fix: correctly name arguments in token adjustement in expert mode

src/expert.py

@@ -1,183 +1,187 @@
-import streamlit as st
-from ecologits.impacts.llm import compute_llm_impacts
-
-from src.utils import format_impacts, average_range_impacts
-from src.impacts import display_impacts
-from src.electricity_mix import COUNTRY_CODES, find_electricity_mix, dataframe_electricity_mix
-from src.models import load_models
-from src.constants import PROMPTS
-
-import plotly.express as px
-
-def reset_model():
-    model = 'CUSTOM'
-
-def expert_mode():
-
-    st.markdown("### 🤓 Expert mode")
-
-    with st.container(border = True):
-
-        ########## Model info ##########
-
-        col1, col2, col3 = st.columns(3)
-        
-        df = load_models(filter_main=True)
-
-        with col1:
-            provider_exp = st.selectbox(
-                label = 'Provider',
-                options = [x for x in df['provider_clean'].unique()],
-                index = 7,
-                key = 1
-            )
-
-        with col2:
-            model_exp = st.selectbox(
-                label = 'Model',
-                options = [x for x in df['name_clean'].unique() if x in df[df['provider_clean'] == provider_exp]['name_clean'].unique()],
-                key = 2
-            )
-
-        with col3:
-            output_tokens_exp = st.selectbox(
-                label = 'Example prompt',
-                options = [x[0] for x in PROMPTS],
-                key = 3
-            )
-        
-        df_filtered = df[(df['provider_clean'] == provider_exp) & (df['name_clean'] == model_exp)]
-
-        try:
-            total_params = int(df_filtered['total_parameters'].iloc[0])
-        except:
-            total_params = int((df_filtered['total_parameters'].values[0]['min'] + df_filtered['total_parameters'].values[0]['max'])/2)
-            
-        try:
-            active_params = int(df_filtered['active_parameters'].iloc[0])
-        except:
-            active_params = int((df_filtered['active_parameters'].values[0]['min'] + df_filtered['active_parameters'].values[0]['max'])/2)
-
-        ########## Model parameters ##########   
-
-        col11, col22, col33 = st.columns(3)
-
-        with col11:
-            active_params = st.number_input('Active parameters (B)', 0, None, active_params)
-
-        with col22:
-            total_params = st.number_input('Total parameters (B)', 0, None, total_params)
-
-        with col33:
-            output_tokens = st.number_input('Output completion tokens', [x[1] for x in PROMPTS if x[0] == output_tokens_exp][0])
-
-        ########## Electricity mix ##########
-
-        location = st.selectbox('Location', [x[0] for x in COUNTRY_CODES])
-
-        col4, col5, col6 = st.columns(3)
-
-        with col4:
-            mix_gwp = st.number_input('Electricity mix - GHG emissions [kgCO2eq / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[2], format="%0.6f")
-            #disp_ranges = st.toggle('Display impact ranges', False)
-        with col5:
-            mix_adpe = st.number_input('Electricity mix - Abiotic resources [kgSbeq / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[0], format="%0.13f")
-        with col6:
-            mix_pe = st.number_input('Electricity mix - Primary energy [MJ / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[1], format="%0.3f")
-
-    impacts = compute_llm_impacts(model_active_parameter_count=active_params,
-                model_total_parameter_count=total_params,
-                output_token_count=output_tokens,
-                request_latency=100000,
-                if_electricity_mix_gwp=mix_gwp,
-                if_electricity_mix_adpe=mix_adpe,
-                if_electricity_mix_pe=mix_pe
-            )
-    
-    impacts, usage, embodied = format_impacts(impacts)
-    
-    with st.container(border = True):
-
-        st.markdown('<h3 align="center">Environmental Impacts</h2>', unsafe_allow_html = True)
-
-        display_impacts(impacts)
-
-    with st.expander('⚖️ Usage vs Embodied'):
-
-        st.markdown('<h3 align="center">Embodied vs Usage comparison</h2>', unsafe_allow_html = True)
-
-        st.markdown('The usage impacts account for the electricity consumption of the model while the embodied impacts account for resource extraction (e.g., minerals and metals), manufacturing, and transportation of the hardware.')
-        
-        col_ghg_comparison, col_adpe_comparison, col_pe_comparison = st.columns(3)
-        
-        with col_ghg_comparison:
-
-            fig_gwp = px.pie(
-            values = [average_range_impacts(usage.gwp.value), average_range_impacts(embodied.gwp.value)],
-            names = ['usage', 'embodied'],
-            title = 'GHG emissions',
-            color_discrete_sequence=["#00BF63", "#0B3B36"],
-            width = 100
-            )
-            fig_gwp.update_layout(showlegend=False, title_x=0.5)
-
-            st.plotly_chart(fig_gwp)
-
-        with col_adpe_comparison:
-            fig_adpe = px.pie(
-                values = [average_range_impacts(usage.adpe.value), average_range_impacts(embodied.adpe.value)],
-                names = ['usage', 'embodied'],
-                title = 'Abiotic depletion',
-                color_discrete_sequence=["#0B3B36","#00BF63"],
-                width = 100)
-            fig_adpe.update_layout(
-                showlegend=False,
-                title_x=0.5)
-            
-            st.plotly_chart(fig_adpe)
-
-        with col_pe_comparison:
-            fig_pe = px.pie(
-                values = [average_range_impacts(usage.pe.value), average_range_impacts(embodied.pe.value)],
-                names = ['usage', 'embodied'],
-                title = 'Primary energy',
-                color_discrete_sequence=["#00BF63", "#0B3B36"],
-                width = 100)
-            fig_pe.update_layout(showlegend=False, title_x=0.5)
-
-            st.plotly_chart(fig_pe)
-
-    with st.expander('🌍️ Location impact'):
-
-        st.markdown('<h4 align="center">How can location impact the footprint ?</h4>', unsafe_allow_html = True)
-
-        countries_to_compare = st.multiselect(
-            label = 'Countries to compare',
-            options = [x[0] for x in COUNTRY_CODES],
-            default = ["🇫🇷 France", "🇺🇸 United States", "🇨🇳 China"]
-            )
-
-        try:
-
-            df_comp = dataframe_electricity_mix(countries_to_compare)
-
-            impact_type = st.selectbox(
-                label='Select an impact type to compare',
-                options=[x for x in df_comp.columns if x!='country'],
-                index=1)
-
-            df_comp.sort_values(by = impact_type, inplace = True)
-            
-            fig_2 = px.bar(
-                df_comp,
-                x = df_comp.index,
-                y = impact_type,
-                text = impact_type,
-                color = impact_type
-            )
-            
-            st.plotly_chart(fig_2)
-
-        except:
-
+import streamlit as st
+from ecologits.impacts.llm import compute_llm_impacts
+
+from src.utils import format_impacts, average_range_impacts
+from src.impacts import display_impacts
+from src.electricity_mix import COUNTRY_CODES, find_electricity_mix, dataframe_electricity_mix
+from src.models import load_models
+from src.constants import PROMPTS
+
+import plotly.express as px
+
+def reset_model():
+    model = 'CUSTOM'
+
+def expert_mode():
+
+    st.markdown("### 🤓 Expert mode")
+
+    with st.container(border = True):
+
+        ########## Model info ##########
+
+        col1, col2, col3 = st.columns(3)
+        
+        df = load_models(filter_main=True)
+
+        with col1:
+            provider_exp = st.selectbox(
+                label = 'Provider',
+                options = [x for x in df['provider_clean'].unique()],
+                index = 7,
+                key = 1
+            )
+
+        with col2:
+            model_exp = st.selectbox(
+                label = 'Model',
+                options = [x for x in df['name_clean'].unique() if x in df[df['provider_clean'] == provider_exp]['name_clean'].unique()],
+                key = 2
+            )
+
+        with col3:
+            output_tokens_exp = st.selectbox(
+                label = 'Example prompt',
+                options = [x[0] for x in PROMPTS],
+                key = 3
+            )
+        
+        df_filtered = df[(df['provider_clean'] == provider_exp) & (df['name_clean'] == model_exp)]
+
+        try:
+            total_params = int(df_filtered['total_parameters'].iloc[0])
+        except:
+            total_params = int((df_filtered['total_parameters'].values[0]['min'] + df_filtered['total_parameters'].values[0]['max'])/2)
+            
+        try:
+            active_params = int(df_filtered['active_parameters'].iloc[0])
+        except:
+            active_params = int((df_filtered['active_parameters'].values[0]['min'] + df_filtered['active_parameters'].values[0]['max'])/2)
+
+        ########## Model parameters ##########   
+
+        col11, col22, col33 = st.columns(3)
+
+        with col11:
+            active_params = st.number_input('Active parameters (B)', 0, None, active_params)
+
+        with col22:
+            total_params = st.number_input('Total parameters (B)', 0, None, total_params)
+
+        with col33:
+            output_tokens = st.number_input(
+                label = 'Output completion tokens',
+                min_value = 0,
+                value = [x[1] for x in PROMPTS if x[0] == output_tokens_exp][0]
+            )
+
+        ########## Electricity mix ##########
+
+        location = st.selectbox('Location', [x[0] for x in COUNTRY_CODES])
+
+        col4, col5, col6 = st.columns(3)
+
+        with col4:
+            mix_gwp = st.number_input('Electricity mix - GHG emissions [kgCO2eq / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[2], format="%0.6f")
+            #disp_ranges = st.toggle('Display impact ranges', False)
+        with col5:
+            mix_adpe = st.number_input('Electricity mix - Abiotic resources [kgSbeq / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[0], format="%0.13f")
+        with col6:
+            mix_pe = st.number_input('Electricity mix - Primary energy [MJ / kWh]', find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[1], format="%0.3f")
+
+    impacts = compute_llm_impacts(model_active_parameter_count=active_params,
+                model_total_parameter_count=total_params,
+                output_token_count=output_tokens,
+                request_latency=100000,
+                if_electricity_mix_gwp=mix_gwp,
+                if_electricity_mix_adpe=mix_adpe,
+                if_electricity_mix_pe=mix_pe
+            )
+    
+    impacts, usage, embodied = format_impacts(impacts)
+    
+    with st.container(border = True):
+
+        st.markdown('<h3 align="center">Environmental Impacts</h2>', unsafe_allow_html = True)
+
+        display_impacts(impacts)
+
+    with st.expander('⚖️ Usage vs Embodied'):
+
+        st.markdown('<h3 align="center">Embodied vs Usage comparison</h2>', unsafe_allow_html = True)
+
+        st.markdown('The usage impacts account for the electricity consumption of the model while the embodied impacts account for resource extraction (e.g., minerals and metals), manufacturing, and transportation of the hardware.')
+        
+        col_ghg_comparison, col_adpe_comparison, col_pe_comparison = st.columns(3)
+        
+        with col_ghg_comparison:
+
+            fig_gwp = px.pie(
+            values = [average_range_impacts(usage.gwp.value), average_range_impacts(embodied.gwp.value)],
+            names = ['usage', 'embodied'],
+            title = 'GHG emissions',
+            color_discrete_sequence=["#00BF63", "#0B3B36"],
+            width = 100
+            )
+            fig_gwp.update_layout(showlegend=False, title_x=0.5)
+
+            st.plotly_chart(fig_gwp)
+
+        with col_adpe_comparison:
+            fig_adpe = px.pie(
+                values = [average_range_impacts(usage.adpe.value), average_range_impacts(embodied.adpe.value)],
+                names = ['usage', 'embodied'],
+                title = 'Abiotic depletion',
+                color_discrete_sequence=["#0B3B36","#00BF63"],
+                width = 100)
+            fig_adpe.update_layout(
+                showlegend=False,
+                title_x=0.5)
+            
+            st.plotly_chart(fig_adpe)
+
+        with col_pe_comparison:
+            fig_pe = px.pie(
+                values = [average_range_impacts(usage.pe.value), average_range_impacts(embodied.pe.value)],
+                names = ['usage', 'embodied'],
+                title = 'Primary energy',
+                color_discrete_sequence=["#00BF63", "#0B3B36"],
+                width = 100)
+            fig_pe.update_layout(showlegend=False, title_x=0.5)
+
+            st.plotly_chart(fig_pe)
+
+    with st.expander('🌍️ Location impact'):
+
+        st.markdown('<h4 align="center">How can location impact the footprint ?</h4>', unsafe_allow_html = True)
+
+        countries_to_compare = st.multiselect(
+            label = 'Countries to compare',
+            options = [x[0] for x in COUNTRY_CODES],
+            default = ["🇫🇷 France", "🇺🇸 United States", "🇨🇳 China"]
+            )
+
+        try:
+
+            df_comp = dataframe_electricity_mix(countries_to_compare)
+
+            impact_type = st.selectbox(
+                label='Select an impact type to compare',
+                options=[x for x in df_comp.columns if x!='country'],
+                index=1)
+
+            df_comp.sort_values(by = impact_type, inplace = True)
+            
+            fig_2 = px.bar(
+                df_comp,
+                x = df_comp.index,
+                y = impact_type,
+                text = impact_type,
+                color = impact_type
+            )
+            
+            st.plotly_chart(fig_2)
+
+        except:
+
             st.warning("Can't display chart with no values.")