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add chatgpt url import and locations static comparison in expert mode

app.py

@@ -1,406 +1,562 @@
-import gradio as gr
-
-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
-
-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_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 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)
-    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
-    provider, model_name = model_name.split('/', 1)
-    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_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 = compute_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=IF_ELECTRICITY_MIX_GWP,
-            interactive=True
-        )
-        input_mix_adpe = gr.Number(
-            label="Electricity mix - Abiotic resources [kgSbeq / kWh]",
-            value=IF_ELECTRICITY_MIX_ADPE,
-            interactive=True
-        )
-        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, 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 = 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')),
-        )
-
-    # License
-    gr.Markdown(LICENCE_TEXT)
-
-if __name__ == '__main__':
-    demo.launch()
+import gradio as gr
+
+import requests
+from bs4 import BeautifulSoup
+
+import tiktoken
+
+import matplotlib
+import matplotlib.pyplot as plt
+
+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
+
+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_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"
+
+tokenizer = tiktoken.get_encoding('cl100k_base')
+
+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 tiktoken_len(text):
+            tokens = tokenizer.encode(
+                text,
+                disallowed_special=()
+            )
+            return len(tokens)
+
+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)
+    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
+    provider, model_name = model_name.split('/', 1)
+    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_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 = compute_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=IF_ELECTRICITY_MIX_GWP,
+            interactive=True
+        )
+        input_mix_adpe = gr.Number(
+            label="Electricity mix - Abiotic resources [kgSbeq / kWh]",
+            value=IF_ELECTRICITY_MIX_ADPE,
+            interactive=True
+        )
+        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, 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 = format_impacts(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>
+                        """)
+                    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.Blocks():
+                    with gr.Row():
+                        gr.Markdown(f"""
+                                    <h2 align="center">How can location impact the footprint ?</h2>
+                                    """)
+                    with gr.Row():
+                        def create_static_bar_plot():
+                            categories = ['Sweden', 'France', 'Canada', 'USA', 'China', 'Australia', 'India']
+                            values = [46, 81, 238, 679, 1057, 1123, 1583]
+                            
+                            def addlabels(x,y):
+                                for i in range(len(x)):
+                                    plt.text(i, y[i], y[i], ha = 'center')
+                                
+                            fig, ax = plt.subplots(figsize=(15,5), facecolor='#1F2937')
+                            ax.bar(categories, values)
+                            #ax.set_xlabel('Countries')
+                            ax.set_ylabel('GHG emissions (gCO2eq) for 1kWh')
+                            ax.set_title('GWP emissions for 1 kWh of electricity consumption')
+                            ax.set_facecolor("#0B0F19")
+                            
+                            addlabels(categories, values)
+                            
+                            font = {'family' : 'monospace',
+                                    'weight' : 'normal',
+                                    'size'   : 14}
+
+                            matplotlib.rc('font', **font)
+                            matplotlib.rcParams.update({'text.color':'white',
+                                                        'axes.labelcolor':'white',
+                                                        'xtick.color':'white',
+                                                        'ytick.color':'white'})
+
+                            return fig
+                        
+                        static_plot = gr.Plot(value=create_static_bar_plot())
+
+    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 process_input(text):
+
+            r = requests.get(text, verify=False)
+            
+            soup = BeautifulSoup(r.text, "html.parser")
+            list_text = str(soup).split('parts":["')
+            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 compute_own_impacts(amount_token, model):
+            provider = model.split('/')[0].lower()
+            model = model.split('/')[1]
+            impacts = compute_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()

requirements-dev.txt

@@ -1,3 +1,6 @@
-gradio
-ecologits==0.1.7
-pint
+gradio
+ecologits==0.1.7
+pint
+beautifulsoup4
+requests
+tiktoken

requirements.txt

@@ -1,2 +1,5 @@
-ecologits==0.1.7
-pint
+ecologits==0.1.7
+pint
+beautifulsoup4
+requests
+tiktoken