmental impacts are calculated based on model architecture and parameter count. For closed-source models, we 
+lack transparency from providers, so we estimate parameter counts using available information. For GPT models, we 
+based our estimates on leaked GPT-4 architecture and scaled parameters count for GPT-4-Turbo and GPT-4o based on 
+pricing differences. For other proprietary models like Anthropic's Claude, we assume similar impacts for models 
+released around the same time with similar performance on public benchmarks. Please note that these estimates are 
+based on assumptions and may not be exact. Our methods are open-source and transparent so you can always see the 
+hypotheses we use.
+**Which generative AI models or providers are supported?**
+To see the full list of **generative AI providers** currently supported by EcoLogits, see the following 
+[documentation page](https://ecologits.ai/providers/). As of today we only support LLMs but we plan to add support for 
+embeddings, image generation, multi-modal models and more. If you are interested don't hesitate to 
+[join us](https://genai-impact.org/contact/) and accelerate our work!
+**How to reduce AI environmental impacts?**
+* Look at **indirect impacts** of your project. Does the finality of your project is impacting negatively the 
+environment?
+* **Be frugal** and question your usage or need of AI
+    * Do you really need AI to solve your problem?
+    * Do you really need GenAI to solve your problem? (you can read this [paper](https://aclanthology.org/2023.emnlp-industry.39.pdf))
+    * Use small and specialized models to solve your problem.
+    * Evaluate before, during and after the development of your project the environmental impacts with tools like 
+    🌱 [EcoLogits](https://github.com/genai-impact/ecologits) or [CodeCarbon](https://github.com/mlco2/codecarbon) 
+    (see [more tools](https://github.com/samuelrince/awesome-green-ai))
+    * Restrict the use case and limit the usage of your tool or feature to the desired purpose.
+* Do NOT buy new GPUs / hardware
+    * Hardware manufacturing for data centers is around 50% of the impact.
+* Use cloud instances that are located in low emissions / high energy efficiency data centers 
+(see [electricitymaps.com](https://app.electricitymaps.com/map))
+* Optimize your models for production
+    * Quantize your models.
+    * Use inference optimization tricks.
+    * Prefer fine-tuning of small and existing models over generalist models.
+**What is the difference between **EcoLogits** and [CodeCarbon](https://github.com/mlco2/codecarbon)?**
+EcoLogits is focused on estimating the environmental impacts of generative AI (only LLMs for now) used **through API 
+providers (such as OpenAI, Anthropic, Cloud APIs...)** whereas  CodeCarbon is more general tool to measure energy 
+consumption and estimate GHG emissions measurement. If you deploy LLMs locally we encourage you to use CodeCarbon to 
+get real numbers of your energy consumption.
+## 🤗 Contributing 
+We are eager to get feedback from the community, don't hesitate to engage the discussion with us on this 
+[GitHub thread](https://github.com/genai-impact/ecologits/discussions/45) or message us on 
+[LinkedIn](https://www.linkedin.com/company/genai-impact/).
+We also welcome any open-source contributions on 🌱 **[EcoLogits](https://github.com/genai-impact/ecologits)** or on 
+🧮 **EcoLogits Calculator**.
+## ⚖️ License
+<p xmlns:cc="http://creativecommons.org/ns#" >
+  This work is licensed under 
+  <a href="https://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">
+    CC BY-SA 4.0
+  </a>
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1" alt="">
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1" alt="">
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1" alt="">
+</p>
+## 🙌 Acknowledgement
+We thank [Data For Good](https://dataforgood.fr/) and [Boavizta](https://boavizta.org/en) for supporting the 
+development of this project. Their contributions of tools, best practices, and expertise in environmental impact 
+assessment have been invaluable.
+We also extend our gratitude to the open-source contributions of 🤗 [Hugging Face](huggingface.com) on the LLM-Perf 
+Leaderboard.
+## 🤝 Contact
+For general question on the project, please use the [GitHub thread](https://github.com/genai-impact/ecologits/discussions/45). 
+Otherwise use our contact form on [genai-impact.org/contact](https://genai-impact.org/contact/).
+"""
+
+
+METHODOLOGY_TEXT = r"""
+## 📖 Methodology
+We have developed a methodology to **estimate the energy consumption and environmental impacts for an LLM inference** 
+based on request parameters and hypotheses on the data center location, the hardware used, the model architecture and
+more.
+In this section we will only cover the principles of the methodology related to the 🧮 **EcoLogits Calculator**. If 
+you wish to learn more on the environmental impacts modeling of an LLM request checkout the 
+🌱 [EcoLogits documentation page](https://ecologits.ai/methodology/).
+### Modeling impacts of an LLM request
+The environmental impacts of an LLM inference are split into the **usage impacts** $I_{request}^u$ to account for 
+electricity consumption and the **embodied impacts** $I_{request}^e$ that relates to resource extraction, hardware 
+manufacturing and transportation. In general terms it can be expressed as follow:
+$$ I_{request} = I_{request}^u  + I_{request}^e $$
+$$ I_{request} = E_{request}*F_{em}+\frac{\Delta T}{\Delta L}*I_{server}^e $$
+With,
+* $E_{request}$ the estimated energy consumption of the server and its cooling system.
+* $F_{em}$ the electricity mix that depends on the country and time.
+* $\frac{\Delta T}{\Delta L}$ the hardware usage ratio i.e. the computation time over the lifetime of the hardware.
+* $I_{server}^e$ the embodied impacts of the server.
+Additionally, to ⚡️ **direct energy consumption** the environmental impacts are expressed in **three dimensions 
+(multi-criteria impacts)** that are:
+* 🌍 **Global Warming Potential** (GWP): Potential impact on global warming in kgCO2eq (commonly known as GHG/carbon 
+emissions).
+* 🪨 **Abiotic Depletion Potential for Elements** (ADPe): Impact on the depletion of non-living resources such as 
+minerals or metals in kgSbeq.
+* ⛽️ **Primary Energy** (PE): Total energy consumed from primary sources in MJ.
+### Principles, Data and Hypotheses
+We use a **bottom-up methodology** to model impacts, meaning that we will estimate the impacts of low-level physical 
+components to then estimate the impacts at software level (in that case an LLM inference). We also rely on **Life 
+Cycle Approach (LCA) proxies and approach** to model both usage and embodied phases with multi-criteria impacts. 
+If you are interested in this approach we recommend you to read the following [Boavizta](https://boavizta.org/) 
+resources.
+* [Digital & environment: How to evaluate server manufacturing footprint, beyond greenhouse gas emissions?](https://boavizta.org/en/blog/empreinte-de-la-fabrication-d-un-serveur) 
+* [Boavizta API automated evaluation of environmental impacts of ICT services and equipments](https://boavizta.org/en/blog/boavizta-api-automated-evaluation-of-ict-impacts-on-the-environment)
+* [Boavizta API documentation](https://doc.api.boavizta.org/)
+We leverage **open data to estimate the environmental impacts**, here is an exhaustive list of our data providers.
+* [LLM-Perf Leaderboard](https://huggingface.co/spaces/optimum/llm-perf-leaderboard) to estimate GPU energy consumption 
+and latency based on the model architecture and number of output tokens.
+* [Boavizta API](https://github.com/Boavizta/boaviztapi) to estimate server embodied impacts and base energy 
+consumption.
+* [ADEME Base Empreinte®](https://base-empreinte.ademe.fr/) for electricity mix impacts per country.
+Finally here are the **main hypotheses** we have made to compute the impacts.
+* ⚠️ **We *"guesstimate"* the model architecture of proprietary LLMs when not disclosed by the provider.** 
+* Production setup: quantized models running on data center grade servers and GPUs such as A100.
+* Electricity mix does not depend on time (help us enhance EcoLogits and work on this [issue](https://github.com/genai-impact/ecologits/issues/42))
+* Ignore the following impacts: unused cloud resources, data center building, network and end-user devices... (for now)
+## Equivalents
+We have integrated impact equivalents to help people better understand the impacts and have reference points for 
+standard use cases and everyday activities.
+### Request impacts
+These equivalents are computed based on the request impacts only.
+#### 🚶‍♂️‍➡️ Walking or 🏃‍♂️‍➡️ running distance
+We compare the ⚡️ direct energy consumption with the energy consumption of someone 🚶‍♂️‍➡️ walking or 🏃‍♂️‍➡️ running. 
+From [runningtools.com](https://www.runningtools.com/energyusage.htm) we consider the following energy values per 
+physical activity (for someone weighing 70kg):
+* 🚶‍♂️‍➡️ walking: $ 196\ kJ/km $ (speed of $ 3\ km/h $)
+* 🏃‍♂️‍➡️ running: $ 294\ kJ/km $ (speed of $ 10\ km/h $)
+We divide the request energy consumption by these values to compute the distance traveled. 
+#### 🔋 Electric Vehicle distance
+We compare the ⚡️ direct energy consumption with the energy consumer by a EV car. From 
+[selectra.info](https://selectra.info/energie/actualites/insolite/consommation-vehicules-electriques-france-2040) or 
+[tesla.com](https://www.tesla.com/fr_fr/support/power-consumption) we consider an average value of energy consumed per 
+kilometer of: $ 0.17\ kWh/km $.
+We divide the request energy consumption by this value to compute the distance driven by an EV. 
+#### ⏯️ Streaming time
+We compare the 🌍 GHG emissions of the request and of streaming a video. From 
+[impactco2.fr](https://impactco2.fr/outils/comparateur?value=1&comparisons=streamingvideo), we consider that 
+$ 1\ kgCO2eq $ is equivalent to $ 15.6\ h $ of streaming.
+We multiply that value by the GHG emissions of the request to get an equivalent in hours of video streaming.
+### Scaled impacts
+These equivalents are computed based on the request impacts scaled to a worldwide adoption use case. We imply that the
+same request is done 1% of the planet everyday for 1 year, and then compute impact equivalents.
+$$
+I_{scaled} = I_{request} * [1 \\% \ \text{of}\ 8B\ \text{people on earth}] * 365\ \text{days}
+$$
+#### Number of 💨 wind turbines or ☢️ nuclear plants
+We compare the ⚡️ direct energy consumption (scaled) by the energy production of wind turbines and nuclear power 
+plants. From [ecologie.gouv.fr](https://www.ecologie.gouv.fr/eolien-terrestre) we consider that a $ 2\ MW $ wind 
+turbine produces $ 4.2\ GWh $ a year. And from [edf.fr](https://www.edf.fr/groupe-edf/espaces-dedies/jeunes-enseignants/pour-les-jeunes/lenergie-de-a-a-z/produire-de-lelectricite/le-nucleaire-en-chiffres) 
+we learn that a $ 900\ MW $ nuclear power plant produces $ 6\ TWh $ a year.
+We divide the scaled energy consumption by these values to get the number of wind turbines or nuclear power plants 
+needed.
+#### Multiplier of 🇮🇪 Ireland electricity consumption
+We compare the ⚡️ direct energy consumption (scaled) by the electricity consumption of Ireland per year. From 
+[wikipedia.org](https://en.wikipedia.org/wiki/List_of_countries_by_electricity_consumption) we consider the Ireland 
+electricity consumption to be $ 33\ TWh $ a year for a population of 5M.
+We divide the scaled energy consumption by this value to get the equivalent number of "Ireland countries".
+#### Number of ✈️ Paris ↔ New York City flights
+We compare the 🌍 GHG emissions (scaled) of the request and of a return flight Paris ↔ New York City. From 
+[impactco2.fr](https://impactco2.fr/outils/comparateur?value=1&comparisons=&equivalent=avion-pny) we consider that a 
+return flight Paris → New York City → Paris for one passenger emits $ 1,770\ kgCO2eq $ and we consider an overall 
+average load of 100 passengers per flight.
+We divide the scaled GHG emissions by this value to get the equivalent number of return flights.
+
+#### If you are motivated to help us test and enhance this methodology [contact us](https://genai-impact.org/contact/)! 💪
+"""
+
+CITATION_LABEL = "BibTeX citation for EcoLogits Calculator and the EcoLogits library:"
+CITATION_TEXT = r"""@misc{ecologits-calculator,
+  author={Samuel Rincé, Adrien Banse and Valentin Defour},
+  title={EcoLogits Calculator},
+  year={2025},
+  howpublished= {\url{https://huggingface.co/spaces/genai-impact/ecologits-calculator}},
+}
+@software{ecologits,
+  author = {Samuel Rincé, Adrien Banse, Vinh Nguyen and Luc Berton},
+  publisher = {GenAI Impact},
+  title = {EcoLogits: track the energy consumption and environmental footprint of using generative AI models through APIs.},
+}"""
+
+LICENCE_TEXT = """<p xmlns:cc="http://creativecommons.org/ns#" >
+  This work is licensed under 
+  <a href="https://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">
+    CC BY-SA 4.0
+  </a>
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1" alt="">
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1" alt="">
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1" alt="">
+</p>"""

{src → data}/electricity_mix.csv

@@ -1,4 +1,4 @@
-name,unit,source,WOR,EEE,ZWE,ZMB,ZAF,YEM,VNM,VEN,UZB,URY,USA,UKR,TZA,TWN,TTO,TUR,TUN,TKM,TJK,THA,TGO,SYR,SLV,SEN,SVK,SVN,SGP,SWE,SDN,SAU,RUS,SCG,ROU,QAT,PRY,PRT,POL,PAK,PHL,PER,PAN,OMN,NZL,NPL,NOR,NLD,NIC,NGA,NAM,MOZ,MYS,MEX,MLT,MNG,MMR,MKD,MDA,MAR,LBY,LVA,LUX,LTU,LKA,LBN,KAZ,KWT,KOR,PRK,KHM,KGZ,KEN,JPN,JOR,JAM,ITA,ISL,IRN,IRQ,IND,ISR,IRL,IDN,HUN,HTI,HRV,HND,HKG,GTM,GRC,GIB,GHA,GEO,GBR,GAB,FRA,FIN,ETH,ESP,ERI,EGY,EST,ECU,DZA,DOM,DNK,DEU,CZE,CYP,CUB,CRI,COL,CHN,CMR,CHL,CIV,CHE,COG,COD,CAN,BLR,BWA,BRA,BOL,BRN,BEN,BHR,BGR,BEL,BGD,BIH,AZE,AUS,AUT,ARG,AGO,ANT,ARM,ALB,ARE
-adpe,kg éq. Sb,ADEME Base IMPACTS ®,0.0000000737708,0.0000000642317,0.000000109502,0.000000162193,0.0000000862445,0.0000000163908,0.0000000945573,0.000000112811,0.000000103681,0.000000104586,0.0000000985548,0.0000000647907,0.000000132261,0.0000000578088,0.000000064552,0.0000000749765,0.0000000177021,0.000000131822,0.000000152621,0.0000000569593,0.000000134255,0.0000000268396,0.0000000472135,0.0000000470662,0.0000000606109,0.0000000992283,0.0000000198459,0.0000000777062,0.0000000955701,0.0000000134206,0.0000000960312,0.000000132772,0.0000000981761,0.00000001324,0.000000149181,0.0000000341863,0.000000101946,0.0000000842952,0.0000000595304,0.0000000952688,0.0000000790553,0.0000000374073,0.0000000720474,0.000000238273,0.000000127486,0.0000000329318,0.0000000414983,0.0000000621,0.000000128285,0.000000148382,0.000000044938,0.0000000578358,0.000000049475,0.000000176361,0.000000152699,0.000000119873,0.000000110674,0.0000000641089,0.0000000206592,0.000000153757,0.000000105692,0.0000000294596,0.0000000986932,0.0000000182134,0.000000135386,0.0000000141168,0.0000000518017,0.000000117457,0.0000000319202,0.000000181827,0.0000000958533,0.0000000596578,0.0000000147031,0.0000000196047,0.00000005439,0.0000000781905,0.0000000220304,0.0000000404306,0.000000100099,0.0000000610194,0.0000000219257,0.0000000610451,0.0000000644587,0.0000000937057,0.000000153989,0.0000000649373,0.0000000816213,0.0000000803251,0.0000000691645,0.0000000286211,0.000000156003,0.000000137999,0.0000000370973,0.000000113843,0.0000000485798,0.0000000805114,0.000000174161,0.0000000518326,0.0000000512406,0.000000033925,0.0000000990171,0.000000127168,0.0000000216438,0.0000000429285,0.0000000157411,0.0000000878733,0.0000000817565,0.0000000448771,0.0000000299542,0.0000000863908,0.000000122031,0.0000000851552,0.000000146313,0.000000105851,0.0000000949004,0.000000100467,0.000000265575,0.000000174647,0.0000000993179,0.0000000840478,0.0000000866014,0.00000010962,0.0000000969793,0.0000000185641,0.0000000239702,0.0000000135014,0.0000000823611,0.0000000337201,0.0000000394158,0.000000148007,0.000000092567,0.0000000790846,0.000000141124,0.0000000768612,0.000000124074,0.0000000449103,0.0000000854245,0.000000229556,0.0000000141548
-pe,MJ,ADPf / (1-%renewable_energy),9.988,12.873,16.122,1.798,11.732,16.250,11.238,15.163,17.367,107.570,11.358,12.936,9.305,11.439,14.289,16.150,12.902,23.300,19.165,10.414,21.978,16.989,13.012,14.516,11.680,12.146,10.477,11.026,29.629,14.058,13.200,14.242,15.585,11.916,0.020,14.153,13.178,16.175,11.120,8.211,16.364,22.306,24.731,0.396,4.952,8.511,24.696,11.279,468.150,0.206,12.268,11.775,19.374,15.114,14.132,19.120,18.429,11.702,19.116,8.249,10.128,21.043,12.116,12.341,13.260,12.753,10.199,32.793,34.655,15.380,68.996,10.718,13.677,14.799,12.656,0.013,15.022,20.372,20.363,10.023,10.706,11.603,11.784,20.167,18.548,15.762,,14.340,14.487,,10.097,10.425,13.579,28.341,11.289,11.275,36.133,12.090,13.289,10.195,16.334,20.908,16.376,12.412,16.824,16.260,12.517,13.118,17.317,45.996,7.312,14.119,10.807,11.348,14.783,11.782,34.147,0.097,11.987,13.194,19.642,9.031,11.587,15.689,14.337,14.036,14.375,10.776,12.935,21.705,12.831,16.908,11.036,10.049,16.972,,13.380,0.201,19.032
+name,unit,source,WOR,EEE,ZWE,ZMB,ZAF,YEM,VNM,VEN,UZB,URY,USA,UKR,TZA,TWN,TTO,TUR,TUN,TKM,TJK,THA,TGO,SYR,SLV,SEN,SVK,SVN,SGP,SWE,SDN,SAU,RUS,SCG,ROU,QAT,PRY,PRT,POL,PAK,PHL,PER,PAN,OMN,NZL,NPL,NOR,NLD,NIC,NGA,NAM,MOZ,MYS,MEX,MLT,MNG,MMR,MKD,MDA,MAR,LBY,LVA,LUX,LTU,LKA,LBN,KAZ,KWT,KOR,PRK,KHM,KGZ,KEN,JPN,JOR,JAM,ITA,ISL,IRN,IRQ,IND,ISR,IRL,IDN,HUN,HTI,HRV,HND,HKG,GTM,GRC,GIB,GHA,GEO,GBR,GAB,FRA,FIN,ETH,ESP,ERI,EGY,EST,ECU,DZA,DOM,DNK,DEU,CZE,CYP,CUB,CRI,COL,CHN,CMR,CHL,CIV,CHE,COG,COD,CAN,BLR,BWA,BRA,BOL,BRN,BEN,BHR,BGR,BEL,BGD,BIH,AZE,AUS,AUT,ARG,AGO,ANT,ARM,ALB,ARE
+adpe,kg éq. Sb,ADEME Base IMPACTS ®,0.0000000737708,0.0000000642317,0.000000109502,0.000000162193,0.0000000862445,0.0000000163908,0.0000000945573,0.000000112811,0.000000103681,0.000000104586,0.0000000985548,0.0000000647907,0.000000132261,0.0000000578088,0.000000064552,0.0000000749765,0.0000000177021,0.000000131822,0.000000152621,0.0000000569593,0.000000134255,0.0000000268396,0.0000000472135,0.0000000470662,0.0000000606109,0.0000000992283,0.0000000198459,0.0000000777062,0.0000000955701,0.0000000134206,0.0000000960312,0.000000132772,0.0000000981761,0.00000001324,0.000000149181,0.0000000341863,0.000000101946,0.0000000842952,0.0000000595304,0.0000000952688,0.0000000790553,0.0000000374073,0.0000000720474,0.000000238273,0.000000127486,0.0000000329318,0.0000000414983,0.0000000621,0.000000128285,0.000000148382,0.000000044938,0.0000000578358,0.000000049475,0.000000176361,0.000000152699,0.000000119873,0.000000110674,0.0000000641089,0.0000000206592,0.000000153757,0.000000105692,0.0000000294596,0.0000000986932,0.0000000182134,0.000000135386,0.0000000141168,0.0000000518017,0.000000117457,0.0000000319202,0.000000181827,0.0000000958533,0.0000000596578,0.0000000147031,0.0000000196047,0.00000005439,0.0000000781905,0.0000000220304,0.0000000404306,0.000000100099,0.0000000610194,0.0000000219257,0.0000000610451,0.0000000644587,0.0000000937057,0.000000153989,0.0000000649373,0.0000000816213,0.0000000803251,0.0000000691645,0.0000000286211,0.000000156003,0.000000137999,0.0000000370973,0.000000113843,0.0000000485798,0.0000000805114,0.000000174161,0.0000000518326,0.0000000512406,0.000000033925,0.0000000990171,0.000000127168,0.0000000216438,0.0000000429285,0.0000000157411,0.0000000878733,0.0000000817565,0.0000000448771,0.0000000299542,0.0000000863908,0.000000122031,0.0000000851552,0.000000146313,0.000000105851,0.0000000949004,0.000000100467,0.000000265575,0.000000174647,0.0000000993179,0.0000000840478,0.0000000866014,0.00000010962,0.0000000969793,0.0000000185641,0.0000000239702,0.0000000135014,0.0000000823611,0.0000000337201,0.0000000394158,0.000000148007,0.000000092567,0.0000000790846,0.000000141124,0.0000000768612,0.000000124074,0.0000000449103,0.0000000854245,0.000000229556,0.0000000141548
+pe,MJ,ADPf / (1-%renewable_energy),9.988,12.873,16.122,1.798,11.732,16.250,11.238,15.163,17.367,107.570,11.358,12.936,9.305,11.439,14.289,16.150,12.902,23.300,19.165,10.414,21.978,16.989,13.012,14.516,11.680,12.146,10.477,11.026,29.629,14.058,13.200,14.242,15.585,11.916,0.020,14.153,13.178,16.175,11.120,8.211,16.364,22.306,24.731,0.396,4.952,8.511,24.696,11.279,468.150,0.206,12.268,11.775,19.374,15.114,14.132,19.120,18.429,11.702,19.116,8.249,10.128,21.043,12.116,12.341,13.260,12.753,10.199,32.793,34.655,15.380,68.996,10.718,13.677,14.799,12.656,0.013,15.022,20.372,20.363,10.023,10.706,11.603,11.784,20.167,18.548,15.762,,14.340,14.487,,10.097,10.425,13.579,28.341,11.289,11.275,36.133,12.090,13.289,10.195,16.334,20.908,16.376,12.412,16.824,16.260,12.517,13.118,17.317,45.996,7.312,14.119,10.807,11.348,14.783,11.782,34.147,0.097,11.987,13.194,19.642,9.031,11.587,15.689,14.337,14.036,14.375,10.776,12.935,21.705,12.831,16.908,11.036,10.049,16.972,,13.380,0.201,19.032
 gwp,kg éq. CO2,ADEME Base IMPACTS ®,0.590478,0.509427,0.842811,0.0141304,1.17562,1.06777,0.555572,0.497373,0.81178,0.296953,0.67978,0.646745,0.475635,0.845351,0.933059,0.706988,0.80722,1.38296,0.0426743,0.646174,0.545455,1.08778,0.473128,1.1195,0.309341,0.498523,0.655825,0.0464664,1.12472,0.913677,0.66131,1.07808,0.664245,0.722125,0.241601,0.571172,1.15075,0.748727,0.761317,0.284364,0.53403,1.41292,0.293397,0.0841323,0.023754,0.544803,0.941626,0.693123,0.357253,0.00880732,0.832206,0.739214,1.31149,1.47192,0.48193,1.24074,1.04213,0.933694,1.35361,0.234273,0.490016,0.154229,0.709185,0.883627,1.128,0.885084,0.599585,0.797361,1.41054,0.156039,0.589603,0.540891,0.781372,1.07345,0.621329,0.0194609,0.930385,1.48728,1.58299,0.901842,0.648118,0.875394,0.541558,1.3858,0.535759,0.692837,0.95888,0.645801,1.13127,0.977477,0.540126,0.132046,0.602137,0.732511,0.0813225,0.322068,0.251299,0.467803,1.13153,0.587775,1.51492,0.627714,1.02318,0.909252,0.633534,0.657374,0.799077,0.978041,1.28325,0.224471,0.214014,1.05738,0.456622,0.574415,0.928583,0.0448568,0.904053,0.0613102,0.238191,0.795697,2.12572,0.278647,0.585131,0.997577,1.19128,0.804567,0.827087,0.256537,0.795168,1.56025,0.726126,1.12326,0.245573,0.476811,0.412014,1.13161,0.252002,0.0207635,1.09088

electricity_mix.py

@@ -0,0 +1,175 @@
+from csv import DictReader
+import pandas as pd
+
+PATH = "src/data/electricity_mix.csv"
+
+COUNTRY_CODES = [
+    ("🌎 World", "WOR"),
+    ("🇪🇺 Europe", "EEE"),
+    ("🇿🇼 Zimbabwe", "ZWE"),
+    ("🇿🇲 Zambia", "ZMB"),
+    ("🇿🇦 South Africa", "ZAF"),
+    ("🇾🇪 Yemen", "YEM"),
+    ("🇻🇳 Vietnam", "VNM"),
+    ("🇻🇪 Venezuela", "VEN"),
+    ("🇺🇿 Uzbekistan", "UZB"),
+    ("🇺🇾 Uruguay", "URY"),
+    ("🇺🇸 United States", "USA"),
+    ("🇺🇦 Ukraine", "UKR"),
+    ("🇹🇿 Tanzania", "TZA"),
+    ("🇹🇼 Taiwan", "TWN"),
+    ("🇹🇹 Trinidad and Tobago", "TTO"),
+    ("🇹🇷 Turkey", "TUR"),
+    ("🇹🇳 Tunisia", "TUN"),
+    ("🇹🇲 Turkmenistan", "TKM"),
+    ("🇹🇯 Tajikistan", "TJK"),
+    ("🇹🇭 Thailand", "THA"),
+    ("🇹🇬 Togo", "TGO"),
+    ("🇸🇾 Syrian Arab Republic", "SYR"),
+    ("🇸🇻 El Salvador", "SLV"),
+    ("🇸🇳 Senegal", "SEN"),
+    ("🇸🇰 Slovak Republic", "SVK"),
+    ("🇸🇮 Slovenia", "SVN"),
+    ("🇸🇬 Singapore", "SGP"),
+    ("🇸🇪 Sweden", "SWE"),
+    ("🇸🇩 Sudan", "SDN"),
+    ("🇸🇦 Saudi Arabia", "SAU"),
+    ("🇷🇺 Russian Federation", "RUS"),
+    ("🇷🇸 Serbia and Montenegro", "SCG"),
+    ("🇷🇴 Romania", "ROU"),
+    ("🇶🇦 Qatar", "QAT"),
+    ("🇵🇾 Paraguay", "PRY"),
+    ("🇵🇹 Portugal", "PRT"),
+    ("🇵🇱 Poland", "POL"),
+    ("🇵🇰 Pakistan", "PAK"),
+    ("🇵🇭 Philippines", "PHL"),
+    ("🇵🇪 Peru", "PER"),
+    ("🇵🇦 Panama", "PAN"),
+    ("🇴🇲 Oman", "OMN"),
+    ("🇳🇿 New Zealand", "NZL"),
+    ("🇳🇵 Nepal", "NPL"),
+    ("🇳🇴 Norway", "NOR"),
+    ("🇳🇱 Netherlands", "NLD"),
+    ("🇳🇮 Nicaragua", "NIC"),
+    ("🇳🇬 Nigeria", "NGA"),
+    ("🇳🇦 Namibia", "NAM"),
+    ("🇲🇿 Mozambique", "MOZ"),
+    ("🇲🇾 Malaysia", "MYS"),
+    ("🇲🇽 Mexico", "MEX"),
+    ("🇲🇹 Malta", "MLT"),
+    ("🇲🇳 Mongolia", "MNG"),
+    ("🇲🇲 Myanmar", "MMR"),
+    ("🇲🇰 North Macedonia", "MKD"),
+    ("🇲🇩 Moldova", "MDA"),
+    ("🇲🇦 Morocco", "MAR"),
+    ("🇱🇾 Libya", "LBY"),
+    ("🇱🇻 Latvia", "LVA"),
+    ("🇱🇺 Luxembourg", "LUX"),
+    ("🇱🇹 Lithuania", "LTU"),
+    ("🇱🇰 Sri Lanka", "LKA"),
+    ("🇱🇧 Lebanon", "LBN"),
+    ("🇰🇿 Kazakhstan", "KAZ"),
+    ("🇰🇼 Kuwait", "KWT"),
+    ("🇰🇷 South Korea", "KOR"),
+    ("🇰🇵 North Korea", "PRK"),
+    ("🇰🇭 Cambodia", "KHM"),
+    ("🇰🇬 Kyrgyz Republic", "KGZ"),
+    ("🇰🇪 Kenya", "KEN"),
+    ("🇯🇵 Japan", "JPN"),
+    ("🇯🇴 Jordan", "JOR"),
+    ("🇯🇲 Jamaica", "JAM"),
+    ("🇮🇹 Italy", "ITA"),
+    ("🇮🇸 Iceland", "ISL"),
+    ("🇮🇷 Iran", "IRN"),
+    ("🇮🇶 Iraq", "IRQ"),
+    ("🇮🇳 India", "IND"),
+    ("🇮🇱 Israel", "ISR"),
+    ("🇮🇪 Ireland", "IRL"),
+    ("🇮🇩 Indonesia", "IDN"),
+    ("🇭🇺 Hungary", "HUN"),
+    ("🇭🇹 Haiti", "HTI"),
+    ("🇭🇷 Croatia", "HRV"),
+    ("🇭🇳 Honduras", "HND"),
+    ("🇭🇰 Hong Kong", "HKG"),
+    ("🇬🇹 Guatemala", "GTM"),
+    ("🇬🇷 Greece", "GRC"),
+    ("🇬🇮 Gibraltar", "GIB"),
+    ("🇬🇭 Ghana", "GHA"),
+    ("🇬🇪 Georgia", "GEO"),
+    ("🇬🇧 United Kingdom", "GBR"),
+    ("🇬🇦 Gabon", "GAB"),
+    ("🇫🇷 France", "FRA"),
+    ("🇫🇮 Finland", "FIN"),
+    ("🇪🇹 Ethiopia", "ETH"),
+    ("🇪🇸 Spain", "ESP"),
+    ("🇪🇷 Eritrea", "ERI"),
+    ("🇪🇬 Egypt", "EGY"),
+    ("🇪🇪 Estonia", "EST"),
+    ("🇪🇨 Ecuador", "ECU"),
+    ("🇩🇿 Algeria", "DZA"),
+    ("🇩🇴 Dominican Republic", "DOM"),
+    ("🇩🇰 Denmark", "DNK"),
+    ("🇩🇪 Germany", "DEU"),
+    ("🇨🇿 Czech Republic", "CZE"),
+    ("🇨🇾 Cyprus", "CYP"),
+    ("🇨🇺 Cuba", "CUB"),
+    ("🇨🇷 Costa Rica", "CRI"),
+    ("🇨🇴 Colombia", "COL"),
+    ("🇨🇳 China", "CHN"),
+    ("🇨🇲 Cameroon", "CMR"),
+    ("🇨🇱 Chile", "CHL"),
+    ("🇨🇮 Cote d'Ivoire", "CIV"),
+    ("🇨🇭 Switzerland", "CHE"),
+    ("🇨🇬 Congo", "COG"),
+    ("🇨🇩 Democratic Republic of the Congo", "COD"),
+    ("🇨🇦 Canada", "CAN"),
+    ("🇧🇾 Belarus", "BLR"),
+    ("🇧🇼 Botswana", "BWA"),
+    ("🇧🇷 Brazil", "BRA"),
+    ("🇧🇴 Bolivia", "BOL"),
+    ("🇧🇳 Brunei", "BRN"),
+    ("🇧🇯 Benin", "BEN"),
+    ("🇧🇭 Bahrain", "BHR"),
+    ("🇧🇬 Bulgaria", "BGR"),
+    ("🇧🇪 Belgium", "BEL"),
+    ("🇧🇩 Bangladesh", "BGD"),
+    ("🇧🇦 Bosnia and Herzegovina", "BIH"),
+    ("🇦🇿 Azerbaijan", "AZE"),
+    ("🇦🇺 Australia", "AUS"),
+    ("🇦🇹 Austria", "AUT"),
+    ("🇦🇷 Argentina", "ARG"),
+    ("🇦🇴 Angola", "AGO"),
+    ("🇦 Netherlands Antilles", "ANT"),
+    ("🇦🇲 Armenia", "ARM"),
+    ("🇦🇱 Albania", "ALB"),
+    ("🇦🇪 United Arab Emirates", "ARE")
+]
+
+
+def find_electricity_mix(code: str):
+    # TODO: Maybe more optimal to construct database at the beginning of the app
+    #       in the same fashion as find_model
+    res = []
+    with open(PATH) as fd:
+        csv = DictReader(fd)
+        for row in csv:
+            res += [float(row[code])]
+    return res
+
+def dataframe_electricity_mix(countries: list):
+
+    df = pd.read_csv('src/data/electricity_mix.csv')
+    df['name_unit'] = df['name'] + ' (' + df['unit'] + ')'
+    df = df[['name_unit'] + [x[1] for x in COUNTRY_CODES if x[0] in countries]]
+
+    df_melted = df.melt(
+        id_vars=['name_unit'],
+        value_vars=[x[1] for x in COUNTRY_CODES if x[0] in countries],
+        var_name='country',
+        value_name='value')
+
+    df = df_melted.pivot(columns='name_unit', 
+                        index='country',
+                        values='value')
+
+    return df

expert.py

@@ -0,0 +1,156 @@
+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.")

impacts.py

@@ -0,0 +1,116 @@
+import streamlit as st
+import ecologits
+from src.utils import (
+    format_energy_eq_electric_vehicle,
+    format_energy_eq_electricity_consumption_ireland,
+    format_energy_eq_electricity_production,
+    format_energy_eq_physical_activity,
+    format_gwp_eq_airplane_paris_nyc,
+    format_gwp_eq_streaming,
+    PhysicalActivity,
+    EnergyProduction
+)
+
+############################################################################################################
+
+def get_impacts(model, active_params, total_params, mix_ghg, mix_adpe, mix_pe):
+
+    return 1
+
+############################################################################################################
+
+
+def display_impacts(impacts):
+
+    st.divider()
+
+    col_energy, col_ghg, col_adpe, col_pe, col_water = st.columns(5)
+
+    with col_energy:
+        st.markdown('<h4 align="center">⚡️ Energy</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.energy.magnitude:.3g} \ \large {impacts.energy.units}')
+        st.markdown(f'<p align="center"><i>Evaluates the electricity consumption<i></p>', unsafe_allow_html = True)
+
+    with col_ghg:
+        st.markdown('<h4 align="center">🌍️ GHG Emissions</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.gwp.magnitude:.3g} \ \large {impacts.gwp.units}')
+        st.markdown(f'<p align="center"><i>Evaluates the effect on global warming<i></p>', unsafe_allow_html = True)
+
+    with col_adpe:
+        st.markdown('<h4 align="center">🪨 Abiotic Resources</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.adpe.magnitude:.3g} \ \large {impacts.adpe.units}')
+        st.markdown(f'<p align="center"><i>Evaluates the use of metals and minerals<i></p>', unsafe_allow_html = True)
+
+    with col_pe:
+        st.markdown('<h4 align="center">⛽️ Primary Energy</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.pe.magnitude:.3g} \ \large {impacts.pe.units}')
+        st.markdown(f'<p align="center"><i>Evaluates the use of energy resources<i></p>', unsafe_allow_html = True)
+
+    with col_water:
+        st.markdown('<h4 align="center">🚰 Water</h4>', unsafe_allow_html = True)
+        st.latex('\Large Upcoming...')
+        st.markdown(f'<p align="center"><i>Evaluates the use of water<i></p>', unsafe_allow_html = True)
+
+############################################################################################################
+
+def display_equivalent(impacts):
+
+    st.divider()
+
+    ev_eq = format_energy_eq_electric_vehicle(impacts.energy)
+    
+    streaming_eq = format_gwp_eq_streaming(impacts.gwp)
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        physical_activity, distance = format_energy_eq_physical_activity(impacts.energy)
+        if physical_activity == PhysicalActivity.WALKING:
+            physical_activity = "🚶 " + physical_activity.capitalize()
+        if physical_activity == PhysicalActivity.RUNNING:
+            physical_activity = "🏃 " + physical_activity.capitalize()
+
+        st.markdown(f'<h4 align="center">{physical_activity}</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {distance.magnitude:.3g} \ \large {distance.units}')
+        st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
+
+    with col2:
+        ev_eq = format_energy_eq_electric_vehicle(impacts.energy)
+        st.markdown(f'<h4 align="center">🔋 Electric Vehicle</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {ev_eq.magnitude:.3g} \ \large {ev_eq.units}')
+        st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
+
+    with col3:
+        streaming_eq = format_gwp_eq_streaming(impacts.gwp)
+        st.markdown(f'<h4 align="center">⏯️ Streaming</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {streaming_eq.magnitude:.3g} \ \large {streaming_eq.units}')
+        st.markdown(f'<p align="center"><i>Based on GHG emissions<i></p>', unsafe_allow_html = True)
+    
+    st.divider()
+    
+    st.markdown('<h3 align="center">What if 1% of the planet does this request everyday for 1 year ?</h3>', unsafe_allow_html = True)
+    st.markdown('<p align="center">If this use case is largely deployed around the world, the equivalent impacts would be the impacts of this request x 1% of 8 billion people x 365 days in a year.</p>', unsafe_allow_html = True)
+
+    col4, col5, col6 = st.columns(3)
+
+    with col4:
+
+        electricity_production, count = format_energy_eq_electricity_production(impacts.energy)
+        if electricity_production == EnergyProduction.NUCLEAR:
+            emoji = "☢️"
+            name = "Nuclear power plants"
+        if electricity_production == EnergyProduction.WIND:
+            emoji = "💨️ "
+            name = "Wind turbines"
+        st.markdown(f'<h4 align="center">{emoji} {count.magnitude:.0f} {name} (yearly)</h4>', unsafe_allow_html = True)
+        st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
+        
+    with col5:
+        ireland_count = format_energy_eq_electricity_consumption_ireland(impacts.energy)
+        st.markdown(f'<h4 align="center">🇮🇪 {ireland_count.magnitude:.3f} x Ireland <span style="font-size: 12px">(yearly ⚡️ cons.)</span></h2></h4>', unsafe_allow_html = True)
+        st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
+
+    with col6:
+        paris_nyc_airplane = format_gwp_eq_airplane_paris_nyc(impacts.gwp)
+        st.markdown(f'<h4 align="center">✈️ {round(paris_nyc_airplane.magnitude):,} Paris ↔ NYC</h4>', unsafe_allow_html = True)
+        st.markdown(f'<p align="center"><i>Based on GHG emissions<i></p>', unsafe_allow_html = True)

models.py

@@ -0,0 +1,46 @@
+import requests
+import json
+import pandas as pd
+from src.constants import MODEL_REPOSITORY_URL
+
+def clean_models_data(df):
+    
+    dict_providers = {
+        'google': 'Google',
+        'mistralai': 'MistralAI',
+        'meta-llama': 'Meta',
+        'openai': 'OpenAI',
+        'anthropic': 'Anthropic',
+        'cohere': 'Cohere',
+        'microsoft': 'Microsoft',
+        'mistral-community': 'Mistral Community',
+        'databricks': 'Databricks'
+    }
+    
+    df.drop('type', axis=1, inplace=True)
+
+    df.loc[df['name'].str.contains('/'), 'name_clean'] = df.loc[df['name'].str.contains('/'), 'name'].str.split('/').str[1]
+    df['name_clean'] = df['name_clean'].fillna(df['name'])
+    df['name_clean'] = df['name_clean'].replace({'-': ' '}, regex = True)
+    
+    df.loc[df['provider'] == 'huggingface_hub', 'provider_clean'] = df.loc[df['provider'] == 'huggingface_hub', 'name'].str.split('/').str[0]
+    df['provider_clean'] = df['provider_clean'].fillna(df['provider'])
+    df['provider_clean'] = df['provider_clean'].replace(dict_providers, regex = True)
+    
+    df['architecture_type'] = df['architecture'].apply(lambda x: x['type'])
+    df['architecture_parameters'] = df['architecture'].apply(lambda x: x['parameters'])
+    
+    df['warnings'] = df['warnings'].apply(lambda x: ', '.join(x) if x else None).fillna('none')
+    df['warning_arch'] = df['warnings'].apply(lambda x: 'model-arch-not-released' in x)
+    df['warning_multi_modal'] = df['warnings'].apply(lambda x: 'model-arch-multimodal' in x)
+    
+    return df[['provider', 'provider_clean', 'name', 'name_clean', 'architecture_type', 'architecture_parameters', 'warning_arch', 'warning_multi_modal']]
+
+def load_models():
+
+    resp = requests.get(MODEL_REPOSITORY_URL)
+    data = json.loads(resp.text)
+    df = pd.DataFrame(data['models'])
+
+    return clean_models_data(df)
+

pyproject.toml

@@ -0,0 +1,12 @@
+[project]
+name = "ecologits-calculator-streamlit"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "ecologits>=0.6.1",
+    "pint>=0.24.4",
+    "plotly>=6.0.0",
+    "streamlit>=1.43.1",
+]

requirements-dev.txt

@@ -1,6 +0,0 @@
-gradio
-ecologits==0.5.1
-pint
-beautifulsoup4
-requests
-tiktoken

requirements.txt

@@ -1,5 +1,4 @@
-ecologits==0.5.1
+streamlit
+ecologits
 pint
-beautifulsoup4
-requests
-tiktoken
+plotly

src/__init__.py

@@ -0,0 +1,8 @@
+from .content import *
+from .constants import *
+from .expert import expert_mode
+from .utils import *
+from .calculator import calculator_mode
+from .impacts import get_impacts, display_impacts
+from .models import load_models
+from .electricity_mix import *

src/assets.py

@@ -1,17 +0,0 @@
-custom_css = """
-
-.descriptive-text span {
-    font-size: 16px !important;
-}
-
-
-.warning-box p {
-    background-color: #ff91001a;
-    border: 1px solid #f19637;
-    border-radius: 8px;
-    padding: 4px 8px;
-    margin: 4px 0px;
-    font-size: 14px !important;
-    font-weight: bold !important;
-}
-"""

src/calculator.py

@@ -0,0 +1,67 @@
+import streamlit as st
+import pandas as pd
+
+from ecologits.tracers.utils import llm_impacts
+from src.impacts import get_impacts, display_impacts, display_equivalent
+from src.utils import format_impacts
+from src.content import WARNING_CLOSED_SOURCE, WARNING_MULTI_MODAL, WARNING_BOTH
+from src.models import load_models, clean_models_data
+
+from src.constants import PROMPTS
+
+def calculator_mode():
+
+    with st.container(border=True):
+        
+        df = load_models()
+        
+        col1, col2, col3 = st.columns(3)
+
+        with col1:
+            provider = st.selectbox(label = 'Provider',
+                                    options = [x for x in df['provider_clean'].unique()],
+                                    index = 9)
+            provider_raw = df[df['provider_clean'] == provider]['provider'].values[0]
+
+        with col2:
+            model = st.selectbox('Model', [x for x in df['name_clean'].unique() if x in df[df['provider_clean'] == provider]['name_clean'].unique()])
+            model_raw = df[(df['provider_clean'] == provider) & (df['name_clean'] == model)]['name'].values[0]
+
+        with col3:
+            output_tokens = st.selectbox('Example prompt', [x[0] for x in PROMPTS])
+            
+        # WARNING DISPLAY
+        
+        df_filtered = df[(df['provider_clean'] == provider) & (df['name_clean'] == model)]
+
+        if df_filtered['warning_arch'].values[0] and not df_filtered['warning_multi_modal'].values[0]:
+            st.warning(WARNING_CLOSED_SOURCE)
+        if df_filtered['warning_multi_modal'].values[0] and not df_filtered['warning_arch'].values[0]:
+            st.warning(WARNING_MULTI_MODAL)
+        if df_filtered['warning_arch'].values[0] and df_filtered['warning_multi_modal'].values[0]:
+            st.warning(WARNING_BOTH)
+            
+    try:
+        impacts = llm_impacts(
+                        provider=provider_raw,
+                        model_name=model_raw,
+                        output_token_count=[x[1] for x in PROMPTS if x[0] == output_tokens][0],
+                        request_latency=100000
+                    )
+
+        impacts, _, _ = format_impacts(impacts)
+        
+        with st.container(border=True):
+
+            st.markdown('<h3 align = "center">Environmental impacts</h3>', unsafe_allow_html=True)
+            st.markdown('<p align = "center">To understand how the environmental impacts are computed go to the 📖 Methodology tab.</p>', unsafe_allow_html=True)
+            display_impacts(impacts)
+        
+        with st.container(border=True):
+            
+            st.markdown('<h3 align = "center">That\'s equivalent to ...</h3>', unsafe_allow_html=True)
+            st.markdown('<p align = "center">Making this request to the LLM is equivalent to the following actions :</p>', unsafe_allow_html=True)
+            display_equivalent(impacts)
+            
+    except Exception as e:
+        st.error('Could not find the model in the repository. Please try another model.')

src/constants.py

@@ -1,103 +1,106 @@
-PROVIDERS = [
-    ("OpenAI", "openai"),
-    ("Anthropic", "anthropic"),
-    ("Cohere", "cohere"),
-    ("Meta", "huggingface_hub/meta"),
-    ("Mistral AI", "mistralai"),
-]
-
-OPENAI_MODELS = [
-    ("GPT-4o", "gpt-4o"),
-    ("GPT-4-Turbo", "gpt-4-turbo"),
-    ("GPT-4", "gpt-4"),
-    ("GPT-3.5-Turbo", "gpt-3.5-turbo"),
-]
-
-ANTHROPIC_MODELS = [
-    ("Claude 3 Opus", "claude-3-opus-20240229"),
-    ("Claude 3 Sonnet", "claude-3-sonnet-20240229"),
-    ("Claude 3 Haiku", "claude-3-haiku-20240307"),
-    ("Claude 2.1", "claude-2.1"),
-    ("Claude 2.0", "claude-2.0"),
-    ("Claude Instant 1.2", "claude-instant-1.2"),
-]
-
-COHERE_MODELS = [
-    ("Command Light", "command-light"),
-    ("Command", "command"),
-    ("Command R", "command-r"),
-    ("Command R+", "command-r-plus"),
-]
-
-META_MODELS = [
-    ("Llama 3 8B", "meta-llama/Meta-Llama-3-8B"),
-    ("Llama 3 70B", "meta-llama/Meta-Llama-3-70B"),
-    ("Llama 2 7B", "meta-llama/Llama-2-7b-hf"),
-    ("Llama 2 13B", "meta-llama/Llama-2-13b-hf"),
-    ("Llama 2 70B", "meta-llama/Llama-2-70b-hf"),
-]
-
-MISTRALAI_MODELS = [
-    ("Mistral 7B", "open-mistral-7b"),
-    ("Mixtral 8x7B", "open-mixtral-8x7b"),
-    ("Mixtral 8x22B", "open-mixtral-8x22b"),
-    ("Tiny", "mistral-tiny-2312"),
-    ("Small", "mistral-small-2402"),
-    ("Medium", "mistral-medium-2312"),
-    ("Large", "mistral-large-2402"),
-]
-
-PROMPTS = [
-    ("Write a Tweet", 50),
-    ("Write an email", 170),
-    ("Write an article summary", 250),
-    ("Small conversation with a chatbot", 400),
-    ("Write a report of 5 pages", 5000),
-]
-PROMPTS = [(s + f" ({v} output tokens)", v) for (s, v) in PROMPTS]
-
-CLOSED_SOURCE_MODELS = {
-    "openai/gpt-4o",
-    "openai/gpt-4-turbo",
-    "openai/gpt-4",
-    "openai/gpt-3.5-turbo",
-    "anthropic/claude-3-opus-20240229",
-    "anthropic/claude-3-sonnet-20240229",
-    "anthropic/claude-3-haiku-20240307",
-    "anthropic/claude-2.1",
-    "anthropic/claude-2.0",
-    "anthropic/claude-instant-1.2",
-    "mistralai/mistral-tiny-2312",
-    "mistralai/mistral-small-2402",
-    "mistralai/mistral-medium-2312",
-    "mistralai/mistral-large-2402",
-}
-
-MODELS = [
-    ("OpenAI / GPT-4o", "openai/gpt-4o"),
-    ("OpenAI / GPT-4-Turbo", "openai/gpt-4-turbo"),
-    ("OpenAI / GPT-4", "openai/gpt-4"),
-    ("OpenAI / GPT-3.5-Turbo", "openai/gpt-3.5-turbo"),
-    ("Anthropic / Claude 3 Opus", "anthropic/claude-3-opus-20240229"),
-    ("Anthropic / Claude 3 Sonnet", "anthropic/claude-3-sonnet-20240229"),
-    ("Anthropic / Claude 3 Haiku", "anthropic/claude-3-haiku-20240307"),
-    ("Anthropic / Claude 2.1", "anthropic/claude-2.1"),
-    ("Anthropic / Claude 2.0", "anthropic/claude-2.0"),
-    ("Anthropic / Claude Instant 1.2", "anthropic/claude-instant-1.2"),
-    ("Mistral AI / Mistral 7B", "mistralai/open-mistral-7b"),
-    ("Mistral AI / Mixtral 8x7B", "mistralai/open-mixtral-8x7b"),
-    ("Mistral AI / Mixtral 8x22B", "mistralai/open-mixtral-8x22b"),
-    ("Mistral AI / Tiny", "mistralai/mistral-tiny-2312"),
-    ("Mistral AI / Small", "mistralai/mistral-small-2402"),
-    ("Mistral AI / Medium", "mistralai/mistral-medium-2312"),
-    ("Mistral AI / Large", "mistralai/mistral-large-2402"),
-    ("Meta / Llama 3 8B", "huggingface_hub/meta-llama/Meta-Llama-3-8B"),
-    ("Meta / Llama 3 70B", "huggingface_hub/meta-llama/Meta-Llama-3-70B"),
-    ("Meta / Llama 2 7B", "huggingface_hub/meta-llama/Llama-2-7b-hf"),
-    ("Meta / Llama 2 13B", "huggingface_hub/meta-llama/Llama-2-13b-hf"),
-    ("Meta / Llama 2 70B", "huggingface_hub/meta-llama/Llama-2-70b-hf"),
-    ("Cohere / Command Light", "cohere/command-light"),
-    ("Cohere / Command", "cohere/command"),
-    ("Cohere / Command R", "cohere/command-r"),
-    ("Cohere / Command R+", "cohere/command-r-plus"),
-]
+PROVIDERS = [
+    ("OpenAI", "openai"),
+    ("Anthropic", "anthropic"),
+    ("Cohere", "cohere"),
+    ("Meta", "huggingface_hub/meta"),
+    ("Mistral AI", "mistralai"),
+]
+
+OPENAI_MODELS = [
+    ("GPT-4o", "gpt-4o"),
+    ("GPT-4-Turbo", "gpt-4-turbo"),
+    ("GPT-4", "gpt-4"),
+    ("GPT-3.5-Turbo", "gpt-3.5-turbo"),
+]
+
+ANTHROPIC_MODELS = [
+    ("Claude 3 Opus", "claude-3-opus-20240229"),
+    ("Claude 3 Sonnet", "claude-3-sonnet-20240229"),
+    ("Claude 3 Haiku", "claude-3-haiku-20240307"),
+    ("Claude 2.1", "claude-2.1"),
+    ("Claude 2.0", "claude-2.0"),
+    ("Claude Instant 1.2", "claude-instant-1.2"),
+]
+
+COHERE_MODELS = [
+    ("Command Light", "command-light"),
+    ("Command", "command"),
+    ("Command R", "command-r"),
+    ("Command R+", "command-r-plus"),
+]
+
+META_MODELS = [
+    ("Llama 3 8B", "meta-llama/Meta-Llama-3-8B"),
+    ("Llama 3 70B", "meta-llama/Meta-Llama-3-70B"),
+    ("Llama 2 7B", "meta-llama/Llama-2-7b-hf"),
+    ("Llama 2 13B", "meta-llama/Llama-2-13b-hf"),
+    ("Llama 2 70B", "meta-llama/Llama-2-70b-hf"),
+]
+
+MISTRALAI_MODELS = [
+    ("Mistral 7B", "open-mistral-7b"),
+    ("Mixtral 8x7B", "open-mixtral-8x7b"),
+    ("Mixtral 8x22B", "open-mixtral-8x22b"),
+    ("Tiny", "mistral-tiny-2312"),
+    ("Small", "mistral-small-2402"),
+    ("Medium", "mistral-medium-2312"),
+    ("Large", "mistral-large-2402"),
+]
+
+PROMPTS = [
+    ("Write a Tweet", 50),
+    ("Write an email", 170),
+    ("Write an article summary", 250),
+    ("Small conversation with a chatbot", 400),
+    ("Write a report of 5 pages", 5000),
+    ("Write the code for this app", 10000)
+]
+PROMPTS = [(s + f" ({v} output tokens)", v) for (s, v) in PROMPTS]
+
+CLOSED_SOURCE_MODELS = {
+    "openai/gpt-4o",
+    "openai/gpt-4-turbo",
+    "openai/gpt-4",
+    "openai/gpt-3.5-turbo",
+    "anthropic/claude-3-opus-20240229",
+    "anthropic/claude-3-sonnet-20240229",
+    "anthropic/claude-3-haiku-20240307",
+    "anthropic/claude-2.1",
+    "anthropic/claude-2.0",
+    "anthropic/claude-instant-1.2",
+    "mistralai/mistral-tiny-2312",
+    "mistralai/mistral-small-2402",
+    "mistralai/mistral-medium-2312",
+    "mistralai/mistral-large-2402",
+}
+
+MODELS = [
+    ("OpenAI / GPT-4o", "openai/gpt-4o"),
+    ("OpenAI / GPT-4-Turbo", "openai/gpt-4-turbo"),
+    ("OpenAI / GPT-4", "openai/gpt-4"),
+    ("OpenAI / GPT-3.5-Turbo", "openai/gpt-3.5-turbo"),
+    ("Anthropic / Claude 3 Opus", "anthropic/claude-3-opus-20240229"),
+    ("Anthropic / Claude 3 Sonnet", "anthropic/claude-3-sonnet-20240229"),
+    ("Anthropic / Claude 3 Haiku", "anthropic/claude-3-haiku-20240307"),
+    ("Anthropic / Claude 2.1", "anthropic/claude-2.1"),
+    ("Anthropic / Claude 2.0", "anthropic/claude-2.0"),
+    ("Anthropic / Claude Instant 1.2", "anthropic/claude-instant-1.2"),
+    ("Mistral AI / Mistral 7B", "mistralai/open-mistral-7b"),
+    ("Mistral AI / Mixtral 8x7B", "mistralai/open-mixtral-8x7b"),
+    ("Mistral AI / Mixtral 8x22B", "mistralai/open-mixtral-8x22b"),
+    ("Mistral AI / Tiny", "mistralai/mistral-tiny-2312"),
+    ("Mistral AI / Small", "mistralai/mistral-small-2402"),
+    ("Mistral AI / Medium", "mistralai/mistral-medium-2312"),
+    ("Mistral AI / Large", "mistralai/mistral-large-2402"),
+    ("Meta / Llama 3 8B", "huggingface_hub/meta-llama/Meta-Llama-3-8B"),
+    ("Meta / Llama 3 70B", "huggingface_hub/meta-llama/Meta-Llama-3-70B"),
+    ("Meta / Llama 2 7B", "huggingface_hub/meta-llama/Llama-2-7b-hf"),
+    ("Meta / Llama 2 13B", "huggingface_hub/meta-llama/Llama-2-13b-hf"),
+    ("Meta / Llama 2 70B", "huggingface_hub/meta-llama/Llama-2-70b-hf"),
+    ("Cohere / Command Light", "cohere/command-light"),
+    ("Cohere / Command", "cohere/command"),
+    ("Cohere / Command R", "cohere/command-r"),
+    ("Cohere / Command R+", "cohere/command-r-plus"),
+]
+
+MODEL_REPOSITORY_URL = "https://raw.githubusercontent.com/genai-impact/ecologits/refs/heads/main/ecologits/data/models.json"

src/content.py

@@ -1,286 +1,232 @@
-
-HERO_TEXT = """
-<div align="center">
-  <a href="https://ecologits.ai/">
-    <img style="max-height: 80px" alt="EcoLogits" src="https://raw.githubusercontent.com/genai-impact/ecologits/main/docs/assets/logo_light.png">
-  </a>
-</div>
-
-<h1 align="center">🧮 EcoLogits Calculator</h1>
-<div align="center">
-  <p style="max-width: 500px; text-align: center">
-    <i><b>EcoLogits</b> is a python library that tracks the <b>energy consumption</b> and <b>environmental 
-    footprint</b> of using <b>generative AI</b> models through APIs.</i>
-  </p>
-</div>
-<br>
-
-This tool is developed and maintained by [GenAI Impact](https://genai-impact.org/) non-profit. Learn more about 
-🌱 EcoLogits by reading the documentation on [ecologits.ai](https://ecologits.ai).
- 
-🩷 Support us by giving a ⭐️ on our [GitHub repository](https://github.com/genai-impact/ecologits) and by following our [LinkedIn page](https://www.linkedin.com/company/genai-impact/).
-"""
-
-ABOUT_TEXT = r"""
-## 🎯 Our goal
-
-**The main goal of the EcoLogits Calculator is to raise awareness on the environmental impacts of LLM inference.**
-
-The rapid evolution of generative AI is reshaping numerous industries and aspects of our daily lives. While these 
-advancements offer some benefits, they also **pose substantial environmental challenges that cannot be overlooked**. 
-Plus the issue of AI's environmental footprint has been mainly discussed at training stage but rarely at the inference 
-stage. That is an issue because **inference impacts for LLMs can largely overcome the training impacts when deployed 
-at large scales**.
-
-At **[GenAI Impact](https://genai-impact.org/) we are dedicated to understanding and mitigating the environmental 
-impacts of generative AI** through rigorous research, innovative tools, and community engagement. Especially, in early 
-2024 we have launched an new open-source tool called [EcoLogits](https://github.com/genai-impact/ecologits) that tracks
-the energy consumption and environmental footprint of using generative AI models through APIs.
-
-## 🙋 FAQ 
-
-**How we assess the impacts of closed-source models?**
-
-Environmental impacts are calculated based on model architecture and parameter count. For closed-source models, we 
-lack transparency from providers, so we estimate parameter counts using available information. For GPT models, we 
-based our estimates on leaked GPT-4 architecture and scaled parameters count for GPT-4-Turbo and GPT-4o based on 
-pricing differences. For other proprietary models like Anthropic's Claude, we assume similar impacts for models 
-released around the same time with similar performance on public benchmarks. Please note that these estimates are 
-based on assumptions and may not be exact. Our methods are open-source and transparent so you can always see the 
-hypotheses we use.
-
-**Which generative AI models or providers are supported?**
-
-To see the full list of **generative AI providers** currently supported by EcoLogits, see the following 
-[documentation page](https://ecologits.ai/providers/). As of today we only support LLMs but we plan to add support for 
-embeddings, image generation, multi-modal models and more. If you are interested don't hesitate to 
-[join us](https://genai-impact.org/contact/) and accelerate our work!
-
-**How to reduce AI environmental impacts?**
-
-* Look at **indirect impacts** of your project. Does the finality of your project is impacting negatively the 
-environment?
-* **Be frugal** and question your usage or need of AI
-    * Do you really need AI to solve your problem?
-    * Do you really need GenAI to solve your problem? (you can read this [paper](https://aclanthology.org/2023.emnlp-industry.39.pdf))
-    * Use small and specialized models to solve your problem.
-    * Evaluate before, during and after the development of your project the environmental impacts with tools like 
-    🌱 [EcoLogits](https://github.com/genai-impact/ecologits) or [CodeCarbon](https://github.com/mlco2/codecarbon) 
-    (see [more tools](https://github.com/samuelrince/awesome-green-ai))
-    * Restrict the use case and limit the usage of your tool or feature to the desired purpose.
-* Do NOT buy new GPUs / hardware
-    * Hardware manufacturing for data centers is around 50% of the impact.
-* Use cloud instances that are located in low emissions / high energy efficiency data centers 
-(see [electricitymaps.com](https://app.electricitymaps.com/map))
-* Optimize your models for production
-    * Quantize your models.
-    * Use inference optimization tricks.
-    * Prefer fine-tuning of small and existing models over generalist models.
-
-**What is the difference between **EcoLogits** and [CodeCarbon](https://github.com/mlco2/codecarbon)?**
-
-EcoLogits is focused on estimating the environmental impacts of generative AI (only LLMs for now) used **through API 
-providers (such as OpenAI, Anthropic, Cloud APIs...)** whereas  CodeCarbon is more general tool to measure energy 
-consumption and estimate GHG emissions measurement. If you deploy LLMs locally we encourage you to use CodeCarbon to 
-get real numbers of your energy consumption.
-
-## 🤗 Contributing 
-
-We are eager to get feedback from the community, don't hesitate to engage the discussion with us on this 
-[GitHub thread](https://github.com/genai-impact/ecologits/discussions/45) or message us on 
-[LinkedIn](https://www.linkedin.com/company/genai-impact/).
-
-We also welcome any open-source contributions on 🌱 **[EcoLogits](https://github.com/genai-impact/ecologits)** or on 
-🧮 **EcoLogits Calculator**.
-
-## ⚖️ License
-
-<p xmlns:cc="http://creativecommons.org/ns#" >
-  This work is licensed under 
-  <a href="https://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">
-    CC BY-SA 4.0
-  </a>
-  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1" alt="">
-  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1" alt="">
-  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1" alt="">
-</p>
-
-## 🙌 Acknowledgement
-
-We thank [Data For Good](https://dataforgood.fr/) and [Boavizta](https://boavizta.org/en) for supporting the 
-development of this project. Their contributions of tools, best practices, and expertise in environmental impact 
-assessment have been invaluable.
-
-We also extend our gratitude to the open-source contributions of 🤗 [Hugging Face](huggingface.com) on the LLM-Perf 
-Leaderboard.
-
-## 🤝 Contact
-
-For general question on the project, please use the [GitHub thread](https://github.com/genai-impact/ecologits/discussions/45). 
-Otherwise use our contact form on [genai-impact.org/contact](https://genai-impact.org/contact/).
-"""
-
-
-METHODOLOGY_TEXT = r"""
-## 📖 Methodology
-
-We have developed a methodology to **estimate the energy consumption and environmental impacts for an LLM inference** 
-based on request parameters and hypotheses on the data center location, the hardware used, the model architecture and
-more.
-
-In this section we will only cover the principles of the methodology related to the 🧮 **EcoLogits Calculator**. If 
-you wish to learn more on the environmental impacts modeling of an LLM request checkout the 
-🌱 [EcoLogits documentation page](https://ecologits.ai/methodology/).
-
-### Modeling impacts of an LLM request
-
-The environmental impacts of an LLM inference are split into the **usage impacts** $I_{request}^u$ to account for 
-electricity consumption and the **embodied impacts** $I_{request}^e$ that relates to resource extraction, hardware 
-manufacturing and transportation. In general terms it can be expressed as follow:
-
-$$ I_{request} = I_{request}^u  + I_{request}^e $$
-
-$$ I_{request} = E_{request}*F_{em}+\frac{\Delta T}{\Delta L}*I_{server}^e $$
-
-With,
-
-* $E_{request}$ the estimated energy consumption of the server and its cooling system.
-* $F_{em}$ the electricity mix that depends on the country and time.
-* $\frac{\Delta T}{\Delta L}$ the hardware usage ratio i.e. the computation time over the lifetime of the hardware.
-* $I_{server}^e$ the embodied impacts of the server.
-
-Additionally, to ⚡️ **direct energy consumption** the environmental impacts are expressed in **three dimensions 
-(multi-criteria impacts)** that are:
-
-* 🌍 **Global Warming Potential** (GWP): Potential impact on global warming in kgCO2eq (commonly known as GHG/carbon 
-emissions).
-* 🪨 **Abiotic Depletion Potential for Elements** (ADPe): Impact on the depletion of non-living resources such as 
-minerals or metals in kgSbeq.
-* ⛽️ **Primary Energy** (PE): Total energy consumed from primary sources in MJ.
-
-### Principles, Data and Hypotheses
-
-We use a **bottom-up methodology** to model impacts, meaning that we will estimate the impacts of low-level physical 
-components to then estimate the impacts at software level (in that case an LLM inference). We also rely on **Life 
-Cycle Approach (LCA) proxies and approach** to model both usage and embodied phases with multi-criteria impacts. 
-If you are interested in this approach we recommend you to read the following [Boavizta](https://boavizta.org/) 
-resources.
-
-* [Digital & environment: How to evaluate server manufacturing footprint, beyond greenhouse gas emissions?](https://boavizta.org/en/blog/empreinte-de-la-fabrication-d-un-serveur) 
-* [Boavizta API automated evaluation of environmental impacts of ICT services and equipments](https://boavizta.org/en/blog/boavizta-api-automated-evaluation-of-ict-impacts-on-the-environment)
-* [Boavizta API documentation](https://doc.api.boavizta.org/)
-
-We leverage **open data to estimate the environmental impacts**, here is an exhaustive list of our data providers.
-
-* [LLM-Perf Leaderboard](https://huggingface.co/spaces/optimum/llm-perf-leaderboard) to estimate GPU energy consumption 
-and latency based on the model architecture and number of output tokens.
-* [Boavizta API](https://github.com/Boavizta/boaviztapi) to estimate server embodied impacts and base energy 
-consumption.
-* [ADEME Base Empreinte®](https://base-empreinte.ademe.fr/) for electricity mix impacts per country.
-
-Finally here are the **main hypotheses** we have made to compute the impacts.
-
-* ⚠️ **We *"guesstimate"* the model architecture of proprietary LLMs when not disclosed by the provider.** 
-* Production setup: quantized models running on data center grade servers and GPUs such as A100.
-* Electricity mix does not depend on time (help us enhance EcoLogits and work on this [issue](https://github.com/genai-impact/ecologits/issues/42))
-* Ignore the following impacts: unused cloud resources, data center building, network and end-user devices... (for now)
-
-## Equivalents
-
-We have integrated impact equivalents to help people better understand the impacts and have reference points for 
-standard use cases and everyday activities.
-
-### Request impacts
-
-These equivalents are computed based on the request impacts only.
-
-#### 🚶‍♂️‍➡️ Walking or 🏃‍♂️‍➡️ running distance
-
-We compare the ⚡️ direct energy consumption with the energy consumption of someone 🚶‍♂️‍➡️ walking or 🏃‍♂️‍➡️ running. 
-From [runningtools.com](https://www.runningtools.com/energyusage.htm) we consider the following energy values per 
-physical activity (for someone weighing 70kg):
-
-* 🚶‍♂️‍➡️ walking: $ 196\ kJ/km $ (speed of $ 3\ km/h $)
-* 🏃‍♂️‍➡️ running: $ 294\ kJ/km $ (speed of $ 10\ km/h $)
-
-We divide the request energy consumption by these values to compute the distance traveled. 
-
-#### 🔋 Electric Vehicle distance
-
-We compare the ⚡️ direct energy consumption with the energy consumer by a EV car. From 
-[selectra.info](https://selectra.info/energie/actualites/insolite/consommation-vehicules-electriques-france-2040) or 
-[tesla.com](https://www.tesla.com/fr_fr/support/power-consumption) we consider an average value of energy consumed per 
-kilometer of: $ 0.17\ kWh/km $.
-
-We divide the request energy consumption by this value to compute the distance driven by an EV. 
-
-#### ⏯️ Streaming time
-
-We compare the 🌍 GHG emissions of the request and of streaming a video. From 
-[impactco2.fr](https://impactco2.fr/outils/comparateur?value=1&comparisons=streamingvideo), we consider that 
-$ 1\ kgCO2eq $ is equivalent to $ 15.6\ h $ of streaming.
-
-We multiply that value by the GHG emissions of the request to get an equivalent in hours of video streaming.
-
-### Scaled impacts
-
-These equivalents are computed based on the request impacts scaled to a worldwide adoption use case. We imply that the
-same request is done 1% of the planet everyday for 1 year, and then compute impact equivalents.
-
-$$
-I_{scaled} = I_{request} * [1 \\% \ \text{of}\ 8B\ \text{people on earth}] * 365\ \text{days}
-$$
-
-#### Number of 💨 wind turbines or ☢️ nuclear plants
-
-We compare the ⚡️ direct energy consumption (scaled) by the energy production of wind turbines and nuclear power 
-plants. From [ecologie.gouv.fr](https://www.ecologie.gouv.fr/eolien-terrestre) we consider that a $ 2\ MW $ wind 
-turbine produces $ 4.2\ GWh $ a year. And from [edf.fr](https://www.edf.fr/groupe-edf/espaces-dedies/jeunes-enseignants/pour-les-jeunes/lenergie-de-a-a-z/produire-de-lelectricite/le-nucleaire-en-chiffres) 
-we learn that a $ 900\ MW $ nuclear power plant produces $ 6\ TWh $ a year.
-
-We divide the scaled energy consumption by these values to get the number of wind turbines or nuclear power plants 
-needed.
-
-#### Multiplier of 🇮🇪 Ireland electricity consumption
-
-We compare the ⚡️ direct energy consumption (scaled) by the electricity consumption of Ireland per year. From 
-[wikipedia.org](https://en.wikipedia.org/wiki/List_of_countries_by_electricity_consumption) we consider the Ireland 
-electricity consumption to be $ 33\ TWh $ a year for a population of 5M.
-
-We divide the scaled energy consumption by this value to get the equivalent number of "Ireland countries".
-
-#### Number of ✈️ Paris ↔ New York City flights
-
-We compare the 🌍 GHG emissions (scaled) of the request and of a return flight Paris ↔ New York City. From 
-[impactco2.fr](https://impactco2.fr/outils/comparateur?value=1&comparisons=&equivalent=avion-pny) we consider that a 
-return flight Paris → New York City → Paris for one passenger emits $ 1,770\ kgCO2eq $ and we consider an overall 
-average load of 100 passengers per flight.
-
-We divide the scaled GHG emissions by this value to get the equivalent number of return flights.
-
-**If you are motivated to help us test and enhance this methodology 
-[contact us](https://genai-impact.org/contact/)!** 💪
-"""
-
-CITATION_LABEL = "BibTeX citation for EcoLogits Calculator and the EcoLogits library:"
-CITATION_TEXT = r"""@misc{ecologits-calculator,
-  author={Samuel Rincé, Adrien Banse and Valentin Defour},
-  title={EcoLogits Calculator},
-  year={2024},
-  howpublished= {\url{https://huggingface.co/spaces/genai-impact/ecologits-calculator}},
-}
-@software{ecologits,
-  author = {Samuel Rincé, Adrien Banse, Vinh Nguyen and Luc Berton},
-  publisher = {GenAI Impact},
-  title = {EcoLogits: track the energy consumption and environmental footprint of using generative AI models through APIs.},
-}"""
-
-LICENCE_TEXT = """<p xmlns:cc="http://creativecommons.org/ns#" >
-  This work is licensed under 
-  <a href="https://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">
-    CC BY-SA 4.0
-  </a>
-  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1" alt="">
-  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1" alt="">
-  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1" alt="">
-</p>"""
+HERO_TEXT = """
+<div align="center">
+  <a href="https://ecologits.ai/">
+    <img style="max-height: 80px" alt="EcoLogits" src="https://raw.githubusercontent.com/genai-impact/ecologits/main/docs/assets/logo_light.png">
+  </a>
+</div>
+<h1 align="center">🧮 EcoLogits Calculator</h1>
+<div align="center">
+  <p style="max-width: 500px; text-align: center">
+    <i><b>EcoLogits</b> is a python library that tracks the <b>energy consumption</b> and <b>environmental 
+    footprint</b> of using <b>generative AI</b> models through APIs.</i>
+  </p>
+</div>
+<br>
+"""
+
+INTRO_TEXT = """
+This tool is developed and maintained by [GenAI Impact](https://genai-impact.org/) non-profit. Learn more about 
+🌱 EcoLogits by reading the documentation on [ecologits.ai](https://ecologits.ai).
+ 
+🩷 Support us by giving a ⭐️ on our [GitHub repository](https://github.com/genai-impact/ecologits) and by following our [LinkedIn page](https://www.linkedin.com/company/genai-impact/).
+"""
+
+WARNING_CLOSED_SOURCE = """
+⚠️ The model architecture has not been publicly released, expect lower precision of estimations.
+"""
+
+WARNING_MULTI_MODAL = """
+⚠️ The model architecture is multimodal, expect lower precision of estimations.
+"""
+
+WARNING_BOTH = """
+⚠️ The model architecture has not been publicly released and is multimodal, expect lower precision of estimations.
+"""
+
+ABOUT_TEXT = r"""
+## 🎯 Our goal
+**The main goal of the EcoLogits Calculator is to raise awareness on the environmental impacts of LLM inference.**
+The rapid evolution of generative AI is reshaping numerous industries and aspects of our daily lives. While these 
+advancements offer some benefits, they also **pose substantial environmental challenges that cannot be overlooked**. 
+Plus the issue of AI's environmental footprint has been mainly discussed at training stage but rarely at the inference 
+stage. That is an issue because **inference impacts for LLMs can largely overcome the training impacts when deployed 
+at large scales**.
+At **[GenAI Impact](https://genai-impact.org/) we are dedicated to understanding and mitigating the environmental 
+impacts of generative AI** through rigorous research, innovative tools, and community engagement. Especially, in early 
+2024 we have launched an new open-source tool called [EcoLogits](https://github.com/genai-impact/ecologits) that tracks
+the energy consumption and environmental footprint of using generative AI models through APIs.
+## 🙋 FAQ 
+**How we assess the impacts of closed-source models?**
+Environmental impacts are calculated based on model architecture and parameter count. For closed-source models, we 
+lack transparency from providers, so we estimate parameter counts using available information. For GPT models, we 
+based our estimates on leaked GPT-4 architecture and scaled parameters count for GPT-4-Turbo and GPT-4o based on 
+pricing differences. For other proprietary models like Anthropic's Claude, we assume similar impacts for models 
+released around the same time with similar performance on public benchmarks. Please note that these estimates are 
+based on assumptions and may not be exact. Our methods are open-source and transparent so you can always see the 
+hypotheses we use.
+**Which generative AI models or providers are supported?**
+To see the full list of **generative AI providers** currently supported by EcoLogits, see the following 
+[documentation page](https://ecologits.ai/providers/). As of today we only support LLMs but we plan to add support for 
+embeddings, image generation, multi-modal models and more. If you are interested don't hesitate to 
+[join us](https://genai-impact.org/contact/) and accelerate our work!
+**How to reduce AI environmental impacts?**
+* Look at **indirect impacts** of your project. Does the finality of your project is impacting negatively the 
+environment?
+* **Be frugal** and question your usage or need of AI
+    * Do you really need AI to solve your problem?
+    * Do you really need GenAI to solve your problem? (you can read this [paper](https://aclanthology.org/2023.emnlp-industry.39.pdf))
+    * Use small and specialized models to solve your problem.
+    * Evaluate before, during and after the development of your project the environmental impacts with tools like 
+    🌱 [EcoLogits](https://github.com/genai-impact/ecologits) or [CodeCarbon](https://github.com/mlco2/codecarbon) 
+    (see [more tools](https://github.com/samuelrince/awesome-green-ai))
+    * Restrict the use case and limit the usage of your tool or feature to the desired purpose.
+* Do NOT buy new GPUs / hardware
+    * Hardware manufacturing for data centers is around 50% of the impact.
+* Use cloud instances that are located in low emissions / high energy efficiency data centers 
+(see [electricitymaps.com](https://app.electricitymaps.com/map))
+* Optimize your models for production
+    * Quantize your models.
+    * Use inference optimization tricks.
+    * Prefer fine-tuning of small and existing models over generalist models.
+**What is the difference between **EcoLogits** and [CodeCarbon](https://github.com/mlco2/codecarbon)?**
+EcoLogits is focused on estimating the environmental impacts of generative AI (only LLMs for now) used **through API 
+providers (such as OpenAI, Anthropic, Cloud APIs...)** whereas  CodeCarbon is more general tool to measure energy 
+consumption and estimate GHG emissions measurement. If you deploy LLMs locally we encourage you to use CodeCarbon to 
+get real numbers of your energy consumption.
+## 🤗 Contributing 
+We are eager to get feedback from the community, don't hesitate to engage the discussion with us on this 
+[GitHub thread](https://github.com/genai-impact/ecologits/discussions/45) or message us on 
+[LinkedIn](https://www.linkedin.com/company/genai-impact/).
+We also welcome any open-source contributions on 🌱 **[EcoLogits](https://github.com/genai-impact/ecologits)** or on 
+🧮 **EcoLogits Calculator**.
+## ⚖️ License
+<p xmlns:cc="http://creativecommons.org/ns#" >
+  This work is licensed under 
+  <a href="https://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">
+    CC BY-SA 4.0
+  </a>
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1" alt="">
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1" alt="">
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1" alt="">
+</p>
+## 🙌 Acknowledgement
+We thank [Data For Good](https://dataforgood.fr/) and [Boavizta](https://boavizta.org/en) for supporting the 
+development of this project. Their contributions of tools, best practices, and expertise in environmental impact 
+assessment have been invaluable.
+We also extend our gratitude to the open-source contributions of 🤗 [Hugging Face](huggingface.com) on the LLM-Perf 
+Leaderboard.
+## 🤝 Contact
+For general question on the project, please use the [GitHub thread](https://github.com/genai-impact/ecologits/discussions/45). 
+Otherwise use our contact form on [genai-impact.org/contact](https://genai-impact.org/contact/).
+"""
+
+
+METHODOLOGY_TEXT = r"""
+## 📖 Methodology
+We have developed a methodology to **estimate the energy consumption and environmental impacts for an LLM inference** 
+based on request parameters and hypotheses on the data center location, the hardware used, the model architecture and
+more.
+In this section we will only cover the principles of the methodology related to the 🧮 **EcoLogits Calculator**. If 
+you wish to learn more on the environmental impacts modeling of an LLM request checkout the 
+🌱 [EcoLogits documentation page](https://ecologits.ai/methodology/).
+### Modeling impacts of an LLM request
+The environmental impacts of an LLM inference are split into the **usage impacts** $I_{request}^u$ to account for 
+electricity consumption and the **embodied impacts** $I_{request}^e$ that relates to resource extraction, hardware 
+manufacturing and transportation. In general terms it can be expressed as follow:
+$$ I_{request} = I_{request}^u  + I_{request}^e $$
+$$ I_{request} = E_{request}*F_{em}+\frac{\Delta T}{\Delta L}*I_{server}^e $$
+With,
+* $E_{request}$ the estimated energy consumption of the server and its cooling system.
+* $F_{em}$ the electricity mix that depends on the country and time.
+* $\frac{\Delta T}{\Delta L}$ the hardware usage ratio i.e. the computation time over the lifetime of the hardware.
+* $I_{server}^e$ the embodied impacts of the server.
+Additionally, to ⚡️ **direct energy consumption** the environmental impacts are expressed in **three dimensions 
+(multi-criteria impacts)** that are:
+* 🌍 **Global Warming Potential** (GWP): Potential impact on global warming in kgCO2eq (commonly known as GHG/carbon 
+emissions).
+* 🪨 **Abiotic Depletion Potential for Elements** (ADPe): Impact on the depletion of non-living resources such as 
+minerals or metals in kgSbeq.
+* ⛽️ **Primary Energy** (PE): Total energy consumed from primary sources in MJ.
+### Principles, Data and Hypotheses
+We use a **bottom-up methodology** to model impacts, meaning that we will estimate the impacts of low-level physical 
+components to then estimate the impacts at software level (in that case an LLM inference). We also rely on **Life 
+Cycle Approach (LCA) proxies and approach** to model both usage and embodied phases with multi-criteria impacts. 
+If you are interested in this approach we recommend you to read the following [Boavizta](https://boavizta.org/) 
+resources.
+* [Digital & environment: How to evaluate server manufacturing footprint, beyond greenhouse gas emissions?](https://boavizta.org/en/blog/empreinte-de-la-fabrication-d-un-serveur) 
+* [Boavizta API automated evaluation of environmental impacts of ICT services and equipments](https://boavizta.org/en/blog/boavizta-api-automated-evaluation-of-ict-impacts-on-the-environment)
+* [Boavizta API documentation](https://doc.api.boavizta.org/)
+We leverage **open data to estimate the environmental impacts**, here is an exhaustive list of our data providers.
+* [LLM-Perf Leaderboard](https://huggingface.co/spaces/optimum/llm-perf-leaderboard) to estimate GPU energy consumption 
+and latency based on the model architecture and number of output tokens.
+* [Boavizta API](https://github.com/Boavizta/boaviztapi) to estimate server embodied impacts and base energy 
+consumption.
+* [ADEME Base Empreinte®](https://base-empreinte.ademe.fr/) for electricity mix impacts per country.
+Finally here are the **main hypotheses** we have made to compute the impacts.
+* ⚠️ **We *"guesstimate"* the model architecture of proprietary LLMs when not disclosed by the provider.** 
+* Production setup: quantized models running on data center grade servers and GPUs such as A100.
+* Electricity mix does not depend on time (help us enhance EcoLogits and work on this [issue](https://github.com/genai-impact/ecologits/issues/42))
+* Ignore the following impacts: unused cloud resources, data center building, network and end-user devices... (for now)
+## Equivalents
+We have integrated impact equivalents to help people better understand the impacts and have reference points for 
+standard use cases and everyday activities.
+### Request impacts
+These equivalents are computed based on the request impacts only.
+#### 🚶‍♂️‍➡️ Walking or 🏃‍♂️‍➡️ running distance
+We compare the ⚡️ direct energy consumption with the energy consumption of someone 🚶‍♂️‍➡️ walking or 🏃‍♂️‍➡️ running. 
+From [runningtools.com](https://www.runningtools.com/energyusage.htm) we consider the following energy values per 
+physical activity (for someone weighing 70kg):
+* 🚶‍♂️‍➡️ walking: $ 196\ kJ/km $ (speed of $ 3\ km/h $)
+* 🏃‍♂️‍➡️ running: $ 294\ kJ/km $ (speed of $ 10\ km/h $)
+We divide the request energy consumption by these values to compute the distance traveled. 
+#### 🔋 Electric Vehicle distance
+We compare the ⚡️ direct energy consumption with the energy consumer by a EV car. From 
+[selectra.info](https://selectra.info/energie/actualites/insolite/consommation-vehicules-electriques-france-2040) or 
+[tesla.com](https://www.tesla.com/fr_fr/support/power-consumption) we consider an average value of energy consumed per 
+kilometer of: $ 0.17\ kWh/km $.
+We divide the request energy consumption by this value to compute the distance driven by an EV. 
+#### ⏯️ Streaming time
+We compare the 🌍 GHG emissions of the request and of streaming a video. From 
+[impactco2.fr](https://impactco2.fr/outils/comparateur?value=1&comparisons=streamingvideo), we consider that 
+$ 1\ kgCO2eq $ is equivalent to $ 15.6\ h $ of streaming.
+We multiply that value by the GHG emissions of the request to get an equivalent in hours of video streaming.
+### Scaled impacts
+These equivalents are computed based on the request impacts scaled to a worldwide adoption use case. We imply that the
+same request is done 1% of the planet everyday for 1 year, and then compute impact equivalents.
+$$
+I_{scaled} = I_{request} * [1 \\% \ \text{of}\ 8B\ \text{people on earth}] * 365\ \text{days}
+$$
+#### Number of 💨 wind turbines or ☢️ nuclear plants
+We compare the ⚡️ direct energy consumption (scaled) by the energy production of wind turbines and nuclear power 
+plants. From [ecologie.gouv.fr](https://www.ecologie.gouv.fr/eolien-terrestre) we consider that a $ 2\ MW $ wind 
+turbine produces $ 4.2\ GWh $ a year. And from [edf.fr](https://www.edf.fr/groupe-edf/espaces-dedies/jeunes-enseignants/pour-les-jeunes/lenergie-de-a-a-z/produire-de-lelectricite/le-nucleaire-en-chiffres) 
+we learn that a $ 900\ MW $ nuclear power plant produces $ 6\ TWh $ a year.
+We divide the scaled energy consumption by these values to get the number of wind turbines or nuclear power plants 
+needed.
+#### Multiplier of 🇮🇪 Ireland electricity consumption
+We compare the ⚡️ direct energy consumption (scaled) by the electricity consumption of Ireland per year. From 
+[wikipedia.org](https://en.wikipedia.org/wiki/List_of_countries_by_electricity_consumption) we consider the Ireland 
+electricity consumption to be $ 33\ TWh $ a year for a population of 5M.
+We divide the scaled energy consumption by this value to get the equivalent number of "Ireland countries".
+#### Number of ✈️ Paris ↔ New York City flights
+We compare the 🌍 GHG emissions (scaled) of the request and of a return flight Paris ↔ New York City. From 
+[impactco2.fr](https://impactco2.fr/outils/comparateur?value=1&comparisons=&equivalent=avion-pny) we consider that a 
+return flight Paris → New York City → Paris for one passenger emits $ 1,770\ kgCO2eq $ and we consider an overall 
+average load of 100 passengers per flight.
+We divide the scaled GHG emissions by this value to get the equivalent number of return flights.
+
+#### If you are motivated to help us test and enhance this methodology [contact us](https://genai-impact.org/contact/)! 💪
+"""
+
+CITATION_LABEL = "BibTeX citation for EcoLogits Calculator and the EcoLogits library:"
+CITATION_TEXT = r"""@misc{ecologits-calculator,
+  author={Samuel Rincé, Adrien Banse and Valentin Defour},
+  title={EcoLogits Calculator},
+  year={2025},
+  howpublished= {\url{https://huggingface.co/spaces/genai-impact/ecologits-calculator}},
+}
+@software{ecologits,
+  author = {Samuel Rincé, Adrien Banse, Vinh Nguyen and Luc Berton},
+  publisher = {GenAI Impact},
+  title = {EcoLogits: track the energy consumption and environmental footprint of using generative AI models through APIs.},
+}"""
+
+LICENCE_TEXT = """<p xmlns:cc="http://creativecommons.org/ns#" >
+  This work is licensed under 
+  <a href="https://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">
+    CC BY-SA 4.0
+  </a>
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1" alt="">
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1" alt="">
+  <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1" alt="">
+</p>"""

src/data/electricity_mix.csv

@@ -0,0 +1,4 @@
+name,unit,source,WOR,EEE,ZWE,ZMB,ZAF,YEM,VNM,VEN,UZB,URY,USA,UKR,TZA,TWN,TTO,TUR,TUN,TKM,TJK,THA,TGO,SYR,SLV,SEN,SVK,SVN,SGP,SWE,SDN,SAU,RUS,SCG,ROU,QAT,PRY,PRT,POL,PAK,PHL,PER,PAN,OMN,NZL,NPL,NOR,NLD,NIC,NGA,NAM,MOZ,MYS,MEX,MLT,MNG,MMR,MKD,MDA,MAR,LBY,LVA,LUX,LTU,LKA,LBN,KAZ,KWT,KOR,PRK,KHM,KGZ,KEN,JPN,JOR,JAM,ITA,ISL,IRN,IRQ,IND,ISR,IRL,IDN,HUN,HTI,HRV,HND,HKG,GTM,GRC,GIB,GHA,GEO,GBR,GAB,FRA,FIN,ETH,ESP,ERI,EGY,EST,ECU,DZA,DOM,DNK,DEU,CZE,CYP,CUB,CRI,COL,CHN,CMR,CHL,CIV,CHE,COG,COD,CAN,BLR,BWA,BRA,BOL,BRN,BEN,BHR,BGR,BEL,BGD,BIH,AZE,AUS,AUT,ARG,AGO,ANT,ARM,ALB,ARE
+adpe,kg éq. Sb,ADEME Base IMPACTS ®,0.0000000737708,0.0000000642317,0.000000109502,0.000000162193,0.0000000862445,0.0000000163908,0.0000000945573,0.000000112811,0.000000103681,0.000000104586,0.0000000985548,0.0000000647907,0.000000132261,0.0000000578088,0.000000064552,0.0000000749765,0.0000000177021,0.000000131822,0.000000152621,0.0000000569593,0.000000134255,0.0000000268396,0.0000000472135,0.0000000470662,0.0000000606109,0.0000000992283,0.0000000198459,0.0000000777062,0.0000000955701,0.0000000134206,0.0000000960312,0.000000132772,0.0000000981761,0.00000001324,0.000000149181,0.0000000341863,0.000000101946,0.0000000842952,0.0000000595304,0.0000000952688,0.0000000790553,0.0000000374073,0.0000000720474,0.000000238273,0.000000127486,0.0000000329318,0.0000000414983,0.0000000621,0.000000128285,0.000000148382,0.000000044938,0.0000000578358,0.000000049475,0.000000176361,0.000000152699,0.000000119873,0.000000110674,0.0000000641089,0.0000000206592,0.000000153757,0.000000105692,0.0000000294596,0.0000000986932,0.0000000182134,0.000000135386,0.0000000141168,0.0000000518017,0.000000117457,0.0000000319202,0.000000181827,0.0000000958533,0.0000000596578,0.0000000147031,0.0000000196047,0.00000005439,0.0000000781905,0.0000000220304,0.0000000404306,0.000000100099,0.0000000610194,0.0000000219257,0.0000000610451,0.0000000644587,0.0000000937057,0.000000153989,0.0000000649373,0.0000000816213,0.0000000803251,0.0000000691645,0.0000000286211,0.000000156003,0.000000137999,0.0000000370973,0.000000113843,0.0000000485798,0.0000000805114,0.000000174161,0.0000000518326,0.0000000512406,0.000000033925,0.0000000990171,0.000000127168,0.0000000216438,0.0000000429285,0.0000000157411,0.0000000878733,0.0000000817565,0.0000000448771,0.0000000299542,0.0000000863908,0.000000122031,0.0000000851552,0.000000146313,0.000000105851,0.0000000949004,0.000000100467,0.000000265575,0.000000174647,0.0000000993179,0.0000000840478,0.0000000866014,0.00000010962,0.0000000969793,0.0000000185641,0.0000000239702,0.0000000135014,0.0000000823611,0.0000000337201,0.0000000394158,0.000000148007,0.000000092567,0.0000000790846,0.000000141124,0.0000000768612,0.000000124074,0.0000000449103,0.0000000854245,0.000000229556,0.0000000141548
+pe,MJ,ADPf / (1-%renewable_energy),9.988,12.873,16.122,1.798,11.732,16.250,11.238,15.163,17.367,107.570,11.358,12.936,9.305,11.439,14.289,16.150,12.902,23.300,19.165,10.414,21.978,16.989,13.012,14.516,11.680,12.146,10.477,11.026,29.629,14.058,13.200,14.242,15.585,11.916,0.020,14.153,13.178,16.175,11.120,8.211,16.364,22.306,24.731,0.396,4.952,8.511,24.696,11.279,468.150,0.206,12.268,11.775,19.374,15.114,14.132,19.120,18.429,11.702,19.116,8.249,10.128,21.043,12.116,12.341,13.260,12.753,10.199,32.793,34.655,15.380,68.996,10.718,13.677,14.799,12.656,0.013,15.022,20.372,20.363,10.023,10.706,11.603,11.784,20.167,18.548,15.762,,14.340,14.487,,10.097,10.425,13.579,28.341,11.289,11.275,36.133,12.090,13.289,10.195,16.334,20.908,16.376,12.412,16.824,16.260,12.517,13.118,17.317,45.996,7.312,14.119,10.807,11.348,14.783,11.782,34.147,0.097,11.987,13.194,19.642,9.031,11.587,15.689,14.337,14.036,14.375,10.776,12.935,21.705,12.831,16.908,11.036,10.049,16.972,,13.380,0.201,19.032
+gwp,kg éq. CO2,ADEME Base IMPACTS ®,0.590478,0.509427,0.842811,0.0141304,1.17562,1.06777,0.555572,0.497373,0.81178,0.296953,0.67978,0.646745,0.475635,0.845351,0.933059,0.706988,0.80722,1.38296,0.0426743,0.646174,0.545455,1.08778,0.473128,1.1195,0.309341,0.498523,0.655825,0.0464664,1.12472,0.913677,0.66131,1.07808,0.664245,0.722125,0.241601,0.571172,1.15075,0.748727,0.761317,0.284364,0.53403,1.41292,0.293397,0.0841323,0.023754,0.544803,0.941626,0.693123,0.357253,0.00880732,0.832206,0.739214,1.31149,1.47192,0.48193,1.24074,1.04213,0.933694,1.35361,0.234273,0.490016,0.154229,0.709185,0.883627,1.128,0.885084,0.599585,0.797361,1.41054,0.156039,0.589603,0.540891,0.781372,1.07345,0.621329,0.0194609,0.930385,1.48728,1.58299,0.901842,0.648118,0.875394,0.541558,1.3858,0.535759,0.692837,0.95888,0.645801,1.13127,0.977477,0.540126,0.132046,0.602137,0.732511,0.0813225,0.322068,0.251299,0.467803,1.13153,0.587775,1.51492,0.627714,1.02318,0.909252,0.633534,0.657374,0.799077,0.978041,1.28325,0.224471,0.214014,1.05738,0.456622,0.574415,0.928583,0.0448568,0.904053,0.0613102,0.238191,0.795697,2.12572,0.278647,0.585131,0.997577,1.19128,0.804567,0.827087,0.256537,0.795168,1.56025,0.726126,1.12326,0.245573,0.476811,0.412014,1.13161,0.252002,0.0207635,1.09088

src/electricity_mix.py

@@ -1,155 +1,175 @@
-from csv import DictReader
-
-PATH = "src/electricity_mix.csv"
-COUNTRY_CODES = [
-    ("🌎 World", "WOR"),
-    ("🇪🇺 Europe", "EEE"),
-    ("🇿🇼 Zimbabwe", "ZWE"),
-    ("🇿🇲 Zambia", "ZMB"),
-    ("🇿🇦 South Africa", "ZAF"),
-    ("🇾🇪 Yemen", "YEM"),
-    ("🇻🇳 Vietnam", "VNM"),
-    ("🇻🇪 Venezuela", "VEN"),
-    ("🇺🇿 Uzbekistan", "UZB"),
-    ("🇺🇾 Uruguay", "URY"),
-    ("🇺🇸 United States", "USA"),
-    ("🇺🇦 Ukraine", "UKR"),
-    ("🇹🇿 Tanzania", "TZA"),
-    ("🇹🇼 Taiwan", "TWN"),
-    ("🇹🇹 Trinidad and Tobago", "TTO"),
-    ("🇹🇷 Turkey", "TUR"),
-    ("🇹🇳 Tunisia", "TUN"),
-    ("🇹🇲 Turkmenistan", "TKM"),
-    ("🇹🇯 Tajikistan", "TJK"),
-    ("🇹🇭 Thailand", "THA"),
-    ("🇹🇬 Togo", "TGO"),
-    ("🇸🇾 Syrian Arab Republic", "SYR"),
-    ("🇸🇻 El Salvador", "SLV"),
-    ("🇸🇳 Senegal", "SEN"),
-    ("🇸🇰 Slovak Republic", "SVK"),
-    ("🇸🇮 Slovenia", "SVN"),
-    ("🇸🇬 Singapore", "SGP"),
-    ("🇸🇪 Sweden", "SWE"),
-    ("🇸🇩 Sudan", "SDN"),
-    ("🇸🇦 Saudi Arabia", "SAU"),
-    ("🇷🇺 Russian Federation", "RUS"),
-    ("🇷🇸 Serbia and Montenegro", "SCG"),
-    ("🇷🇴 Romania", "ROU"),
-    ("🇶🇦 Qatar", "QAT"),
-    ("🇵🇾 Paraguay", "PRY"),
-    ("🇵🇹 Portugal", "PRT"),
-    ("🇵🇱 Poland", "POL"),
-    ("🇵🇰 Pakistan", "PAK"),
-    ("🇵🇭 Philippines", "PHL"),
-    ("🇵🇪 Peru", "PER"),
-    ("🇵🇦 Panama", "PAN"),
-    ("🇴🇲 Oman", "OMN"),
-    ("🇳🇿 New Zealand", "NZL"),
-    ("🇳🇵 Nepal", "NPL"),
-    ("🇳🇴 Norway", "NOR"),
-    ("🇳🇱 Netherlands", "NLD"),
-    ("🇳🇮 Nicaragua", "NIC"),
-    ("🇳🇬 Nigeria", "NGA"),
-    ("🇳🇦 Namibia", "NAM"),
-    ("🇲🇿 Mozambique", "MOZ"),
-    ("🇲🇾 Malaysia", "MYS"),
-    ("🇲🇽 Mexico", "MEX"),
-    ("🇲🇹 Malta", "MLT"),
-    ("🇲🇳 Mongolia", "MNG"),
-    ("🇲🇲 Myanmar", "MMR"),
-    ("🇲🇰 North Macedonia", "MKD"),
-    ("🇲🇩 Moldova", "MDA"),
-    ("🇲🇦 Morocco", "MAR"),
-    ("🇱🇾 Libya", "LBY"),
-    ("🇱🇻 Latvia", "LVA"),
-    ("🇱🇺 Luxembourg", "LUX"),
-    ("🇱🇹 Lithuania", "LTU"),
-    ("🇱🇰 Sri Lanka", "LKA"),
-    ("🇱🇧 Lebanon", "LBN"),
-    ("🇰🇿 Kazakhstan", "KAZ"),
-    ("🇰🇼 Kuwait", "KWT"),
-    ("🇰🇷 South Korea", "KOR"),
-    ("🇰🇵 North Korea", "PRK"),
-    ("🇰🇭 Cambodia", "KHM"),
-    ("🇰🇬 Kyrgyz Republic", "KGZ"),
-    ("🇰🇪 Kenya", "KEN"),
-    ("🇯🇵 Japan", "JPN"),
-    ("🇯🇴 Jordan", "JOR"),
-    ("🇯🇲 Jamaica", "JAM"),
-    ("🇮🇹 Italy", "ITA"),
-    ("🇮🇸 Iceland", "ISL"),
-    ("🇮🇷 Iran", "IRN"),
-    ("🇮🇶 Iraq", "IRQ"),
-    ("🇮🇳 India", "IND"),
-    ("🇮🇱 Israel", "ISR"),
-    ("🇮🇪 Ireland", "IRL"),
-    ("🇮🇩 Indonesia", "IDN"),
-    ("🇭🇺 Hungary", "HUN"),
-    ("🇭🇹 Haiti", "HTI"),
-    ("🇭🇷 Croatia", "HRV"),
-    ("🇭🇳 Honduras", "HND"),
-    ("🇭🇰 Hong Kong", "HKG"),
-    ("🇬🇹 Guatemala", "GTM"),
-    ("🇬🇷 Greece", "GRC"),
-    ("🇬🇮 Gibraltar", "GIB"),
-    ("🇬🇭 Ghana", "GHA"),
-    ("🇬🇪 Georgia", "GEO"),
-    ("🇬🇧 United Kingdom", "GBR"),
-    ("🇬🇦 Gabon", "GAB"),
-    ("🇫🇷 France", "FRA"),
-    ("🇫🇮 Finland", "FIN"),
-    ("🇪🇹 Ethiopia", "ETH"),
-    ("🇪🇸 Spain", "ESP"),
-    ("🇪🇷 Eritrea", "ERI"),
-    ("🇪🇬 Egypt", "EGY"),
-    ("🇪🇪 Estonia", "EST"),
-    ("🇪🇨 Ecuador", "ECU"),
-    ("🇩🇿 Algeria", "DZA"),
-    ("🇩🇴 Dominican Republic", "DOM"),
-    ("🇩🇰 Denmark", "DNK"),
-    ("🇩🇪 Germany", "DEU"),
-    ("🇨🇿 Czech Republic", "CZE"),
-    ("🇨🇾 Cyprus", "CYP"),
-    ("🇨🇺 Cuba", "CUB"),
-    ("🇨🇷 Costa Rica", "CRI"),
-    ("🇨🇴 Colombia", "COL"),
-    ("🇨🇳 China", "CHN"),
-    ("🇨🇲 Cameroon", "CMR"),
-    ("🇨🇱 Chile", "CHL"),
-    ("🇨🇮 Cote d'Ivoire", "CIV"),
-    ("🇨🇭 Switzerland", "CHE"),
-    ("🇨🇬 Congo", "COG"),
-    ("🇨🇩 Democratic Republic of the Congo", "COD"),
-    ("🇨🇦 Canada", "CAN"),
-    ("🇧🇾 Belarus", "BLR"),
-    ("🇧🇼 Botswana", "BWA"),
-    ("🇧🇷 Brazil", "BRA"),
-    ("🇧🇴 Bolivia", "BOL"),
-    ("🇧🇳 Brunei", "BRN"),
-    ("🇧🇯 Benin", "BEN"),
-    ("🇧🇭 Bahrain", "BHR"),
-    ("🇧🇬 Bulgaria", "BGR"),
-    ("🇧🇪 Belgium", "BEL"),
-    ("🇧🇩 Bangladesh", "BGD"),
-    ("🇧🇦 Bosnia and Herzegovina", "BIH"),
-    ("🇦🇿 Azerbaijan", "AZE"),
-    ("🇦🇺 Australia", "AUS"),
-    ("🇦🇹 Austria", "AUT"),
-    ("🇦🇷 Argentina", "ARG"),
-    ("🇦🇴 Angola", "AGO"),
-    ("🇦 Netherlands Antilles", "ANT"),
-    ("🇦🇲 Armenia", "ARM"),
-    ("🇦🇱 Albania", "ALB"),
-    ("🇦🇪 United Arab Emirates", "ARE")
-]
-
-
-def find_electricity_mix(code: str):
-    # TODO: Maybe more optimal to construct database at the beginning of the app
-    #       in the same fashion as find_model
-    res = []
-    with open(PATH) as fd:
-        csv = DictReader(fd)
-        for row in csv:
-            res += [float(row[code])]
-    return res
+from csv import DictReader
+import pandas as pd
+
+PATH = "src/data/electricity_mix.csv"
+
+COUNTRY_CODES = [
+    ("🌎 World", "WOR"),
+    ("🇪🇺 Europe", "EEE"),
+    ("🇿🇼 Zimbabwe", "ZWE"),
+    ("🇿🇲 Zambia", "ZMB"),
+    ("🇿🇦 South Africa", "ZAF"),
+    ("🇾🇪 Yemen", "YEM"),
+    ("🇻🇳 Vietnam", "VNM"),
+    ("🇻🇪 Venezuela", "VEN"),
+    ("🇺🇿 Uzbekistan", "UZB"),
+    ("🇺🇾 Uruguay", "URY"),
+    ("🇺🇸 United States", "USA"),
+    ("🇺🇦 Ukraine", "UKR"),
+    ("🇹🇿 Tanzania", "TZA"),
+    ("🇹🇼 Taiwan", "TWN"),
+    ("🇹🇹 Trinidad and Tobago", "TTO"),
+    ("🇹🇷 Turkey", "TUR"),
+    ("🇹🇳 Tunisia", "TUN"),
+    ("🇹🇲 Turkmenistan", "TKM"),
+    ("🇹🇯 Tajikistan", "TJK"),
+    ("🇹🇭 Thailand", "THA"),
+    ("🇹🇬 Togo", "TGO"),
+    ("🇸🇾 Syrian Arab Republic", "SYR"),
+    ("🇸🇻 El Salvador", "SLV"),
+    ("🇸🇳 Senegal", "SEN"),
+    ("🇸🇰 Slovak Republic", "SVK"),
+    ("🇸🇮 Slovenia", "SVN"),
+    ("🇸🇬 Singapore", "SGP"),
+    ("🇸🇪 Sweden", "SWE"),
+    ("🇸🇩 Sudan", "SDN"),
+    ("🇸🇦 Saudi Arabia", "SAU"),
+    ("🇷🇺 Russian Federation", "RUS"),
+    ("🇷🇸 Serbia and Montenegro", "SCG"),
+    ("🇷🇴 Romania", "ROU"),
+    ("🇶🇦 Qatar", "QAT"),
+    ("🇵🇾 Paraguay", "PRY"),
+    ("🇵🇹 Portugal", "PRT"),
+    ("🇵🇱 Poland", "POL"),
+    ("🇵🇰 Pakistan", "PAK"),
+    ("🇵🇭 Philippines", "PHL"),
+    ("🇵🇪 Peru", "PER"),
+    ("🇵🇦 Panama", "PAN"),
+    ("🇴🇲 Oman", "OMN"),
+    ("🇳🇿 New Zealand", "NZL"),
+    ("🇳🇵 Nepal", "NPL"),
+    ("🇳🇴 Norway", "NOR"),
+    ("🇳🇱 Netherlands", "NLD"),
+    ("🇳🇮 Nicaragua", "NIC"),
+    ("🇳🇬 Nigeria", "NGA"),
+    ("🇳🇦 Namibia", "NAM"),
+    ("🇲🇿 Mozambique", "MOZ"),
+    ("🇲🇾 Malaysia", "MYS"),
+    ("🇲🇽 Mexico", "MEX"),
+    ("🇲🇹 Malta", "MLT"),
+    ("🇲🇳 Mongolia", "MNG"),
+    ("🇲🇲 Myanmar", "MMR"),
+    ("🇲🇰 North Macedonia", "MKD"),
+    ("🇲🇩 Moldova", "MDA"),
+    ("🇲🇦 Morocco", "MAR"),
+    ("🇱🇾 Libya", "LBY"),
+    ("🇱🇻 Latvia", "LVA"),
+    ("🇱🇺 Luxembourg", "LUX"),
+    ("🇱🇹 Lithuania", "LTU"),
+    ("🇱🇰 Sri Lanka", "LKA"),
+    ("🇱🇧 Lebanon", "LBN"),
+    ("🇰🇿 Kazakhstan", "KAZ"),
+    ("🇰🇼 Kuwait", "KWT"),
+    ("🇰🇷 South Korea", "KOR"),
+    ("🇰🇵 North Korea", "PRK"),
+    ("🇰🇭 Cambodia", "KHM"),
+    ("🇰🇬 Kyrgyz Republic", "KGZ"),
+    ("🇰🇪 Kenya", "KEN"),
+    ("🇯🇵 Japan", "JPN"),
+    ("🇯🇴 Jordan", "JOR"),
+    ("🇯🇲 Jamaica", "JAM"),
+    ("🇮🇹 Italy", "ITA"),
+    ("🇮🇸 Iceland", "ISL"),
+    ("🇮🇷 Iran", "IRN"),
+    ("🇮🇶 Iraq", "IRQ"),
+    ("🇮🇳 India", "IND"),
+    ("🇮🇱 Israel", "ISR"),
+    ("🇮🇪 Ireland", "IRL"),
+    ("🇮🇩 Indonesia", "IDN"),
+    ("🇭🇺 Hungary", "HUN"),
+    ("🇭🇹 Haiti", "HTI"),
+    ("🇭🇷 Croatia", "HRV"),
+    ("🇭🇳 Honduras", "HND"),
+    ("🇭🇰 Hong Kong", "HKG"),
+    ("🇬🇹 Guatemala", "GTM"),
+    ("🇬🇷 Greece", "GRC"),
+    ("🇬🇮 Gibraltar", "GIB"),
+    ("🇬🇭 Ghana", "GHA"),
+    ("🇬🇪 Georgia", "GEO"),
+    ("🇬🇧 United Kingdom", "GBR"),
+    ("🇬🇦 Gabon", "GAB"),
+    ("🇫🇷 France", "FRA"),
+    ("🇫🇮 Finland", "FIN"),
+    ("🇪🇹 Ethiopia", "ETH"),
+    ("🇪🇸 Spain", "ESP"),
+    ("🇪🇷 Eritrea", "ERI"),
+    ("🇪🇬 Egypt", "EGY"),
+    ("🇪🇪 Estonia", "EST"),
+    ("🇪🇨 Ecuador", "ECU"),
+    ("🇩🇿 Algeria", "DZA"),
+    ("🇩🇴 Dominican Republic", "DOM"),
+    ("🇩🇰 Denmark", "DNK"),
+    ("🇩🇪 Germany", "DEU"),
+    ("🇨🇿 Czech Republic", "CZE"),
+    ("🇨🇾 Cyprus", "CYP"),
+    ("🇨🇺 Cuba", "CUB"),
+    ("🇨🇷 Costa Rica", "CRI"),
+    ("🇨🇴 Colombia", "COL"),
+    ("🇨🇳 China", "CHN"),
+    ("🇨🇲 Cameroon", "CMR"),
+    ("🇨🇱 Chile", "CHL"),
+    ("🇨🇮 Cote d'Ivoire", "CIV"),
+    ("🇨🇭 Switzerland", "CHE"),
+    ("🇨🇬 Congo", "COG"),
+    ("🇨🇩 Democratic Republic of the Congo", "COD"),
+    ("🇨🇦 Canada", "CAN"),
+    ("🇧🇾 Belarus", "BLR"),
+    ("🇧🇼 Botswana", "BWA"),
+    ("🇧🇷 Brazil", "BRA"),
+    ("🇧🇴 Bolivia", "BOL"),
+    ("🇧🇳 Brunei", "BRN"),
+    ("🇧🇯 Benin", "BEN"),
+    ("🇧🇭 Bahrain", "BHR"),
+    ("🇧🇬 Bulgaria", "BGR"),
+    ("🇧🇪 Belgium", "BEL"),
+    ("🇧🇩 Bangladesh", "BGD"),
+    ("🇧🇦 Bosnia and Herzegovina", "BIH"),
+    ("🇦🇿 Azerbaijan", "AZE"),
+    ("🇦🇺 Australia", "AUS"),
+    ("🇦🇹 Austria", "AUT"),
+    ("🇦🇷 Argentina", "ARG"),
+    ("🇦🇴 Angola", "AGO"),
+    ("🇦 Netherlands Antilles", "ANT"),
+    ("🇦🇲 Armenia", "ARM"),
+    ("🇦🇱 Albania", "ALB"),
+    ("🇦🇪 United Arab Emirates", "ARE")
+]
+
+
+def find_electricity_mix(code: str):
+    # TODO: Maybe more optimal to construct database at the beginning of the app
+    #       in the same fashion as find_model
+    res = []
+    with open(PATH) as fd:
+        csv = DictReader(fd)
+        for row in csv:
+            res += [float(row[code])]
+    return res
+
+def dataframe_electricity_mix(countries: list):
+
+    df = pd.read_csv('src/data/electricity_mix.csv')
+    df['name_unit'] = df['name'] + ' (' + df['unit'] + ')'
+    df = df[['name_unit'] + [x[1] for x in COUNTRY_CODES if x[0] in countries]]
+
+    df_melted = df.melt(
+        id_vars=['name_unit'],
+        value_vars=[x[1] for x in COUNTRY_CODES if x[0] in countries],
+        var_name='country',
+        value_name='value')
+
+    df = df_melted.pivot(columns='name_unit', 
+                        index='country',
+                        values='value')
+
+    return df

src/expert.py

@@ -0,0 +1,156 @@
+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.")

src/impacts.py

@@ -0,0 +1,116 @@
+import streamlit as st
+import ecologits
+from src.utils import (
+    format_energy_eq_electric_vehicle,
+    format_energy_eq_electricity_consumption_ireland,
+    format_energy_eq_electricity_production,
+    format_energy_eq_physical_activity,
+    format_gwp_eq_airplane_paris_nyc,
+    format_gwp_eq_streaming,
+    PhysicalActivity,
+    EnergyProduction
+)
+
+############################################################################################################
+
+def get_impacts(model, active_params, total_params, mix_ghg, mix_adpe, mix_pe):
+
+    return 1
+
+############################################################################################################
+
+
+def display_impacts(impacts):
+
+    st.divider()
+
+    col_energy, col_ghg, col_adpe, col_pe, col_water = st.columns(5)
+
+    with col_energy:
+        st.markdown('<h4 align="center">⚡️ Energy</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.energy.magnitude:.3g} \ \large {impacts.energy.units}')
+        st.markdown(f'<p align="center"><i>Evaluates the electricity consumption<i></p>', unsafe_allow_html = True)
+
+    with col_ghg:
+        st.markdown('<h4 align="center">🌍️ GHG Emissions</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.gwp.magnitude:.3g} \ \large {impacts.gwp.units}')
+        st.markdown(f'<p align="center"><i>Evaluates the effect on global warming<i></p>', unsafe_allow_html = True)
+
+    with col_adpe:
+        st.markdown('<h4 align="center">🪨 Abiotic Resources</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.adpe.magnitude:.3g} \ \large {impacts.adpe.units}')
+        st.markdown(f'<p align="center"><i>Evaluates the use of metals and minerals<i></p>', unsafe_allow_html = True)
+
+    with col_pe:
+        st.markdown('<h4 align="center">⛽️ Primary Energy</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.pe.magnitude:.3g} \ \large {impacts.pe.units}')
+        st.markdown(f'<p align="center"><i>Evaluates the use of energy resources<i></p>', unsafe_allow_html = True)
+
+    with col_water:
+        st.markdown('<h4 align="center">🚰 Water</h4>', unsafe_allow_html = True)
+        st.latex('\Large Upcoming...')
+        st.markdown(f'<p align="center"><i>Evaluates the use of water<i></p>', unsafe_allow_html = True)
+
+############################################################################################################
+
+def display_equivalent(impacts):
+
+    st.divider()
+
+    ev_eq = format_energy_eq_electric_vehicle(impacts.energy)
+    
+    streaming_eq = format_gwp_eq_streaming(impacts.gwp)
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        physical_activity, distance = format_energy_eq_physical_activity(impacts.energy)
+        if physical_activity == PhysicalActivity.WALKING:
+            physical_activity = "🚶 " + physical_activity.capitalize()
+        if physical_activity == PhysicalActivity.RUNNING:
+            physical_activity = "🏃 " + physical_activity.capitalize()
+
+        st.markdown(f'<h4 align="center">{physical_activity}</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {distance.magnitude:.3g} \ \large {distance.units}')
+        st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
+
+    with col2:
+        ev_eq = format_energy_eq_electric_vehicle(impacts.energy)
+        st.markdown(f'<h4 align="center">🔋 Electric Vehicle</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {ev_eq.magnitude:.3g} \ \large {ev_eq.units}')
+        st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
+
+    with col3:
+        streaming_eq = format_gwp_eq_streaming(impacts.gwp)
+        st.markdown(f'<h4 align="center">⏯️ Streaming</h4>', unsafe_allow_html = True)
+        st.latex(f'\Large {streaming_eq.magnitude:.3g} \ \large {streaming_eq.units}')
+        st.markdown(f'<p align="center"><i>Based on GHG emissions<i></p>', unsafe_allow_html = True)
+    
+    st.divider()
+    
+    st.markdown('<h3 align="center">What if 1% of the planet does this request everyday for 1 year ?</h3>', unsafe_allow_html = True)
+    st.markdown('<p align="center">If this use case is largely deployed around the world, the equivalent impacts would be the impacts of this request x 1% of 8 billion people x 365 days in a year.</p>', unsafe_allow_html = True)
+
+    col4, col5, col6 = st.columns(3)
+
+    with col4:
+
+        electricity_production, count = format_energy_eq_electricity_production(impacts.energy)
+        if electricity_production == EnergyProduction.NUCLEAR:
+            emoji = "☢️"
+            name = "Nuclear power plants"
+        if electricity_production == EnergyProduction.WIND:
+            emoji = "💨️ "
+            name = "Wind turbines"
+        st.markdown(f'<h4 align="center">{emoji} {count.magnitude:.0f} {name} (yearly)</h4>', unsafe_allow_html = True)
+        st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
+        
+    with col5:
+        ireland_count = format_energy_eq_electricity_consumption_ireland(impacts.energy)
+        st.markdown(f'<h4 align="center">🇮🇪 {ireland_count.magnitude:.3f} x Ireland <span style="font-size: 12px">(yearly ⚡️ cons.)</span></h2></h4>', unsafe_allow_html = True)
+        st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
+
+    with col6:
+        paris_nyc_airplane = format_gwp_eq_airplane_paris_nyc(impacts.gwp)
+        st.markdown(f'<h4 align="center">✈️ {round(paris_nyc_airplane.magnitude):,} Paris ↔ NYC</h4>', unsafe_allow_html = True)
+        st.markdown(f'<p align="center"><i>Based on GHG emissions<i></p>', unsafe_allow_html = True)

src/models.py

@@ -0,0 +1,46 @@
+import requests
+import json
+import pandas as pd
+from src.constants import MODEL_REPOSITORY_URL
+
+def clean_models_data(df):
+    
+    dict_providers = {
+        'google': 'Google',
+        'mistralai': 'MistralAI',
+        'meta-llama': 'Meta',
+        'openai': 'OpenAI',
+        'anthropic': 'Anthropic',
+        'cohere': 'Cohere',
+        'microsoft': 'Microsoft',
+        'mistral-community': 'Mistral Community',
+        'databricks': 'Databricks'
+    }
+    
+    df.drop('type', axis=1, inplace=True)
+
+    df.loc[df['name'].str.contains('/'), 'name_clean'] = df.loc[df['name'].str.contains('/'), 'name'].str.split('/').str[1]
+    df['name_clean'] = df['name_clean'].fillna(df['name'])
+    df['name_clean'] = df['name_clean'].replace({'-': ' '}, regex = True)
+    
+    df.loc[df['provider'] == 'huggingface_hub', 'provider_clean'] = df.loc[df['provider'] == 'huggingface_hub', 'name'].str.split('/').str[0]
+    df['provider_clean'] = df['provider_clean'].fillna(df['provider'])
+    df['provider_clean'] = df['provider_clean'].replace(dict_providers, regex = True)
+    
+    df['architecture_type'] = df['architecture'].apply(lambda x: x['type'])
+    df['architecture_parameters'] = df['architecture'].apply(lambda x: x['parameters'])
+    
+    df['warnings'] = df['warnings'].apply(lambda x: ', '.join(x) if x else None).fillna('none')
+    df['warning_arch'] = df['warnings'].apply(lambda x: 'model-arch-not-released' in x)
+    df['warning_multi_modal'] = df['warnings'].apply(lambda x: 'model-arch-multimodal' in x)
+    
+    return df[['provider', 'provider_clean', 'name', 'name_clean', 'architecture_type', 'architecture_parameters', 'warning_arch', 'warning_multi_modal']]
+
+def load_models():
+
+    resp = requests.get(MODEL_REPOSITORY_URL)
+    data = json.loads(resp.text)
+    df = pd.DataFrame(data['models'])
+
+    return clean_models_data(df)
+

src/scrapper.py

@@ -1,33 +0,0 @@
-import requests
-from bs4 import BeautifulSoup
-import tiktoken
-
-tokenizer = tiktoken.get_encoding('cl100k_base')
-
-def process_input(text):
-
-    r = requests.get(text, verify=False)
-    
-    soup = BeautifulSoup(r.text, "html.parser")
-    print(soup)
-    list_text = str(soup).split('parts":["')
-    #print(list_text)
-    s = ''
-    for item in list_text[1:int(len(list_text)/2)]:
-        if list_text.index(item)%2 == 1:
-                s = s + item.split('"]')[0]
-
-    amout_token = tiktoken_len(s)
-
-    return amout_token
-
-def tiktoken_len(text):
-            tokens = tokenizer.encode(
-                text,
-                disallowed_special=()
-            )
-            return len(tokens)
-
-answer = process_input('https://chatgpt.com/share/6737b9b5-56fc-8002-a212-35339f5b1d5a')
-
-print(answer)

src/utils.py

@@ -1,197 +1,262 @@
-from dataclasses import dataclass
-from enum import Enum
-
-import pandas as pd
-
-from ecologits.impacts.modeling import Impacts, Energy, GWP, ADPe, PE
-from ecologits.tracers.utils import llm_impacts, _avg
-from pint import UnitRegistry, Quantity
-
-
-u = UnitRegistry()
-u.define('Wh = watt_hour')
-u.define('kWh = kilowatt_hour')
-u.define('MWh = megawatt_hour')
-u.define('GWh = gigawatt_hour')
-u.define('TWh = terawatt_hour')
-u.define('gCO2eq = gram')
-u.define('kgCO2eq = kilogram')
-u.define('tCO2eq = metricton')
-u.define('kgSbeq = kilogram')
-u.define('kJ = kilojoule')
-u.define('MJ = megajoule')
-u.define('m = meter')
-u.define('km = kilometer')
-u.define('s = second')
-u.define('min = minute')
-u.define('h = hour')
-q = u.Quantity
-
-
-@dataclass
-class QImpacts:
-    energy: Quantity
-    gwp: Quantity
-    adpe: Quantity
-    pe: Quantity
-
-
-class PhysicalActivity(str, Enum):
-    RUNNING = "running"
-    WALKING = "walking"
-
-
-class EnergyProduction(str, Enum):
-    NUCLEAR = "nuclear"
-    WIND = "wind"
-
-
-COUNTRIES = [
-    ("cook_islands", 38.81, 9_556),
-    ("tonga", 51.15, 104_490),
-    ("comoros", 100, 821_632),
-    ("samoa", 100, 821_632),
-]
-
-def df_elec_mix_for_plot():
-    return pd.DataFrame({
-                'country': ['Sweden', 'France', 'Canada', 'USA', 'China', 'Australia', 'India'],
-                'electricity_mix': [46, 81, 238, 679, 1057, 1123, 1583]
-            })
-
-# From https://www.runningtools.com/energyusage.htm
-RUNNING_ENERGY_EQ = q("294 kJ / km")     # running 1 km at 10 km/h with a weight of 70 kg
-WALKING_ENERGY_EQ = q("196 kJ / km")     # walking 1 km at 3 km/h with a weight of 70 kg
-
-# From https://selectra.info/energie/actualites/insolite/consommation-vehicules-electriques-france-2040
-# and https://www.tesla.com/fr_fr/support/power-consumption
-EV_ENERGY_EQ = q("0.17 kWh / km")
-
-# From https://impactco2.fr/outils/comparateur?value=1&comparisons=streamingvideo
-STREAMING_GWP_EQ = q("15.6 h / kgCO2eq")
-
-# From https://ourworldindata.org/population-growth
-ONE_PERCENT_WORLD_POPULATION = 80_000_000
-
-DAYS_IN_YEAR = 365
-
-# For a 900 MW nuclear plant -> 500 000 MWh / month
-# From https://www.edf.fr/groupe-edf/espaces-dedies/jeunes-enseignants/pour-les-jeunes/lenergie-de-a-a-z/produire-de-lelectricite/le-nucleaire-en-chiffres
-YEARLY_NUCLEAR_ENERGY_EQ = q("6 TWh")
-
-# For a 2MW wind turbine
-# https://www.ecologie.gouv.fr/eolien-terrestre
-YEARLY_WIND_ENERGY_EQ = q("4.2 GWh")
-
-# Ireland yearly electricity consumption
-# From https://en.wikipedia.org/wiki/List_of_countries_by_electricity_consumption
-YEARLY_IRELAND_ELECTRICITY_CONSUMPTION = q("33 TWh")
-IRELAND_POPULATION_MILLION = 5
-
-# From https://impactco2.fr/outils/comparateur?value=1&comparisons=&equivalent=avion-pny
-AIRPLANE_PARIS_NYC_GWP_EQ = q("177000 kgCO2eq")
-
-
-def format_energy(energy: Energy) -> Quantity:
-    val = q(energy.value, energy.unit)
-    if val < q("1 kWh"):
-        val = val.to("Wh")
-    return val
-
-
-def format_gwp(gwp: GWP) -> Quantity:
-    val = q(gwp.value, gwp.unit)
-    if val < q("1 kgCO2eq"):
-        val = val.to("gCO2eq")
-    return val
-
-
-def format_adpe(adpe: ADPe) -> Quantity:
-    return q(adpe.value, adpe.unit)
-
-
-def format_pe(pe: PE) -> Quantity:
-    val = q(pe.value, pe.unit)
-    if val < q("1 MJ"):
-        val = val.to("kJ")
-    return val
-
-def format_impacts(impacts: Impacts) -> QImpacts:
-    try:
-        impacts.energy.value = (impacts.energy.value.max + impacts.energy.value.min)/2
-        impacts.gwp.value = (impacts.gwp.value.max + impacts.gwp.value.min)/2
-        impacts.adpe.value = (impacts.adpe.value.max + impacts.adpe.value.min)/2
-        impacts.pe.value = (impacts.pe.value.max + impacts.pe.value.min)/2
-        return QImpacts(
-            energy=format_energy(impacts.energy),
-            gwp=format_gwp(impacts.gwp),
-            adpe=format_adpe(impacts.adpe),
-            pe=format_pe(impacts.pe)
-        )
-    except: #when no range
-        return QImpacts(
-            energy=format_energy(impacts.energy),
-            gwp=format_gwp(impacts.gwp),
-            adpe=format_adpe(impacts.adpe),
-            pe=format_pe(impacts.pe)
-        )
-
-def format_impacts_expert(impacts: Impacts) -> QImpacts:
-    return QImpacts(
-        energy=format_energy(impacts.energy),
-        gwp=format_gwp(impacts.gwp),
-        adpe=format_adpe(impacts.adpe),
-        pe=format_pe(impacts.pe)
-
-    ), impacts.usage, impacts.embodied
-
-
-def format_energy_eq_physical_activity(energy: Quantity) -> tuple[PhysicalActivity, Quantity]:
-    energy = energy.to("kJ")
-    running_eq = energy / RUNNING_ENERGY_EQ
-    if running_eq > q("1 km"):
-        return PhysicalActivity.RUNNING, running_eq
-
-    walking_eq = energy / WALKING_ENERGY_EQ
-    if walking_eq < q("1 km"):
-        walking_eq = walking_eq.to("meter")
-    return PhysicalActivity.WALKING, walking_eq
-
-
-def format_energy_eq_electric_vehicle(energy: Quantity) -> Quantity:
-    energy = energy.to("kWh")
-    ev_eq = energy / EV_ENERGY_EQ
-    if ev_eq < q("1 km"):
-        ev_eq = ev_eq.to("meter")
-    return ev_eq
-
-
-def format_gwp_eq_streaming(gwp: Quantity) -> Quantity:
-    gwp = gwp.to("kgCO2eq")
-    streaming_eq = gwp * STREAMING_GWP_EQ
-    if streaming_eq < q("1 h"):
-        streaming_eq = streaming_eq.to("min")
-    if streaming_eq < q("1 min"):
-        streaming_eq = streaming_eq.to("s")
-    return streaming_eq
-
-
-def format_energy_eq_electricity_production(energy: Quantity) -> tuple[EnergyProduction, Quantity]:
-    electricity_eq = energy * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
-    electricity_eq = electricity_eq.to("TWh")
-    if electricity_eq > YEARLY_NUCLEAR_ENERGY_EQ:
-        return EnergyProduction.NUCLEAR, electricity_eq / YEARLY_NUCLEAR_ENERGY_EQ
-    electricity_eq = electricity_eq.to("GWh")
-    return EnergyProduction.WIND, electricity_eq / YEARLY_WIND_ENERGY_EQ
-
-
-def format_energy_eq_electricity_consumption_ireland(energy: Quantity) -> Quantity:
-    electricity_eq = energy * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
-    electricity_eq = electricity_eq.to("TWh")
-    return electricity_eq / YEARLY_IRELAND_ELECTRICITY_CONSUMPTION
-
-
-def format_gwp_eq_airplane_paris_nyc(gwp: Quantity) -> Quantity:
-    gwp_eq = gwp * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
-    gwp_eq = gwp_eq.to("kgCO2eq")
-    return gwp_eq / AIRPLANE_PARIS_NYC_GWP_EQ
+from dataclasses import dataclass
+from enum import Enum
+
+import pandas as pd
+from ecologits.model_repository import models
+from ecologits.impacts.modeling import Impacts, Energy, GWP, ADPe, PE
+#from ecologits.tracers.utils import llm_impacts
+from pint import UnitRegistry, Quantity
+
+#####################################################################################
+### UNITS DEFINITION
+#####################################################################################
+
+u = UnitRegistry()
+u.define('Wh = watt_hour')
+u.define('kWh = kilowatt_hour')
+u.define('MWh = megawatt_hour')
+u.define('GWh = gigawatt_hour')
+u.define('TWh = terawatt_hour')
+u.define('gCO2eq = gram')
+u.define('kgCO2eq = kilogram')
+u.define('tCO2eq = metricton')
+u.define('kgSbeq = kilogram')
+u.define('kJ = kilojoule')
+u.define('MJ = megajoule')
+u.define('m = meter')
+u.define('km = kilometer')
+u.define('s = second')
+u.define('min = minute')
+u.define('h = hour')
+q = u.Quantity
+
+@dataclass
+class QImpacts:
+    energy: Quantity
+    gwp: Quantity
+    adpe: Quantity
+    pe: Quantity
+
+
+class PhysicalActivity(str, Enum):
+    RUNNING = "running"
+    WALKING = "walking"
+
+
+class EnergyProduction(str, Enum):
+    NUCLEAR = "nuclear"
+    WIND = "wind"
+
+
+COUNTRIES = [
+    ("cook_islands", 38.81, 9_556),
+    ("tonga", 51.15, 104_490),
+    ("comoros", 100, 821_632),
+    ("samoa", 100, 821_632),
+]
+
+#####################################################################################
+### EQUIVALENT RAW DATA
+#####################################################################################
+
+# From https://www.runningtools.com/energyusage.htm
+RUNNING_ENERGY_EQ = q("294 kJ / km")     # running 1 km at 10 km/h with a weight of 70 kg
+WALKING_ENERGY_EQ = q("196 kJ / km")     # walking 1 km at 3 km/h with a weight of 70 kg
+
+# From https://selectra.info/energie/actualites/insolite/consommation-vehicules-electriques-france-2040
+# and https://www.tesla.com/fr_fr/support/power-consumption
+EV_ENERGY_EQ = q("0.17 kWh / km")
+
+# From https://impactco2.fr/outils/comparateur?value=1&comparisons=streamingvideo
+STREAMING_GWP_EQ = q("15.6 h / kgCO2eq")
+
+# From https://ourworldindata.org/population-growth
+ONE_PERCENT_WORLD_POPULATION = 80_000_000
+
+DAYS_IN_YEAR = 365
+
+# For a 900 MW nuclear plant -> 500 000 MWh / month
+# From https://www.edf.fr/groupe-edf/espaces-dedies/jeunes-enseignants/pour-les-jeunes/lenergie-de-a-a-z/produire-de-lelectricite/le-nucleaire-en-chiffres
+YEARLY_NUCLEAR_ENERGY_EQ = q("6 TWh")
+
+# For a 2MW wind turbine
+# https://www.ecologie.gouv.fr/eolien-terrestre
+YEARLY_WIND_ENERGY_EQ = q("4.2 GWh")
+
+# Ireland yearly electricity consumption
+# From https://en.wikipedia.org/wiki/List_of_countries_by_electricity_consumption
+YEARLY_IRELAND_ELECTRICITY_CONSUMPTION = q("33 TWh")
+IRELAND_POPULATION_MILLION = 5
+
+# From https://impactco2.fr/outils/comparateur?value=1&comparisons=&equivalent=avion-pny
+AIRPLANE_PARIS_NYC_GWP_EQ = q("177000 kgCO2eq")
+
+def filter_models(provider, list_models):
+
+    model = 1
+
+    return model
+
+#####################################################################################
+### IMPACTS FORMATING
+#####################################################################################
+
+def format_energy(energy: Energy) -> Quantity:
+    val = q(energy.value, energy.unit)
+    if val < q("1 kWh"):
+        val = val.to("Wh")
+    return val
+
+def format_gwp(gwp: GWP) -> Quantity:
+    val = q(gwp.value, gwp.unit)
+    if val < q("1 kgCO2eq"):
+        val = val.to("gCO2eq")
+    return val
+
+def format_adpe(adpe: ADPe) -> Quantity:
+    return q(adpe.value, adpe.unit)
+
+def format_pe(pe: PE) -> Quantity:
+    val = q(pe.value, pe.unit)
+    if val < q("1 MJ"):
+        val = val.to("kJ")
+    return val
+
+def format_impacts(impacts: Impacts) -> QImpacts:
+
+    try:
+        impacts.energy.value = (impacts.energy.value.max + impacts.energy.value.min)/2
+        impacts.gwp.value = (impacts.gwp.value.max + impacts.gwp.value.min)/2
+        impacts.adpe.value = (impacts.adpe.value.max + impacts.adpe.value.min)/2
+        impacts.pe.value = (impacts.pe.value.max + impacts.pe.value.min)/2
+        return QImpacts(
+            energy=format_energy(impacts.energy),
+            gwp=format_gwp(impacts.gwp),
+            adpe=format_adpe(impacts.adpe),
+            pe=format_pe(impacts.pe)
+        ), impacts.usage, impacts.embodied
+    except: #when no range
+        return QImpacts(
+            energy=format_energy(impacts.energy),
+            gwp=format_gwp(impacts.gwp),
+            adpe=format_adpe(impacts.adpe),
+            pe=format_pe(impacts.pe)
+        ), impacts.usage, impacts.embodied
+
+def split_impacts_u_e(impacts: Impacts) -> QImpacts:
+    return impacts.usage, impacts.embodied
+
+def average_range_impacts(RangeValue):
+    return (RangeValue.max + RangeValue.min)/2
+
+def format_impacts_expert(impacts: Impacts, display_range: bool) -> QImpacts:
+    
+    if display_range:
+        return QImpacts(
+            energy=format_energy(impacts.energy),
+            gwp=format_gwp(impacts.gwp),
+            adpe=format_adpe(impacts.adpe),
+            pe=format_pe(impacts.pe)
+        ), impacts.usage, impacts.embodied
+    
+    else:
+        energy = {"value":(impacts.energy.value.max + impacts.energy.value.min)/2, "unit":impacts.energy.unit}
+        gwp = (impacts.gwp.value.max + impacts.gwp.value.min)/2
+        adpe = (impacts.adpe.value.max + impacts.adpe.value.min)/2
+        pe = (impacts.pe.value.max + impacts.pe.value.min)/2
+        return QImpacts(
+            energy=format_energy(energy),
+            gwp=format_gwp(gwp),
+            adpe=format_adpe(adpe),
+            pe=format_pe(pe)
+        ), impacts.usage, impacts.embodied
+
+#####################################################################################
+### EQUIVALENT FORMATING
+#####################################################################################
+
+def format_energy_eq_physical_activity(energy: Quantity) -> tuple[PhysicalActivity, Quantity]:
+    energy = energy.to("kJ")
+    running_eq = energy / RUNNING_ENERGY_EQ
+    if running_eq > q("1 km"):
+        return PhysicalActivity.RUNNING, running_eq
+
+    walking_eq = energy / WALKING_ENERGY_EQ
+    if walking_eq < q("1 km"):
+        walking_eq = walking_eq.to("meter")
+    return PhysicalActivity.WALKING, walking_eq
+
+def format_energy_eq_electric_vehicle(energy: Quantity) -> Quantity:
+    energy = energy.to("kWh")
+    ev_eq = energy / EV_ENERGY_EQ
+    if ev_eq < q("1 km"):
+        ev_eq = ev_eq.to("meter")
+    return ev_eq
+
+def format_gwp_eq_streaming(gwp: Quantity) -> Quantity:
+    gwp = gwp.to("kgCO2eq")
+    streaming_eq = gwp * STREAMING_GWP_EQ
+    if streaming_eq < q("1 h"):
+        streaming_eq = streaming_eq.to("min")
+    if streaming_eq < q("1 min"):
+        streaming_eq = streaming_eq.to("s")
+    return streaming_eq
+
+def format_energy_eq_electricity_production(energy: Quantity) -> tuple[EnergyProduction, Quantity]:
+    electricity_eq = energy * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+    electricity_eq = electricity_eq.to("TWh")
+    if electricity_eq > YEARLY_NUCLEAR_ENERGY_EQ:
+        return EnergyProduction.NUCLEAR, electricity_eq / YEARLY_NUCLEAR_ENERGY_EQ
+    electricity_eq = electricity_eq.to("GWh")
+    return EnergyProduction.WIND, electricity_eq / YEARLY_WIND_ENERGY_EQ
+
+
+def format_energy_eq_electricity_consumption_ireland(energy: Quantity) -> Quantity:
+    electricity_eq = energy * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+    electricity_eq = electricity_eq.to("TWh")
+    return electricity_eq / YEARLY_IRELAND_ELECTRICITY_CONSUMPTION
+
+def format_gwp_eq_airplane_paris_nyc(gwp: Quantity) -> Quantity:
+    gwp_eq = gwp * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+    gwp_eq = gwp_eq.to("kgCO2eq")
+    return gwp_eq / AIRPLANE_PARIS_NYC_GWP_EQ
+
+#####################################################################################
+### MODELS PARAMETERS
+#####################################################################################
+
+def model_active_params_fn(provider_name: str, model_name: str, n_param: float):
+    if model_name == 'CUSTOM':
+        return n_param
+    else:
+        model = models.find_model(provider=provider_name, model_name=model_name)
+
+        if model.architecture == 'moe':
+            try:
+                return model.architecture.parameters.active.max
+            except:
+                try:
+                    return model.architecture.parameters.active
+                except:
+                    return model.architecture.parameters
+        elif model.architecture == 'dense':
+            try: #dense with range
+                return model.architecture.parameters.max
+            except: #dense without range
+                return model.architecture.parameters
+
+def model_total_params_fn(provider_name: str, model_name: str, n_param: float):
+    if model_name == 'CUSTOM':
+        return n_param
+    provider, model_name = model_name.split('/', 1)
+    model = models.find_model(provider=provider, model_name=model_name)
+    try: #moe
+        return model.architecture.parameters.total.max
+    except:
+        try: #dense with range
+            return model.architecture.parameters.max
+        except: #dense without range
+            try:
+                return model.architecture.parameters.total
+            except:
+                return model.architecture.parameters

utils.py

@@ -0,0 +1,262 @@
+from dataclasses import dataclass
+from enum import Enum
+
+import pandas as pd
+from ecologits.model_repository import models
+from ecologits.impacts.modeling import Impacts, Energy, GWP, ADPe, PE
+#from ecologits.tracers.utils import llm_impacts
+from pint import UnitRegistry, Quantity
+
+#####################################################################################
+### UNITS DEFINITION
+#####################################################################################
+
+u = UnitRegistry()
+u.define('Wh = watt_hour')
+u.define('kWh = kilowatt_hour')
+u.define('MWh = megawatt_hour')
+u.define('GWh = gigawatt_hour')
+u.define('TWh = terawatt_hour')
+u.define('gCO2eq = gram')
+u.define('kgCO2eq = kilogram')
+u.define('tCO2eq = metricton')
+u.define('kgSbeq = kilogram')
+u.define('kJ = kilojoule')
+u.define('MJ = megajoule')
+u.define('m = meter')
+u.define('km = kilometer')
+u.define('s = second')
+u.define('min = minute')
+u.define('h = hour')
+q = u.Quantity
+
+@dataclass
+class QImpacts:
+    energy: Quantity
+    gwp: Quantity
+    adpe: Quantity
+    pe: Quantity
+
+
+class PhysicalActivity(str, Enum):
+    RUNNING = "running"
+    WALKING = "walking"
+
+
+class EnergyProduction(str, Enum):
+    NUCLEAR = "nuclear"
+    WIND = "wind"
+
+
+COUNTRIES = [
+    ("cook_islands", 38.81, 9_556),
+    ("tonga", 51.15, 104_490),
+    ("comoros", 100, 821_632),
+    ("samoa", 100, 821_632),
+]
+
+#####################################################################################
+### EQUIVALENT RAW DATA
+#####################################################################################
+
+# From https://www.runningtools.com/energyusage.htm
+RUNNING_ENERGY_EQ = q("294 kJ / km")     # running 1 km at 10 km/h with a weight of 70 kg
+WALKING_ENERGY_EQ = q("196 kJ / km")     # walking 1 km at 3 km/h with a weight of 70 kg
+
+# From https://selectra.info/energie/actualites/insolite/consommation-vehicules-electriques-france-2040
+# and https://www.tesla.com/fr_fr/support/power-consumption
+EV_ENERGY_EQ = q("0.17 kWh / km")
+
+# From https://impactco2.fr/outils/comparateur?value=1&comparisons=streamingvideo
+STREAMING_GWP_EQ = q("15.6 h / kgCO2eq")
+
+# From https://ourworldindata.org/population-growth
+ONE_PERCENT_WORLD_POPULATION = 80_000_000
+
+DAYS_IN_YEAR = 365
+
+# For a 900 MW nuclear plant -> 500 000 MWh / month
+# From https://www.edf.fr/groupe-edf/espaces-dedies/jeunes-enseignants/pour-les-jeunes/lenergie-de-a-a-z/produire-de-lelectricite/le-nucleaire-en-chiffres
+YEARLY_NUCLEAR_ENERGY_EQ = q("6 TWh")
+
+# For a 2MW wind turbine
+# https://www.ecologie.gouv.fr/eolien-terrestre
+YEARLY_WIND_ENERGY_EQ = q("4.2 GWh")
+
+# Ireland yearly electricity consumption
+# From https://en.wikipedia.org/wiki/List_of_countries_by_electricity_consumption
+YEARLY_IRELAND_ELECTRICITY_CONSUMPTION = q("33 TWh")
+IRELAND_POPULATION_MILLION = 5
+
+# From https://impactco2.fr/outils/comparateur?value=1&comparisons=&equivalent=avion-pny
+AIRPLANE_PARIS_NYC_GWP_EQ = q("177000 kgCO2eq")
+
+def filter_models(provider, list_models):
+
+    model = 1
+
+    return model
+
+#####################################################################################
+### IMPACTS FORMATING
+#####################################################################################
+
+def format_energy(energy: Energy) -> Quantity:
+    val = q(energy.value, energy.unit)
+    if val < q("1 kWh"):
+        val = val.to("Wh")
+    return val
+
+def format_gwp(gwp: GWP) -> Quantity:
+    val = q(gwp.value, gwp.unit)
+    if val < q("1 kgCO2eq"):
+        val = val.to("gCO2eq")
+    return val
+
+def format_adpe(adpe: ADPe) -> Quantity:
+    return q(adpe.value, adpe.unit)
+
+def format_pe(pe: PE) -> Quantity:
+    val = q(pe.value, pe.unit)
+    if val < q("1 MJ"):
+        val = val.to("kJ")
+    return val
+
+def format_impacts(impacts: Impacts) -> QImpacts:
+
+    try:
+        impacts.energy.value = (impacts.energy.value.max + impacts.energy.value.min)/2
+        impacts.gwp.value = (impacts.gwp.value.max + impacts.gwp.value.min)/2
+        impacts.adpe.value = (impacts.adpe.value.max + impacts.adpe.value.min)/2
+        impacts.pe.value = (impacts.pe.value.max + impacts.pe.value.min)/2
+        return QImpacts(
+            energy=format_energy(impacts.energy),
+            gwp=format_gwp(impacts.gwp),
+            adpe=format_adpe(impacts.adpe),
+            pe=format_pe(impacts.pe)
+        ), impacts.usage, impacts.embodied
+    except: #when no range
+        return QImpacts(
+            energy=format_energy(impacts.energy),
+            gwp=format_gwp(impacts.gwp),
+            adpe=format_adpe(impacts.adpe),
+            pe=format_pe(impacts.pe)
+        ), impacts.usage, impacts.embodied
+
+def split_impacts_u_e(impacts: Impacts) -> QImpacts:
+    return impacts.usage, impacts.embodied
+
+def average_range_impacts(RangeValue):
+    return (RangeValue.max + RangeValue.min)/2
+
+def format_impacts_expert(impacts: Impacts, display_range: bool) -> QImpacts:
+    
+    if display_range:
+        return QImpacts(
+            energy=format_energy(impacts.energy),
+            gwp=format_gwp(impacts.gwp),
+            adpe=format_adpe(impacts.adpe),
+            pe=format_pe(impacts.pe)
+        ), impacts.usage, impacts.embodied
+    
+    else:
+        energy = {"value":(impacts.energy.value.max + impacts.energy.value.min)/2, "unit":impacts.energy.unit}
+        gwp = (impacts.gwp.value.max + impacts.gwp.value.min)/2
+        adpe = (impacts.adpe.value.max + impacts.adpe.value.min)/2
+        pe = (impacts.pe.value.max + impacts.pe.value.min)/2
+        return QImpacts(
+            energy=format_energy(energy),
+            gwp=format_gwp(gwp),
+            adpe=format_adpe(adpe),
+            pe=format_pe(pe)
+        ), impacts.usage, impacts.embodied
+
+#####################################################################################
+### EQUIVALENT FORMATING
+#####################################################################################
+
+def format_energy_eq_physical_activity(energy: Quantity) -> tuple[PhysicalActivity, Quantity]:
+    energy = energy.to("kJ")
+    running_eq = energy / RUNNING_ENERGY_EQ
+    if running_eq > q("1 km"):
+        return PhysicalActivity.RUNNING, running_eq
+
+    walking_eq = energy / WALKING_ENERGY_EQ
+    if walking_eq < q("1 km"):
+        walking_eq = walking_eq.to("meter")
+    return PhysicalActivity.WALKING, walking_eq
+
+def format_energy_eq_electric_vehicle(energy: Quantity) -> Quantity:
+    energy = energy.to("kWh")
+    ev_eq = energy / EV_ENERGY_EQ
+    if ev_eq < q("1 km"):
+        ev_eq = ev_eq.to("meter")
+    return ev_eq
+
+def format_gwp_eq_streaming(gwp: Quantity) -> Quantity:
+    gwp = gwp.to("kgCO2eq")
+    streaming_eq = gwp * STREAMING_GWP_EQ
+    if streaming_eq < q("1 h"):
+        streaming_eq = streaming_eq.to("min")
+    if streaming_eq < q("1 min"):
+        streaming_eq = streaming_eq.to("s")
+    return streaming_eq
+
+def format_energy_eq_electricity_production(energy: Quantity) -> tuple[EnergyProduction, Quantity]:
+    electricity_eq = energy * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+    electricity_eq = electricity_eq.to("TWh")
+    if electricity_eq > YEARLY_NUCLEAR_ENERGY_EQ:
+        return EnergyProduction.NUCLEAR, electricity_eq / YEARLY_NUCLEAR_ENERGY_EQ
+    electricity_eq = electricity_eq.to("GWh")
+    return EnergyProduction.WIND, electricity_eq / YEARLY_WIND_ENERGY_EQ
+
+
+def format_energy_eq_electricity_consumption_ireland(energy: Quantity) -> Quantity:
+    electricity_eq = energy * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+    electricity_eq = electricity_eq.to("TWh")
+    return electricity_eq / YEARLY_IRELAND_ELECTRICITY_CONSUMPTION
+
+def format_gwp_eq_airplane_paris_nyc(gwp: Quantity) -> Quantity:
+    gwp_eq = gwp * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+    gwp_eq = gwp_eq.to("kgCO2eq")
+    return gwp_eq / AIRPLANE_PARIS_NYC_GWP_EQ
+
+#####################################################################################
+### MODELS PARAMETERS
+#####################################################################################
+
+def model_active_params_fn(provider_name: str, model_name: str, n_param: float):
+    if model_name == 'CUSTOM':
+        return n_param
+    else:
+        model = models.find_model(provider=provider_name, model_name=model_name)
+
+        if model.architecture == 'moe':
+            try:
+                return model.architecture.parameters.active.max
+            except:
+                try:
+                    return model.architecture.parameters.active
+                except:
+                    return model.architecture.parameters
+        elif model.architecture == 'dense':
+            try: #dense with range
+                return model.architecture.parameters.max
+            except: #dense without range
+                return model.architecture.parameters
+
+def model_total_params_fn(provider_name: str, model_name: str, n_param: float):
+    if model_name == 'CUSTOM':
+        return n_param
+    provider, model_name = model_name.split('/', 1)
+    model = models.find_model(provider=provider, model_name=model_name)
+    try: #moe
+        return model.architecture.parameters.total.max
+    except:
+        try: #dense with range
+            return model.architecture.parameters.max
+        except: #dense without range
+            try:
+                return model.architecture.parameters.total
+            except:
+                return model.architecture.parameters