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LICENSE

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README.md

@@ -1,13 +1,13 @@
 ---
 title: EcoLogits Calculator
-emoji: 🌱
-colorFrom: gray
-colorTo: blue
+emoji: 🧮
+colorFrom: green
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.26.0
+sdk_version: 4.36.1
 app_file: app.py
 pinned: true
-license: apache-2.0
+license: cc-by-sa-4.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
@@ -33,5 +33,7 @@ To do:
     - [X] Expose more inputs like the electricity mix
     - [X] Examples of electricity mixes
     - [X] Custom number of parameters
-- [ ] Live reload mode (Adrien)
-- [ ] Idea : "estimate a given prompt impact" function which allows to enter a prompt in a text field and estimate its impacts
+- [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

app.py

@@ -1,137 +1,85 @@
+
 import gradio as gr
-from pint import UnitRegistry
 
-from data.mixes import MIXES, find_mix
 
 from ecologits.tracers.utils import compute_llm_impacts, _avg
 from ecologits.impacts.llm import compute_llm_impacts as compute_llm_impacts_expert
 from ecologits.impacts.llm import IF_ELECTRICITY_MIX_GWP, IF_ELECTRICITY_MIX_ADPE, IF_ELECTRICITY_MIX_PE
 from ecologits.model_repository import models
 
-u = UnitRegistry()
-u.define('kWh = kilowatt_hour')
-u.define('Wh = watt_hour')
-u.define('gCO2eq = gram')
-u.define('kgCO2eq = kilogram')
-u.define('kgSbeq = kilogram')
-u.define('MJ = megajoule')
-u.define('kJ = kilojoule')
-u.define('m = meter')
-u.define('km = kilometer')
-u.define('episodes = number of episodes')
-q = u.Quantity
-
-
-MODELS = [
-    ("OpenAI / GPT-3.5-Turbo", "openai/gpt-3.5-turbo"),
-    ("OpenAI / GPT-4", "openai/gpt-4"),
-    ("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"),
-]
-
-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]
-
-
-def format_indicator(name: str, value: str, unit: str) -> str:
-    return f"""
-    ## {name}
-    $$ \LARGE {value} \ \large {unit} $$
-    """
-
-
-def form_output(impacts):
-    energy_ = q(impacts.energy.value, impacts.energy.unit)
-    eq_energy_ = q(impacts.energy.value * 2, 'km')
-    if energy_ < q("1 kWh"):
-        energy_ = energy_.to("Wh")
-        eq_energy_ = q(impacts.energy.value * 2000, 'm')
-    
-    gwp_ = q(impacts.gwp.value, impacts.gwp.unit)
-    eq_gwp_ = q(impacts.gwp.value / 0.032, 'episodes')
-    if gwp_ < q("1 kgCO2eq"):
-        gwp_ = gwp_.to("1 gCO2eq")
-        eq_gwp_ = q(impacts.gwp.value / 0.032, 'episodes')
-    adpe_ = q(impacts.adpe.value, impacts.adpe.unit)
-    
-    pe_ = q(impacts.pe.value, impacts.pe.unit)
-    if pe_ < q("1 MJ"):
-        pe_ = pe_.to("kJ")
-    
-    return (
-        format_indicator("⚡️ Energy", f"{energy_.magnitude:.3g}", energy_.units),
-        format_indicator("🌍 GHG Emissions", f"{gwp_.magnitude:.3g}", gwp_.units),
-        format_indicator("🪨 Abiotic Resources", f"{adpe_.magnitude:.3g}", adpe_.units),
-        format_indicator("⛽️ Primary Energy", f"{pe_.magnitude:.3g}", pe_.units),
-        format_indicator("🔋 Equivalent energy: distance with a small electric car", f"{eq_energy_.magnitude:.3g}", eq_energy_.units),
-        format_indicator("🏰 Equivalent emissions for 1000 prompts: watching GoT in streaming", f"{eq_gwp_.magnitude:.3g}", eq_gwp_.units)
-    )
-
-
-def form(
-    model_name: str,
-    prompt_generated_tokens: int
-):
-    provider, model_name = model_name.split('/', 1)
-    impacts = compute_llm_impacts(
-        provider=provider,
-        model_name=model_name,
-        output_token_count=prompt_generated_tokens,
-        request_latency=100000
-    )
-    return form_output(impacts)
-
-
-def form_expert(
-    model_active_params: float,
-    model_total_params: float,
-    prompt_generated_tokens: int,
-    mix_gwp: float,
-    mix_adpe: float,
-    mix_pe: float
-): 
-    impacts = compute_llm_impacts_expert(
-        model_active_parameter_count=model_active_params,
-        model_total_parameter_count=model_total_params,
-        output_token_count=prompt_generated_tokens,
-        request_latency=100000, 
-        if_electricity_mix_gwp=mix_gwp,
-        if_electricity_mix_adpe=mix_adpe,
-        if_electricity_mix_pe=mix_pe
-    )
-    return form_output(impacts)
-
+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,
+    MODELS
+)
+from src.utils import (
+    format_impacts,
+    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
+)
 
 CUSTOM = "Custom"
-def 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
@@ -139,6 +87,7 @@ def model_active_params_fn(model_name: str, n_param: float):
     model = models.find_model(provider=provider, model_name=model_name)
     return model.active_parameters or _avg(model.active_parameters_range)
 
+
 def model_total_params_fn(model_name: str, n_param: float):
     if model_name == CUSTOM:
         return n_param
@@ -146,82 +95,163 @@ def model_total_params_fn(model_name: str, n_param: float):
     model = models.find_model(provider=provider, model_name=model_name)
     return model.total_parameters or _avg(model.total_parameters_range)
 
+
 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_mix(country_code)
-
-with gr.Blocks() as demo:
+    return find_electricity_mix(country_code)
 
-### TITLE
-    gr.Markdown("""
-    # 🌱 EcoLogits Calculator
-    
-    **EcoLogits** is a python library that tracks the **energy consumption** and **environmental footprint** of using 
-    **generative AI** models through APIs.
 
-    Read the documentation: 
-    [ecologits.ai](https://ecologits.ai) | ⭐️ us on GitHub: [genai-impact/ecologits](https://github.com/genai-impact/ecologits) |
-    ✅ Follow us on Linkedin: [GenAI Impact](https://www.linkedin.com/company/genai-impact/posts/?feedView=all) 
-    """)
-
-### SIMPLE CALCULATOR
-    with gr.Tab("Home"):
-        gr.Markdown(""" 
-        ## 😊 Calculator
-        """)
+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():
-            model = gr.Dropdown(
-                MODELS,
-                label="Model name",
-                value="openai/gpt-3.5-turbo",
+            input_provider = gr.Dropdown(
+                PROVIDERS,
+                label="Provider",
+                value=PROVIDERS[0][1],
                 filterable=True,
-            )                
-            prompt = gr.Dropdown(
-                PROMPTS,
-                label="Example prompt",
-                value=50
             )
 
-        with gr.Row():
-            energy = gr.Markdown(
-                label="energy",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            gwp = gr.Markdown(
-                label="gwp",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            adpe = gr.Markdown(
-                label="adpe",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
+            input_model = gr.Dropdown(
+                OPENAI_MODELS,
+                label="Model",
+                value=OPENAI_MODELS[0][1],
+                filterable=True,
             )
-            pe = gr.Markdown(
-                label="pe",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
+            input_provider.change(model_list, input_provider, input_model)
+
+            input_prompt = gr.Dropdown(
+                PROMPTS,
+                label="Example prompt",
+                value=50,
             )
-            
-        gr.Markdown('---')
-            
+
+
+        @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 = compute_llm_impacts(
+                    provider=provider,
+                    model_name=model,
+                    output_token_count=prompt,
+                    request_latency=100000
+                )
+                impacts = format_impacts(impacts)
+
+                # Inference impacts
+                with gr.Blocks():
+                    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 everyday for 1 year?
+                        
+                        If this use case is largely deployed around the world the equivalent impacts would be.
+                        """)
+                    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} $$ \Large {count:.0f} $$ {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">🇮🇪 $$ \Large {ireland_count:.2g} $$ 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">✈️ $$ \Large {paris_nyc_airplane:,.0f} $$ 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():
-            equivalent_1 = gr.Markdown(
-                label="eq_energy",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            equivalent_2 = gr.Markdown(
-                label="eq_gwp",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-        
-        submit_btn = gr.Button("Submit")
-        submit_btn.click(fn=form, inputs=[model, prompt], outputs=[energy, gwp, adpe, pe, equivalent_1, equivalent_2])
-
-### EXPERT CALCULATOR
-    with gr.Tab("Expert Mode"):
-        gr.Markdown(""" 
-        ## 🤓 Expert mode
-        """)
+            gr.Markdown("# 🤓 Expert mode")
         model = gr.Dropdown(
             MODELS + [CUSTOM],
             label="Model name",
@@ -229,145 +259,140 @@ with gr.Blocks() as demo:
             filterable=True,
             interactive=True
         )
-        model_active_params = gr.Number(
+        input_model_active_params = gr.Number(
             label="Number of billions of active parameters",
             value=45.0,
-            interactive=True  
+            interactive=True
         )
-        model_total_params = gr.Number(
+        input_model_total_params = gr.Number(
             label="Number of billions of total parameters",
             value=45.0,
-            interactive=True  
+            interactive=True
         )
-    
-        model.change(fn=model_active_params_fn, inputs=[model, model_active_params], outputs=[model_active_params])
-        model.change(fn=model_total_params_fn, inputs=[model, model_total_params], outputs=[model_total_params])
-        model_active_params.input(fn=custom, outputs=[model])
-        model_total_params.input(fn=custom, outputs=[model])
+
+        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])
 
         tokens = gr.Number(
-            label="Output tokens", 
+            label="Output tokens",
             value=100
         )
 
         mix = gr.Dropdown(
-            MIXES + [CUSTOM], 
+            COUNTRY_CODES + [CUSTOM],
             label="Location",
-            value="WOR", 
+            value="WOR",
             filterable=True,
             interactive=True
         )
-        mix_adpe = gr.Number(
-            label="Electricity mix - Abiotic resources [kgSbeq / kWh]",
-            value=IF_ELECTRICITY_MIX_ADPE, 
+        input_mix_gwp = gr.Number(
+            label="Electricity mix - GHG emissions [kgCO2eq / kWh]",
+            value=IF_ELECTRICITY_MIX_GWP,
             interactive=True
         )
-        mix_pe = gr.Number(
-            label="Electricity mix - Primary energy [MJ / kWh]",
-            value=IF_ELECTRICITY_MIX_PE, 
+        input_mix_adpe = gr.Number(
+            label="Electricity mix - Abiotic resources [kgSbeq / kWh]",
+            value=IF_ELECTRICITY_MIX_ADPE,
             interactive=True
         )
-        mix_gwp = gr.Number(
-            label="Electricity mix - GHG emissions [kgCO2eq / kWh]",
-            value=IF_ELECTRICITY_MIX_GWP, 
+        input_mix_pe = gr.Number(
+            label="Electricity mix - Primary energy [MJ / kWh]",
+            value=IF_ELECTRICITY_MIX_PE,
             interactive=True
         )
 
-        mix.change(fn=mix_fn, inputs=[mix, mix_adpe, mix_pe, mix_gwp], outputs=[mix_adpe, mix_pe, mix_gwp])
-        mix_adpe.input(fn=custom, outputs=mix)
-        mix_pe.input(fn=custom, outputs=mix)
-        mix_gwp.input(fn=custom, outputs=mix)
-        
-        with gr.Row():
-            energy = gr.Markdown(
-                label="energy",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            gwp = gr.Markdown(
-                label="gwp",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            adpe = gr.Markdown(
-                label="adpe",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            pe = gr.Markdown(
-                label="pe",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-
-        with gr.Row():
-            energy = gr.Markdown(
-                label="energy",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
+        mix.change(fn=mix_fn,
+                   inputs=[mix, input_mix_gwp, input_mix_adpe, input_mix_pe],
+                   outputs=[input_mix_gwp, input_mix_adpe, input_mix_pe])
+        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_prompt,
+            input_mix_gwp,
+            input_mix_adpe,
+            input_mix_pe
+        ])
+        def render_expert(
+                model_active_params,
+                model_total_params,
+                prompt,
+                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=prompt,
+                request_latency=100000,
+                if_electricity_mix_gwp=mix_gwp,
+                if_electricity_mix_adpe=mix_adpe,
+                if_electricity_mix_pe=mix_pe
             )
-            gwp = gr.Markdown(
-                label="gwp",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            adpe = gr.Markdown(
-                label="adpe",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            pe = gr.Markdown(
-                label="pe",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            
-        gr.Markdown('---')
-            
-        with gr.Row():
-            equivalent_1 = gr.Markdown(
-                label="eq_energy",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-            equivalent_2 = gr.Markdown(
-                label="eq_gwp",
-                latex_delimiters=[{"left": "$$", "right": "$$", "display": False}]
-            )
-
-        submit_btn = gr.Button("Submit")
-        submit_btn.click(
-            fn=form_expert, 
-            inputs=[model_active_params, model_total_params, tokens, mix_gwp, mix_adpe, mix_pe], 
-            outputs=[energy, gwp, adpe, pe, equivalent_1, equivalent_2]
+            impacts = format_impacts(impacts)
+
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Markdown("## Environmental impacts")
+                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>
+                        """)
+
+    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')),
         )
 
-### METHOD QUICK EXPLANATION
-    with gr.Tab('Methodology'):
-        gr.Markdown("""## 📖 Methodology
-        🚧 Under construction
-        """)
-
-### INFORMATION ABOUT INDICATORS
-    with gr.Accordion("📊 More about the indicators", open = False):
-        gr.Markdown("""
-        - ⚡️ **Energy**: Final energy consumption, 
-        - 🌍 **GHG Emissions**: Potential impact on global warming (commonly known as GHG/carbon emissions), 
-        - 🪨 **Abiotic Resources**: Impact on the depletion of non-living resources such as minerals or metals, 
-        - ⛽️ **Primary Energy**: Total energy consumed from primary sources.
-        """)
-
-### INFORMATION ABOUT REDUCING IMPACTS
-    with gr.Accordion("📉 How to reduce / limit these impacts ?", open = False):
-        gr.Markdown("""
-                    
-        * ❓ **Fundamental rule**: Show **sobriety** on the uses of (generative) AI
-            * Questionning the usefulness of the project;
-            * Estimating impacts of the project;
-            * Evaluating the project purpose;
-            * Restricting the use case to the desired purposes.
-        
-        * 🦾 On the hardware side
-            * If you can, try to relocate the computing in low emissions and/or energy efficient datacenters.
-        
-        * 🤖 On the ML side :
-            * Develop a zero-shot learning approach for general tasks;
-            * Prefer the smaller and yet well-peforming models (using number of parameters for example); 
-            * If a specialization is needed, always prefer fine-tuning an existing model than re-training one from scratch;
-            * During model inference, try caching the most popular prompts ("hey, tell me a joke about ...").
-            
-        """)
+    # License
+    gr.Markdown(LICENCE_TEXT)
 
 if __name__ == '__main__':
     demo.launch()

src/assets.py

@@ -0,0 +1,6 @@
+custom_css = """
+
+.descriptive-text span {
+    font-size: 16px !important;
+}
+"""

src/constants.py

@@ -0,0 +1,82 @@
+PROVIDERS = [
+    ("OpenAI", "openai"),
+    ("Anthropic", "anthropic"),
+    ("Cohere", "cohere"),
+    ("Meta", "huggingface_hub/meta"),
+    ("Mistral AI", "mistralai"),
+]
+
+OPENAI_MODELS = [
+    ("GPT-3.5-Turbo", "gpt-3.5-turbo"),
+    ("GPT-4", "gpt-4"),
+]
+
+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]
+
+MODELS = [
+    ("OpenAI / GPT-3.5-Turbo", "openai/gpt-3.5-turbo"),
+    ("OpenAI / GPT-4", "openai/gpt-4"),
+    ("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"),
+]

src/content.py

@@ -0,0 +1,229 @@
+
+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 as 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 
+
+**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](git.lnyan.comm) 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
+
+### 🚶‍♂️‍➡️ Walking or 🏃‍♂️‍➡️ running distance
+
+🔴 TODO
+
+### 🔋 Electric Vehicle distance
+
+🔴 TODO
+
+### ⏯️ Streaming time
+
+🔴 TODO
+
+### Number of 💨 wind turbines or ☢️ nuclear plants
+
+🔴 TODO
+
+### Multiplier of 🇮🇪 Ireland electricity consumption
+
+🔴 TODO
+
+### Number of ✈️ Paris ↔ New York City flights
+
+🔴 TODO
+
+**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>"""

data/mixes.py → src/electricity_mix.py

@@ -1,7 +1,7 @@
 from csv import DictReader
 
 PATH = "data/electricity_mix.csv"
-MIXES = [
+COUNTRY_CODES = [
     ("🌎 World", "WOR"),
     ("🇪🇺 Europe", "EEE"),
     ("🇿🇼 Zimbabwe", "ZWE"),
@@ -143,7 +143,8 @@ MIXES = [
     ("🇦🇪 United Arab Emirates", "ARE")
 ]
 
-def find_mix(code: str):
+
+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 = []
@@ -151,4 +152,4 @@ def find_mix(code: str):
         csv = DictReader(fd)
         for row in csv:
             res += [float(row[code])]
-    return res
+    return res

src/utils.py

@@ -0,0 +1,182 @@
+from dataclasses import dataclass
+from enum import Enum
+
+from ecologits.impacts.models import Impacts, Energy, GWP, ADPe, PE
+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),
+]
+
+
+# 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")
+
+# 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")
+
+# From https://ourworldindata.org/population-growth
+ONE_PERCENT_WORLD_POPULATION = 80_000_000
+
+DAYS_IN_YEAR = 365
+
+# 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("1770 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:
+    return QImpacts(
+        energy=format_energy(impacts.energy),
+        gwp=format_gwp(impacts.gwp),
+        adpe=format_adpe(impacts.adpe),
+        pe=format_pe(impacts.pe),
+    )
+
+
+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("m")
+    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("m")
+    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, float]:
+    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) -> float:
+    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) -> float:
+    gwp_eq = gwp * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+    gwp_eq = gwp_eq.to("kgCO2eq")
+    return gwp_eq / AIRPLANE_PARIS_NYC_GWP_EQ
+
+
+if __name__ == '__main__':
+    # energy = 5590e-9    # GWh
+    # energy = 3.58e-9    # GWh
+    # val = q("5.59 kWh") # gpt4
+    val = q("0.448 Wh")
+    val = val.to("MWh")
+    pop = 80_000_000
+    days = 365
+
+    tot = val * pop * days
+    print(tot)