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import subprocess
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT_1,
LLM_BENCHMARKS_TEXT_2,
CROSS_EVALUATION_METRICS,
NOTE_GENERATION_METRICS,
# EVALUATION_EXAMPLE_IMG,
# LLM_BENCHMARKS_TEXT_2,
# ENTITY_DISTRIBUTION_IMG,
# LLM_BENCHMARKS_TEXT_3,
TITLE,
LOGO,
FIVE_PILLAR_DIAGRAM
)
from src.display.css_html_js import custom_css
# changes to be made here
from src.display.utils import (
DATASET_BENCHMARK_COLS,
OPEN_ENDED_BENCHMARK_COLS,
MED_SAFETY_BENCHMARK_COLS,
MEDICAL_SUMMARIZATION_BENCHMARK_COLS,
ACI_BENCHMARK_COLS,
SOAP_BENCHMARK_COLS,
DATASET_COLS,
OPEN_ENDED_COLS,
MED_SAFETY_COLS,
MEDICAL_SUMMARIZATION_COLS,
ACI_COLS,
SOAP_COLS,
EVAL_COLS,
EVAL_TYPES,
NUMERIC_INTERVALS,
TYPES,
AutoEvalColumn,
ModelType,
ModelArch,
PromptTemplateName,
Precision,
WeightType,
fields,
render_generation_templates
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval, PLACEHOLDER_DATASET_WISE_NORMALIZATION_CONFIG
def restart_space():
API.restart_space(repo_id=REPO_ID)
try:
print(EVAL_REQUESTS_PATH)
snapshot_download(
repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
)
except Exception:
restart_space()
try:
print(EVAL_RESULTS_PATH)
snapshot_download(
repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
)
except Exception:
restart_space()
# Span based results
# changes to be made here
_, harness_datasets_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, DATASET_COLS, DATASET_BENCHMARK_COLS, "accuracy", "datasets")
harness_datasets_leaderboard_df = harness_datasets_original_df.copy()
_, open_ended_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, OPEN_ENDED_COLS, OPEN_ENDED_BENCHMARK_COLS, "score", "open_ended")
open_ended_leaderboard_df = open_ended_original_df.copy()
_, med_safety_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, MED_SAFETY_COLS, MED_SAFETY_BENCHMARK_COLS, "score", "med_safety")
med_safety_leaderboard_df = med_safety_original_df.copy()
_, medical_summarization_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, MEDICAL_SUMMARIZATION_COLS, MEDICAL_SUMMARIZATION_BENCHMARK_COLS, "score", "medical_summarization")
medical_summarization_leaderboard_df = medical_summarization_original_df.copy()
_, aci_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, ACI_COLS, ACI_BENCHMARK_COLS, "score", "aci")
aci_leaderboard_df = aci_original_df.copy()
_, soap_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, SOAP_COLS, SOAP_BENCHMARK_COLS, "score", "soap")
soap_leaderboard_df = soap_original_df.copy()
# breakpoint()
# # Token based results
# _, token_based_datasets_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, DATASET_COLS, DATASET_BENCHMARK_COLS, "TokenBasedWithMacroAverage", "datasets")
# token_based_datasets_leaderboard_df = token_based_datasets_original_df.copy()
# _, token_based_types_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, Clinical_TYPES_COLS, TYPES_BENCHMARK_COLS, "TokenBasedWithMacroAverage", "clinical_types")
# token_based_types_leaderboard_df = token_based_types_original_df.copy()
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
# breakpoint()
def update_df(shown_columns, subset="datasets"):
# changes to be made here
if subset == "datasets":
leaderboard_table_df = harness_datasets_leaderboard_df.copy()
hidden_leader_board_df = harness_datasets_original_df
elif subset == "open_ended":
leaderboard_table_df = open_ended_leaderboard_df.copy()
hidden_leader_board_df = open_ended_original_df
elif subset == "med_safety":
leaderboard_table_df = med_safety_leaderboard_df.copy()
hidden_leader_board_df = med_safety_original_df
elif subset == "medical_summarization":
leaderboard_table_df = medical_summarization_leaderboard_df.copy()
hidden_leader_board_df = medical_summarization_original_df
elif subset == "aci":
leaderboard_table_df = aci_leaderboard_df.copy()
hidden_leader_board_df = aci_original_df
elif subset == "soap":
leaderboard_table_df = soap_leaderboard_df.copy()
hidden_leader_board_df = soap_original_df
# else:
# match evaluation_metric:
# case "Span Based":
# leaderboard_table_df = span_based_types_leaderboard_df.copy()
# hidden_leader_board_df = span_based_types_original_df
# case "Token Based":
# leaderboard_table_df = token_based_types_leaderboard_df.copy()
# hidden_leader_board_df = token_based_types_original_df
# case _:
# pass
value_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns
# breakpoint()
return leaderboard_table_df[value_cols], hidden_leader_board_df
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
query: str = "",
type_query: list = None,
domain_specific_query: list = None,
size_query: list = None,
precision_query: str = None,
show_deleted: bool = False,
):
filtered_df = filter_models(hidden_df, type_query, domain_specific_query, size_query, precision_query, show_deleted)
filtered_df = filter_queries(query, filtered_df)
df = select_columns(filtered_df, columns, list(hidden_df.columns))
return df
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list, cols:list) -> pd.DataFrame:
always_here_cols = [
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.model.name,
]
# We use COLS to maintain sorting
filtered_df = df[always_here_cols + [c for c in cols if c in df.columns and c in columns]]
return filtered_df
def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
final_df = []
if query != "":
queries = [q.strip() for q in query.split(";")]
for _q in queries:
_q = _q.strip()
if _q != "":
temp_filtered_df = search_table(filtered_df, _q)
if len(temp_filtered_df) > 0:
final_df.append(temp_filtered_df)
if len(final_df) > 0:
filtered_df = pd.concat(final_df)
filtered_df = filtered_df.drop_duplicates(
subset=[
AutoEvalColumn.model.name,
# AutoEvalColumn.precision.name,
# AutoEvalColumn.revision.name,
]
)
return filtered_df
def filter_models(
df: pd.DataFrame, type_query: list, domain_specific_query: list, size_query: list, precision_query: list, show_deleted: bool
) -> pd.DataFrame:
# Show all models
# if show_deleted:
# filtered_df = df
# else: # Show only still on the hub models
# filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
filtered_df = df
if type_query is not None:
type_name = [t.split(" ")[1] for t in type_query]
filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type.name].isin(type_name)]
if domain_specific_query is not None:
domain_specifics = []
if "🏥 Clinical models" in domain_specific_query:
domain_specifics.append(True)
if "Generic models" in domain_specific_query:
domain_specifics.append(False)
filtered_df = filtered_df.loc[df[AutoEvalColumn.is_domain_specific.name].isin(domain_specifics)]
# if architecture_query is not None:
# arch_types = [t for t in architecture_query]
# filtered_df = filtered_df.loc[df[AutoEvalColumn.architecture.name].isin(arch_types)]
# # filtered_df = filtered_df.loc[df[AutoEvalColumn.architecture.name].isin(architecture_query + ["None"])]
if precision_query is not None:
if AutoEvalColumn.precision.name in df.columns:
filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
if size_query is not None:
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
filtered_df = filtered_df.loc[mask]
return filtered_df
demo = gr.Blocks(css=custom_css)
with demo:
print("hello")
gr.HTML(TITLE)
gr.HTML(LOGO)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 Closed Ended Evaluation", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and (c.invariant or c.dataset_task_col)],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden and (c.invariant or c.dataset_task_col)
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
# with gr.Row():
# deleted_models_visibility = gr.Checkbox(
# value=False, label="Show gated/private/deleted models", interactive=True
# )
with gr.Column(min_width=320):
# with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model Types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
# filter_columns_architecture = gr.CheckboxGroup(
# label="Architecture Types",
# choices=[i.value.name for i in ModelArch],
# value=[i.value.name for i in ModelArch],
# interactive=True,
# elem_id="filter-columns-architecture",
# )
filter_domain_specific = gr.CheckboxGroup(
label="Domain Specificity",
choices=["🏥 Clinical models", "Generic models"],
value=["🏥 Clinical models", "Generic models"],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
datasets_leaderboard_df, datasets_original_df = update_df(shown_columns.value, subset="datasets")
leaderboard_table = gr.components.Dataframe(
value=datasets_leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.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=datasets_original_df[DATASET_COLS],
headers=DATASET_COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
# filter_columns_architecture
],
leaderboard_table,
)
for selector in [
shown_columns,
filter_columns_type,
filter_domain_specific,
# filter_columns_architecture,
filter_columns_size,
# deleted_models_visibility,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
# filter_columns_architecture,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("🏅 Open Ended Evaluation", elem_id="llm-benchmark-tab-table", id=1):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and (c.invariant or c.open_ended_col)],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden and (c.invariant or c.open_ended_col)
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
# with gr.Row():
# deleted_models_visibility = gr.Checkbox(
# value=False, label="Show gated/private/deleted models", interactive=True
# )
with gr.Column(min_width=320):
# with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model Types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
# filter_columns_architecture = gr.CheckboxGroup(
# label="Architecture Types",
# choices=[i.value.name for i in ModelArch],
# value=[i.value.name for i in ModelArch],
# interactive=True,
# elem_id="filter-columns-architecture",
# )
filter_domain_specific = gr.CheckboxGroup(
label="Domain Specificity",
choices=["🏥 Clinical models", "Generic models"],
value=["🏥 Clinical models", "Generic models"],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
datasets_leaderboard_df, datasets_original_df = update_df(shown_columns.value, subset="open_ended")
leaderboard_table = gr.components.Dataframe(
value=datasets_leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.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=datasets_original_df[OPEN_ENDED_COLS],
headers=OPEN_ENDED_COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
# filter_columns_architecture
],
leaderboard_table,
)
for selector in [
shown_columns,
filter_columns_type,
filter_domain_specific,
# filter_columns_architecture,
filter_columns_size,
# deleted_models_visibility,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
# filter_columns_architecture,
],
leaderboard_table,
queue=True,
)
with gr.Accordion("💬 Generation templates", open=False):
with gr.Accordion("Response generation", open=False):
system_prompt, user_prompt = render_generation_templates(task="open_ended", generation_type="response_generation")
with gr.Accordion("Scoring Rubric", open=False):
system_prompt, user_prompt = render_generation_templates(task="open_ended", generation_type="scoring_rubric")
with gr.TabItem("🏅 Med Safety", elem_id="llm-benchmark-tab-table", id=2):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and (c.invariant or c.med_safety_col)],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden and (c.invariant or c.med_safety_col)
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
# with gr.Row():
# deleted_models_visibility = gr.Checkbox(
# value=False, label="Show gated/private/deleted models", interactive=True
# )
with gr.Column(min_width=320):
# with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model Types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
# filter_columns_architecture = gr.CheckboxGroup(
# label="Architecture Types",
# choices=[i.value.name for i in ModelArch],
# value=[i.value.name for i in ModelArch],
# interactive=True,
# elem_id="filter-columns-architecture",
# )
filter_domain_specific = gr.CheckboxGroup(
label="Domain Specificity",
choices=["🏥 Clinical models", "Generic models"],
value=["🏥 Clinical models", "Generic models"],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
datasets_leaderboard_df, datasets_original_df = update_df(shown_columns.value, subset="med_safety")
leaderboard_table = gr.components.Dataframe(
value=datasets_leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.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=datasets_original_df[MED_SAFETY_COLS],
headers=MED_SAFETY_COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
# filter_columns_architecture
],
leaderboard_table,
)
for selector in [
shown_columns,
filter_columns_type,
filter_domain_specific,
filter_columns_size,
# deleted_models_visibility,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
],
leaderboard_table,
queue=True,
)
with gr.Accordion("💬 Generation templates", open=False):
with gr.Accordion("Response generation", open=False):
system_prompt, user_prompt = render_generation_templates(task="med_safety", generation_type="response_generation")
with gr.Accordion("Scoring Rubric", open=False):
system_prompt, user_prompt = render_generation_templates(task="med_safety", generation_type="scoring_rubric")
with gr.TabItem("🏅 Medical Summarization", elem_id="llm-benchmark-tab-table", id=3):
gr.Markdown(CROSS_EVALUATION_METRICS, elem_classes="markdown-text")
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and (c.invariant or c.medical_summarization_col)],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden and (c.invariant or c.medical_summarization_col)
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
# with gr.Row():
# deleted_models_visibility = gr.Checkbox(
# value=False, label="Show gated/private/deleted models", interactive=True
# )
with gr.Column(min_width=320):
# with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model Types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
# filter_columns_architecture = gr.CheckboxGroup(
# label="Architecture Types",
# choices=[i.value.name for i in ModelArch],
# value=[i.value.name for i in ModelArch],
# interactive=True,
# elem_id="filter-columns-architecture",
# )
filter_domain_specific = gr.CheckboxGroup(
label="Domain Specificity",
choices=["🏥 Clinical models", "Generic models"],
value=["🏥 Clinical models", "Generic models"],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
datasets_leaderboard_df, datasets_original_df = update_df(shown_columns.value, subset="medical_summarization")
leaderboard_table = gr.components.Dataframe(
value=datasets_leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.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=datasets_original_df[MEDICAL_SUMMARIZATION_COLS],
headers=MEDICAL_SUMMARIZATION_COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
# filter_columns_architecture
],
leaderboard_table,
)
for selector in [
shown_columns,
filter_columns_type,
filter_domain_specific,
filter_columns_size,
# deleted_models_visibility,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
],
leaderboard_table,
queue=True,
)
with gr.Accordion("💬 Generation templates", open=False):
with gr.Accordion("Response generation", open=False):
system_prompt, user_prompt = render_generation_templates(task="medical_summarization", generation_type="response_generation")
with gr.Accordion("Question generation", open=False):
system_prompt, user_prompt = render_generation_templates(task="ce", generation_type="question_generation")
with gr.Accordion("Cross Examination", open=False):
system_prompt, user_prompt = render_generation_templates(task="ce", generation_type="cross_examination")
with gr.TabItem("🏅 Note generation", elem_id="llm-benchmark-tab-table", id=4):
gr.Markdown(NOTE_GENERATION_METRICS, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons2") as tabs:
with gr.TabItem("ACI Bench", elem_id="llm-benchmark-tab-table2", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and (c.invariant or c.aci_col)],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden and (c.invariant or c.aci_col)
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
# with gr.Row():
# deleted_models_visibility = gr.Checkbox(
# value=False, label="Show gated/private/deleted models", interactive=True
# )
with gr.Column(min_width=320):
# with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model Types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
# filter_columns_architecture = gr.CheckboxGroup(
# label="Architecture Types",
# choices=[i.value.name for i in ModelArch],
# value=[i.value.name for i in ModelArch],
# interactive=True,
# elem_id="filter-columns-architecture",
# )
filter_domain_specific = gr.CheckboxGroup(
label="Domain Specificity",
choices=["🏥 Clinical models", "Generic models"],
value=["🏥 Clinical models", "Generic models"],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
datasets_leaderboard_df, datasets_original_df = update_df(shown_columns.value, subset="aci")
leaderboard_table = gr.components.Dataframe(
value=datasets_leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.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=datasets_original_df[ACI_COLS],
headers=ACI_COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
# filter_columns_architecture
],
leaderboard_table,
)
for selector in [
shown_columns,
filter_columns_type,
filter_domain_specific,
filter_columns_size,
# deleted_models_visibility,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
],
leaderboard_table,
queue=True,
)
with gr.TabItem("SOAP Notes", elem_id="llm-benchmark-tab-table2", id=1):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and (c.invariant or c.soap_col)],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden and (c.invariant or c.soap_col)
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
# with gr.Row():
# deleted_models_visibility = gr.Checkbox(
# value=False, label="Show gated/private/deleted models", interactive=True
# )
with gr.Column(min_width=320):
# with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model Types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
# filter_columns_architecture = gr.CheckboxGroup(
# label="Architecture Types",
# choices=[i.value.name for i in ModelArch],
# value=[i.value.name for i in ModelArch],
# interactive=True,
# elem_id="filter-columns-architecture",
# )
filter_domain_specific = gr.CheckboxGroup(
label="Domain Specificity",
choices=["🏥 Clinical models", "Generic models"],
value=["🏥 Clinical models", "Generic models"],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
datasets_leaderboard_df, datasets_original_df = update_df(shown_columns.value, subset="soap")
leaderboard_table = gr.components.Dataframe(
value=datasets_leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.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=datasets_original_df[SOAP_COLS],
headers=SOAP_COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
# filter_columns_architecture
],
leaderboard_table,
)
for selector in [
shown_columns,
filter_columns_type,
filter_domain_specific,
filter_columns_size,
# deleted_models_visibility,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
search_bar,
filter_columns_type,
filter_domain_specific,
filter_columns_size
],
leaderboard_table,
queue=True,
)
with gr.Accordion("💬 Generation templates", open=False):
with gr.Accordion("ACI-Bench Response generation", open=False):
system_prompt, user_prompt = render_generation_templates(task="aci", generation_type="response_generation")
with gr.Accordion("SOAP Notes Response generation", open=False):
system_prompt, user_prompt = render_generation_templates(task="soap", generation_type="response_generation")
with gr.Accordion("Question generation", open=False):
system_prompt, user_prompt = render_generation_templates(task="ce", generation_type="question_generation")
with gr.Accordion("Cross Examination", open=False):
system_prompt, user_prompt = render_generation_templates(task="ce", generation_type="cross_examination")
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=5):
gr.Markdown(LLM_BENCHMARKS_TEXT_1, elem_classes="markdown-text")
gr.HTML(FIVE_PILLAR_DIAGRAM)
gr.Markdown(LLM_BENCHMARKS_TEXT_2, elem_classes="markdown-text")
# gr.HTML(EVALUATION_EXAMPLE_IMG, elem_classes="logo")
# gr.Markdown(LLM_BENCHMARKS_TEXT_2, elem_classes="markdown-text")
# gr.HTML(ENTITY_DISTRIBUTION_IMG, elem_classes="logo")
# gr.Markdown(LLM_BENCHMARKS_TEXT_3, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=6):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Column():
with gr.Accordion(
f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
open=False,
):
with gr.Row():
finished_eval_table = gr.components.Dataframe(
value=finished_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
open=False,
):
with gr.Row():
running_eval_table = gr.components.Dataframe(
value=running_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
open=False,
):
with gr.Row():
pending_eval_table = gr.components.Dataframe(
value=pending_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Row():
gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
model_type = gr.Dropdown(
choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
with gr.Column():
precision = gr.Dropdown(
choices=[i.value.name for i in Precision if i != Precision.Unknown],
label="Precision",
multiselect=False,
value="auto",
interactive=True,
)
weight_type = gr.Dropdown(
choices=[i.value.name for i in WeightType],
label="Weights type",
multiselect=False,
value=WeightType.Original.value.name,
interactive=False,
)
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)", interactive=False)
with gr.Row():
domain_specific_toggle = gr.Checkbox(
label="Domain specific",
value=False,
info="Is your model medically oriented?",
)
chat_template_toggle = gr.Checkbox(
label="Use chat template",
value=False,
info="Is your model a chat model?",
)
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
base_model_name_textbox,
revision_name_textbox,
model_type,
domain_specific_toggle,
chat_template_toggle,
precision,
weight_type
],
submission_result,
)
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(allowed_paths=['./assets/'])
|