refactor: remove unused code and format

app.py

@@ -15,7 +15,7 @@ from src.expert import expert_mode
 from src.calculator import calculator_mode
 from src.token_estimator import token_estimator
 
-st.set_page_config(layout="wide", page_title="ECOLOGITS", page_icon="💬")
+st.set_page_config(layout="wide", page_title="EcoLogits Calculator", page_icon="🧮")
 
 with open("src/style.css") as css:
     st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)

src/__init__.py

@@ -4,6 +4,6 @@ from .expert import expert_mode
 from .token_estimator import token_estimator
 from .utils import *
 from .calculator import calculator_mode
-from .impacts import get_impacts, display_impacts
+from .impacts import display_impacts
 from .models import load_models
 from .electricity_mix import *

src/calculator.py

@@ -1,99 +1,99 @@
-import streamlit as st
-
-from ecologits.tracers.utils import llm_impacts
-from src.impacts import get_impacts, display_impacts, display_equivalent_ghg, display_equivalent_energy
-from src.utils import format_impacts
-from src.content import WARNING_CLOSED_SOURCE, WARNING_MULTI_MODAL, WARNING_BOTH, HOW_TO_TEXT
-from src.models import load_models
-
-from src.constants import PROMPTS
-
-
-def calculator_mode():
-
-    st.expander("How to use this calculator?", expanded = False).markdown(HOW_TO_TEXT)
-
-    with st.container(border=True):
-        df = load_models(filter_main=True)
-
-        col1, col2, col3 = st.columns(3)
-
-        with col1:
-            providers_clean = [x for x in df["provider_clean"].unique()]
-            provider = st.selectbox(
-                label="Provider",
-                options=providers_clean,
-                index=providers_clean.index("OpenAI"),
-            )
-
-        with col2:
-            model = st.selectbox(
-                label="Model",
-                options=[
-                    x
-                    for x in df["name_clean"].unique()
-                    if x in df[df["provider_clean"] == provider]["name_clean"].unique()
-                ],
-            )
-
-        with col3:
-            output_tokens = st.selectbox("Example prompt", [x[0] for x in PROMPTS])
-
-        # WARNING DISPLAY
-        provider_raw = df[
-            (df["provider_clean"] == provider) & (df["name_clean"] == model)
-        ]["provider"].values[0]
-        model_raw = df[
-            (df["provider_clean"] == provider) & (df["name_clean"] == model)
-        ]["name"].values[0]
-
-        df_filtered = df[
-            (df["provider_clean"] == provider) & (df["name_clean"] == model)
-        ]
-
-        if (
-            df_filtered["warning_arch"].values[0]
-            and not df_filtered["warning_multi_modal"].values[0]
-        ):
-            st.warning(WARNING_CLOSED_SOURCE, icon="⚠️")
-        if (
-            df_filtered["warning_multi_modal"].values[0]
-            and not df_filtered["warning_arch"].values[0]
-        ):
-            st.warning(WARNING_MULTI_MODAL, icon="⚠️")
-        if (
-            df_filtered["warning_arch"].values[0]
-            and df_filtered["warning_multi_modal"].values[0]
-        ):
-            st.warning(WARNING_BOTH, icon="⚠️")
-
-    try:
-        impacts = llm_impacts(
-            provider=provider_raw,
-            model_name=model_raw,
-            output_token_count=[x[1] for x in PROMPTS if x[0] == output_tokens][0],
-            request_latency=100000,
-        )
-
-        impacts, _, _ = format_impacts(impacts)
-
-        with st.container(border=True):
-
-            st.markdown('<h3 align = "center">Environmental impacts</h3>', unsafe_allow_html=True)
-            #st.markdown('<p align = "center">To understand how the environmental impacts are computed go to the 📖 Methodology tab.</p>', unsafe_allow_html=True)
-            display_impacts(impacts)                 
-        
-        with st.container(border=False):
-            st.markdown('<h3 align = "center">Equivalences</h3>', unsafe_allow_html=True)
-            st.markdown('<p align = "center">Making this request to the LLM is equivalent to the following actions :</p>', unsafe_allow_html=True)
-            page = st.radio(' ', ['Energy' , 'GHG'], horizontal=True)
-        
-        with st.container(border=True):                                            
-             if page == 'Energy' :
-                display_equivalent_energy(impacts)
-             else :  
-                display_equivalent_ghg(impacts)
-        
-            
-    except Exception as e:
+import streamlit as st
+
+from ecologits.tracers.utils import llm_impacts
+from src.impacts import display_impacts, display_equivalent_ghg, display_equivalent_energy
+from src.utils import format_impacts
+from src.content import WARNING_CLOSED_SOURCE, WARNING_MULTI_MODAL, WARNING_BOTH, HOW_TO_TEXT
+from src.models import load_models
+
+from src.constants import PROMPTS
+
+
+def calculator_mode():
+
+    st.expander("How to use this calculator?", expanded = False).markdown(HOW_TO_TEXT)
+
+    with st.container(border=True):
+        df = load_models(filter_main=True)
+
+        col1, col2, col3 = st.columns(3)
+
+        with col1:
+            providers_clean = [x for x in df["provider_clean"].unique()]
+            provider = st.selectbox(
+                label="Provider",
+                options=providers_clean,
+                index=providers_clean.index("OpenAI"),
+            )
+
+        with col2:
+            model = st.selectbox(
+                label="Model",
+                options=[
+                    x
+                    for x in df["name_clean"].unique()
+                    if x in df[df["provider_clean"] == provider]["name_clean"].unique()
+                ],
+            )
+
+        with col3:
+            output_tokens = st.selectbox("Example prompt", [x[0] for x in PROMPTS])
+
+        # WARNING DISPLAY
+        provider_raw = df[
+            (df["provider_clean"] == provider) & (df["name_clean"] == model)
+        ]["provider"].values[0]
+        model_raw = df[
+            (df["provider_clean"] == provider) & (df["name_clean"] == model)
+        ]["name"].values[0]
+
+        df_filtered = df[
+            (df["provider_clean"] == provider) & (df["name_clean"] == model)
+        ]
+
+        if (
+            df_filtered["warning_arch"].values[0]
+            and not df_filtered["warning_multi_modal"].values[0]
+        ):
+            st.warning(WARNING_CLOSED_SOURCE, icon="⚠️")
+        if (
+            df_filtered["warning_multi_modal"].values[0]
+            and not df_filtered["warning_arch"].values[0]
+        ):
+            st.warning(WARNING_MULTI_MODAL, icon="⚠️")
+        if (
+            df_filtered["warning_arch"].values[0]
+            and df_filtered["warning_multi_modal"].values[0]
+        ):
+            st.warning(WARNING_BOTH, icon="⚠️")
+
+    try:
+        impacts = llm_impacts(
+            provider=provider_raw,
+            model_name=model_raw,
+            output_token_count=[x[1] for x in PROMPTS if x[0] == output_tokens][0],
+            request_latency=100000,
+        )
+
+        impacts, _, _ = format_impacts(impacts)
+
+        with st.container(border=True):
+
+            st.markdown('<h3 align = "center">Environmental impacts</h3>', unsafe_allow_html=True)
+            #st.markdown('<p align = "center">To understand how the environmental impacts are computed go to the 📖 Methodology tab.</p>', unsafe_allow_html=True)
+            display_impacts(impacts)                 
+        
+        with st.container(border=False):
+            st.markdown('<h3 align = "center">Equivalences</h3>', unsafe_allow_html=True)
+            st.markdown('<p align = "center">Making this request to the LLM is equivalent to the following actions :</p>', unsafe_allow_html=True)
+            page = st.radio(' ', ['Energy' , 'GHG'], horizontal=True)
+        
+        with st.container(border=True):                                            
+             if page == 'Energy' :
+                display_equivalent_energy(impacts)
+             else :  
+                display_equivalent_ghg(impacts)
+        
+            
+    except Exception as e:
         st.error('Could not find the model in the repository. Please try another model.')

src/electricity_mix.py

@@ -74,22 +74,3 @@ def format_country_name(code: str) -> str | None:
 
 def format_electricity_mix_criterion(criterion: str) -> str | None:
     return CRITERIA.get(criterion)
-
-
-def dataframe_electricity_mix(countries: list):
-    df = pd.read_csv("src/data/electricity_mix.csv")
-
-
-    df["name_unit"] = df["name"] + " (" + df["unit"] + ")"
-    df = df[["name_unit"] + [x[1] for x in COUNTRY_CODES if x[0] in countries]]
-
-    df_melted = df.melt(
-        id_vars=["name_unit"],
-        value_vars=[x[1] for x in COUNTRY_CODES if x[0] in countries],
-        var_name="country",
-        value_name="value",
-    )
-
-    df = df_melted.pivot(columns="name_unit", index="country", values="value")
-
-    return df

src/expert.py

@@ -3,7 +3,7 @@ import streamlit as st
 from ecologits.electricity_mix_repository import electricity_mixes
 from ecologits.impacts.llm import compute_llm_impacts
 
-from src.utils import format_impacts, average_range_impacts
+from src.utils import format_impacts
 from src.impacts import display_impacts
 from src.electricity_mix import COUNTRY_CODES, format_electricity_mix_criterion, format_country_name
 from src.models import load_models
@@ -175,8 +175,8 @@ def expert_mode():
         with col_ghg_comparison:
             fig_gwp = px.pie(
                 values=[
-                    average_range_impacts(usage.gwp.value),
-                    average_range_impacts(embodied.gwp.value),
+                    usage.gwp.value if isinstance(usage.gwp.value, float) else usage.gwp.value.mean,
+                    embodied.gwp.value if isinstance(embodied.gwp.value, float) else embodied.gwp.value.mean,
                 ],
                 names=["usage", "embodied"],
                 title="GHG emissions",
@@ -190,8 +190,8 @@ def expert_mode():
         with col_adpe_comparison:
             fig_adpe = px.pie(
                 values=[
-                    average_range_impacts(usage.adpe.value),
-                    average_range_impacts(embodied.adpe.value),
+                    usage.adpe.value if isinstance(usage.adpe.value, float) else usage.adpe.value.mean,
+                    embodied.adpe.value if isinstance(embodied.adpe.value, float) else embodied.adpe.value.mean,
                 ],
                 names=["usage", "embodied"],
                 title="Abiotic depletion",
@@ -205,8 +205,8 @@ def expert_mode():
         with col_pe_comparison:
             fig_pe = px.pie(
                 values=[
-                    average_range_impacts(usage.pe.value),
-                    average_range_impacts(embodied.pe.value),
+                    usage.pe.value if isinstance(usage.pe.value, float) else usage.pe.value.mean,
+                    embodied.pe.value if isinstance(embodied.pe.value, float) else embodied.pe.value.mean,
                 ],
                 names=["usage", "embodied"],
                 title="Primary energy",

src/impacts.py

@@ -10,15 +10,6 @@ from src.utils import (
     EnergyProduction,
 )
 
-############################################################################################################
-
-
-def get_impacts(model, active_params, total_params, mix_ghg, mix_adpe, mix_pe):
-    return 1
-
-
-############################################################################################################
-
 
 def display_impacts(impacts):
     st.divider()

src/utils.py

@@ -1,12 +1,10 @@
 from dataclasses import dataclass
 from enum import Enum
 
-from ecologits.impacts.modeling import Impacts, Energy, GWP, ADPe, PE, WCF
+from ecologits.impacts.modeling import Impacts, Energy, GWP, ADPe, PE, WCF, Usage, Embodied
 
-# from ecologits.tracers.utils import llm_impacts
 from pint import UnitRegistry, Quantity
 import streamlit as st
-import plotly.express as px
 import plotly.graph_objects as go
 
 #####################################################################################
@@ -163,7 +161,7 @@ def format_wcf(wcf_value: float, wcf_unit = WCF(value=0.).unit) -> Quantity:
     return val
 
 
-def format_impacts(impacts: Impacts) -> QImpacts:
+def format_impacts(impacts: Impacts) -> tuple[QImpacts, Usage, Embodied]:
     if isinstance(impacts.energy.value, float):
         return QImpacts(
             energy=format_energy(impacts.energy.value),
@@ -192,50 +190,6 @@ def format_impacts(impacts: Impacts) -> QImpacts:
             wcf_max=format_wcf(impacts.wcf.value.max),
             ranges=True
         ), impacts.usage, impacts.embodied
-    
-
-def split_impacts_u_e(impacts: Impacts) -> QImpacts:
-    return impacts.usage, impacts.embodied
-
-
-def average_range_impacts(x):
-    if isinstance(x, float):
-        return x
-    else:
-        return (x.max + x.min) / 2
-
-
-def format_impacts_expert(impacts: Impacts, display_range: bool) -> QImpacts:
-    if display_range:
-        return (
-            QImpacts(
-                energy=format_energy(impacts.energy),
-                gwp=format_gwp(impacts.gwp),
-                adpe=format_adpe(impacts.adpe),
-                pe=format_pe(impacts.pe),
-            ),
-            impacts.usage,
-            impacts.embodied,
-        )
-
-    else:
-        energy = {
-            "value": (impacts.energy.value.max + impacts.energy.value.min) / 2,
-            "unit": impacts.energy.unit,
-        }
-        gwp = (impacts.gwp.value.max + impacts.gwp.value.min) / 2
-        adpe = (impacts.adpe.value.max + impacts.adpe.value.min) / 2
-        pe = (impacts.pe.value.max + impacts.pe.value.min) / 2
-        return (
-            QImpacts(
-                energy=format_energy(energy),
-                gwp=format_gwp(gwp),
-                adpe=format_adpe(adpe),
-                pe=format_pe(pe),
-            ),
-            impacts.usage,
-            impacts.embodied,
-        )
 
 
 #####################################################################################
@@ -295,7 +249,7 @@ def format_energy_eq_electricity_consumption_ireland(energy: Quantity) -> Quanti
 def format_gwp_eq_airplane_paris_nyc(gwp: Quantity) -> Quantity:
     gwp_eq = gwp * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
     gwp_eq = gwp_eq.to("kgCO2eq")
-    return gwp_eq / AIRPLANE_PARIS_NYC_GWP_EQ####################################################################################### MODELS PARAMETER####################################################################################
+    return gwp_eq / AIRPLANE_PARIS_NYC_GWP_EQ
 
 #####################################################################################
 ### VISUALIZATIONS
@@ -354,4 +308,4 @@ def range_plot (mean_val, min_val, max_val, unit):
     )
 
     # Show the plot in Streamlit
-    st.plotly_chart(fig, use_container_width=True)    
+    st.plotly_chart(fig, use_container_width=True)