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import gradio as gr
import pandas as pd
from src.about import CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, SUBMIT_TEXT, TITLE
from src.display.css_html_js import custom_css
from src.display.utils import COLS, TYPES, AutoEvalColumn, fields
from src.envs import CRM_RESULTS_PATH
from src.populate import get_leaderboard_df_crm
original_df = get_leaderboard_df_crm(CRM_RESULTS_PATH, COLS)
leaderboard_df = original_df.copy()
# leaderboard_df = leaderboard_df.style.format({"accuracy_metric_average": "{0:.2f}"})
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
framework_query: list
# llm_query: list,
# llm_provider_query: list,
# accuracy_method_query: str,
# accuracy_threshold_query: str,
# use_case_area_query: list,
# use_case_query: list,
# use_case_type_query: list,
# metric_area_query: list,
):
filtered_df = filter_framework_func(hidden_df, framework_query)
# filtered_df = filter_llm_func(hidden_df, llm_query)
# filtered_df = filter_llm_provider_func(filtered_df, llm_provider_query)
# filtered_df = filter_accuracy_method_func(filtered_df, accuracy_method_query)
# filtered_df["Accuracy Threshold"] = filter_accuracy_threshold_func(filtered_df, accuracy_threshold_query)
# filtered_df = filtered_df[filtered_df["Accuracy Threshold"]]
# filtered_df["Use Case Area"] = filtered_df["Use Case Name"].apply(lambda x: x.split(": ")[0])
# filtered_df = filter_use_case_area_func(filtered_df, use_case_area_query)
# filtered_df = filter_use_case_func(filtered_df, use_case_query)
# filtered_df = filter_use_case_type_func(filtered_df, use_case_type_query)
# Filtering by metric area
# metric_area_maps = {
# "Cost": ["Cost Band"],
# "Accuracy": ["Accuracy", "Instruction Following", "Conciseness", "Completeness", "Factuality"],
# "Speed (Latency)": ["Response Time (Sec)", "Mean Output Tokens"],
# "Trust & Safety": ["Trust & Safety", "Safety", "Privacy", "Truthfulness", "CRM Fairness"],
# }
# all_metric_cols = []
# for area in metric_area_maps:
# all_metric_cols = all_metric_cols + metric_area_maps[area]
# columns_to_keep = list(set(columns).difference(set(all_metric_cols)))
# for area in metric_area_query:
# columns_to_keep = columns_to_keep + metric_area_maps[area]
# columns = list(set(columns).intersection(set(columns_to_keep)))
df = select_columns(filtered_df, columns)
return df.style.map(highlight_cost_band_low, props="background-color: #b3d5a4")
# def highlight_cols(x):
# df = x.copy()
# df.loc[:, :] = "color: black"
# df.loc[, ["Accuracy"]] = "background-color: #b3d5a4"
# return df
def highlight_cost_band_low(s, props=""):
return props if s == "Low" else None
def init_leaderboard_df(
leaderboard_df: pd.DataFrame,
columns: list,
llm_query: list,
# llm_provider_query: list,
# accuracy_method_query: str,
# accuracy_threshold_query: str,
# use_case_area_query: list,
# use_case_query: list,
# use_case_type_query: list,
# metric_area_query: list,
):
# Applying the style function
# return df.style.apply(highlight_cols, axis=None)
return update_table(
leaderboard_df,
columns,
llm_query,
# llm_provider_query,
# accuracy_method_query,
# accuracy_threshold_query,
# use_case_area_query,
# use_case_query,
# use_case_type_query,
# metric_area_query,
)
def filter_accuracy_method_func(df: pd.DataFrame, accuracy_method_query: str) -> pd.DataFrame:
return df[df["Accuracy Method"] == accuracy_method_query]
def filter_accuracy_threshold_func(df: pd.DataFrame, accuracy_threshold_query: str) -> pd.DataFrame:
accuracy_cols = ["Instruction Following", "Conciseness", "Completeness", "Accuracy"]
return (df.loc[:, accuracy_cols] >= float(accuracy_threshold_query)).all(axis=1)
def filter_use_case_area_func(df: pd.DataFrame, use_case_area_query: list) -> pd.DataFrame:
return df[
df["Use Case Area"].apply(
lambda x: len(set([_.strip() for _ in x.split("&")]).intersection(use_case_area_query))
)
> 0
]
def filter_use_case_func(df: pd.DataFrame, use_case_query: list) -> pd.DataFrame:
return df[df["Use Case Name"].isin(use_case_query)]
def filter_use_case_type_func(df: pd.DataFrame, use_case_type_query: list) -> pd.DataFrame:
return df[df["Use Case Type"].isin(use_case_type_query)]
def filter_llm_func(df: pd.DataFrame, llm_query: list) -> pd.DataFrame:
return df[df["Model"].isin(llm_query)]
def filter_framework_func(df: pd.DataFrame, framework_query: list) -> pd.DataFrame:
return df[df["Agentic Framework"].isin(framework_query)]
def filter_llm_provider_func(df: pd.DataFrame, llm_provider_query: list) -> pd.DataFrame:
return df[df["LLM Provider"].isin(llm_provider_query)]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
# always_here_cols = [
# AutoEvalColumn.model.name,
# ]
# model_provider_col = [AutoEvalColumn.model_provider.name] if AutoEvalColumn.model_provider.name in columns else []
# We use COLS to maintain sortingx
filtered_df = df[
(
[AutoEvalColumn.model.name]
# + model_provider_col
+ [AutoEvalColumn.agentic_framework.name]
+ [c for c in COLS if c in df.columns and c in columns ]
+ [AutoEvalColumn.overall.name]
)
]
return filtered_df
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 CRMArena Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
filter_agentic_framework = gr.CheckboxGroup(
choices=list(original_df["Agentic Framework"].unique()),
value=list(original_df["Agentic Framework"].unique()),
label="Agentic Frameworks",
info="",
interactive=True,
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden
],
label="CRMArena Tasks",
elem_id="column-select",
interactive=True,
)
# with gr.Column():
# filter_llm = gr.CheckboxGroup(
# choices=list(original_df["Model"].unique()),
# value=list(original_df["Model"].unique()),
# label="Model",
# info="",
# interactive=True,
# )
# with gr.Column():
# with gr.Row():
# filter_llm_provider = gr.CheckboxGroup(
# choices=list(original_df["LLM Provider"].unique()),
# value=list(original_df["LLM Provider"].unique()),
# label="LLM Provider",
# info="",
# interactive=True,
# )
# with gr.Row():
# filter_metric_area = gr.CheckboxGroup(
# choices=["Accuracy", "Speed (Latency)", "Trust & Safety", "Cost"],
# value=["Accuracy", "Speed (Latency)", "Trust & Safety", "Cost"],
# label="Metric Area",
# info="",
# interactive=True,
# )
# with gr.Row():
# filter_use_case = gr.CheckboxGroup(
# choices=list(original_df["Use Case Name"].unique()),
# value=list(original_df["Use Case Name"].unique()),
# label="Use Case",
# info="",
# # multiselect=True,
# interactive=True,
# )
# with gr.Row():
# with gr.Column():
# filter_use_case_area = gr.CheckboxGroup(
# choices=["Service", "Sales"],
# value=["Service", "Sales"],
# label="Use Case Area",
# info="",
# interactive=True,
# )
# with gr.Column():
# filter_use_case_type = gr.CheckboxGroup(
# choices=["Summary", "Generation"],
# value=["Summary", "Generation"],
# label="Use Case Type",
# info="",
# interactive=True,
# )
# with gr.Column():
# filter_use_case = gr.Dropdown(
# choices=list(original_df["Use Case Name"].unique()),
# value=list(original_df["Use Case Name"].unique()),
# label="Use Case",
# info="",
# multiselect=True,
# interactive=True,
# )
# with gr.Column():
# filter_accuracy_method = gr.Radio(
# choices=["Manual", "Auto"],
# value="Manual",
# label="Accuracy Method",
# info="",
# interactive=True,
# )
# with gr.Column():
# filter_accuracy_threshold = gr.Number(
# value="0",
# label="Accuracy Threshold",
# info="Range: 0.0 to 4.0",
# interactive=True,
# )
leaderboard_table = gr.components.Dataframe(
# value=leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value],
value=init_leaderboard_df(
leaderboard_df,
shown_columns.value,
filter_agentic_framework.value
# filter_llm.value,
# filter_llm_provider.value,
# filter_accuracy_method.value,
# filter_accuracy_threshold.value,
# filter_use_case_area.value,
# filter_use_case.value,
# filter_use_case_type.value,
# filter_metric_area.value,
),
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df[COLS],
headers=COLS,
datatype=TYPES,
visible=False,
)
for selector in [
shown_columns,
filter_agentic_framework
# filter_llm,
# filter_llm_provider,
# filter_accuracy_method,
# filter_accuracy_threshold,
# filter_use_case_area,
# filter_use_case,
# filter_use_case_type,
# filter_metric_area,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_agentic_framework,
# filter_llm,
# filter_llm_provider,
# filter_accuracy_method,
# filter_accuracy_threshold,
# filter_use_case_area,
# filter_use_case,
# filter_use_case_type,
# filter_metric_area,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=3):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit", elem_id="llm-benchmark-tab-table", id=4):
gr.Markdown(SUBMIT_TEXT, elem_classes="markdown-text")
with gr.Row():
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
lines=20,
elem_id="citation-button",
show_copy_button=True,
)
# scheduler = BackgroundScheduler()
# scheduler.add_job(restart_space, "interval", seconds=1800)
# scheduler.start()
demo.queue(default_concurrency_limit=40).launch()
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