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