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import gradio as gr
import pandas as pd
from themes import Seafoam
from load_data import load_main_table
from constants import BANNER, CITATION_TEXT, css, js_code, all_task_types, js_light
TYPES = ["number", "markdown", "number"]
MAIN_TABLE_COLS = ['Model', 'Language', 'Average Toxicity', 'Expected Maximum Toxicity', 'Empirical Probability']
df_main = load_main_table()
available_models = df_main['Model'].unique()
MODEL_SIZE = list(df_main['Model Size'].unique())
MODEL_TYPE = list(df_main['Model Type'].unique())
LANGAUGES = list(df_main['Language'].unique())
MODEL_FAMILY = list(df_main['Model Family'].unique())
with open("_intro.md", "r") as f:
INTRO_MD = f.read()
with open("_about_us.md", "r") as f:
ABOUT_MD = f.read()
with open("_header.md", "r") as f:
HEADER_MD = f.read()
with open("_metrics.md", "r") as f:
METRIC_MD = f.read()
with gr.Blocks(theme=gr.themes.Soft(), css=css, js=js_light) as demo:
gr.HTML(BANNER, elem_id="banner")
# gr.HTML("<img src='file/image.png' alt='image One'>")
gr.Markdown(HEADER_MD, elem_classes="markdown-text")
gr.Image("data/ptp.png")
gr.Markdown(f"**Version**: PTP-Small | **# Examples**: 85K | **# Models**: {len(available_models)}", elem_classes="markdown-text")
gr.Markdown(METRIC_MD, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 Multilingual Leaderboard", elem_id="od-benchmark-tab-table", id=0, elem_classes="subtab"):
print(df_main.head())
mling_df = df_main.loc[df_main['Multilingual']==True, MAIN_TABLE_COLS].copy()
del mling_df['Language']
mling_df = mling_df.groupby("Model").agg('mean').reset_index().round(3)
mling_df = mling_df.sort_values(by="Average Toxicity")
print(mling_df.head())
ablation_table = gr.components.Dataframe(
value=mling_df,
datatype=TYPES,
height=1000,
elem_id="mling-table",
interactive=False,
visible=True,
min_width=60,
)
with gr.TabItem("📊 Ablation Results", elem_id="od-benchmark-tab-table", id=1, elem_classes="subtab"):
with gr.Row():
language = gr.CheckboxGroup(
choices=LANGAUGES,
value=LANGAUGES,
label='Language',
interactive=True
)
with gr.Row():
model_family = gr.CheckboxGroup(
choices=MODEL_FAMILY,
value=MODEL_FAMILY,
label='Model Family',
interactive=True
)
with gr.Row():
model_size = gr.CheckboxGroup(
choices=MODEL_SIZE,
value=MODEL_SIZE,
label='Model Size',
interactive=True,
)
model_type = gr.CheckboxGroup(
choices=MODEL_TYPE,
value=MODEL_TYPE,
label='Model Type',
interactive=True
)
ablation_table = gr.components.Dataframe(
value=df_main[MAIN_TABLE_COLS],
datatype=TYPES,
height=500,
elem_id="full-table",
interactive=False,
visible=True,
min_width=60,
)
def filter_df(model_size, model_type, language, model_family):
df = df_main.copy()
print(df.isnull().sum())
df = df[df['Model Type'].isin(model_type)]
df = df[df['Model Size'].isin(model_size)]
df = df[df['Language'].isin(language)]
df = df[df['Model Family'].isin(model_family)]
df = df.sort_values(by="Average Toxicity")
assert (df.isnull().sum().sum())==0
comp = gr.components.DataFrame(
value=df[MAIN_TABLE_COLS],
datatype=TYPES,
interactive=False,
visible=True)
return comp
for cbox in [model_size, model_type, language, model_family]:
cbox.change(fn=filter_df, inputs=[model_size, model_type, language, model_family], outputs=ablation_table)
with gr.TabItem("📮 About Us", elem_id="od-benchmark-tab-table", id=2):
gr.Markdown(ABOUT_MD, elem_classes="markdown-text")
with gr.Row():
with gr.Accordion("📙 Citation", open=False, elem_classes="accordion-label"):
gr.Textbox(
value=CITATION_TEXT,
lines=7,
label="Copy the BibTeX snippet to cite this source",
elem_id="citation-button",
show_copy_button=True)
if __name__ == '__main__':
demo.launch(share=True, allowed_paths=["."]) |