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__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']
import gradio as gr
import pandas as pd
import json
import io
from constants import *
global data_component, data_component_150, filter_component
def upload_file(files):
file_paths = [file.name for file in files]
return file_paths
def compute_scores(input_data):
return [None, [
input_data["Average_MTScore"],
input_data["Average_CHScore"],
input_data["Average_GPT4o-MTScore"],
input_data["Average_UMT-FVD"],
input_data["Average_UMTScore"]
]]
def add_new_eval(
input_file,
model_name_textbox: str,
revision_name_textbox: str,
backbone_type_dropdown: str,
model_link: str,
):
if input_file is None:
return "Error! Empty file!"
else:
input_json = json.load(io.BytesIO(input_file))
if model_name_textbox not in input_json:
return f"Error! Model '{model_name_textbox}' not found in input file!"
selected_model_data = input_json[model_name_textbox]
scores = compute_scores(selected_model_data)
input_data = scores[1]
input_data = [float(i) for i in input_data]
csv_data = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH)
if revision_name_textbox == '':
col = csv_data.shape[0]
model_name = model_name_textbox
name_list = [name.split(']')[0][1:] if name.endswith(')') else name for name in csv_data['Model']]
assert model_name not in name_list
else:
model_name = revision_name_textbox
model_name_list = csv_data['Model']
name_list = [name.split(']')[0][1:] if name.endswith(')') else name for name in model_name_list]
if revision_name_textbox not in name_list:
col = csv_data.shape[0]
else:
col = name_list.index(revision_name_textbox)
if model_link == '':
model_name = model_name # no url
else:
model_name = '[' + model_name + '](' + model_link + ')'
backbone = backbone_type_dropdown
new_data = [
model_name,
backbone,
input_data[3],
input_data[4],
input_data[0],
input_data[1],
input_data[2],
]
csv_data.loc[col] = new_data
csv_data.to_csv(CSV_DIR_CHRONOMAGIC_BENCH, index=False)
return "Evaluation successfully submitted!"
def add_new_eval_150(
input_file,
model_name_textbox: str,
revision_name_textbox: str,
backbone_type_dropdown: str,
model_link: str,
):
if input_file is None:
return "Error! Empty file!"
else:
input_json = json.load(io.BytesIO(input_file))
if model_name_textbox not in input_json:
return f"Error! Model '{model_name_textbox}' not found in input file!"
selected_model_data = input_json[model_name_textbox]
scores = compute_scores(selected_model_data)
input_data = scores[1]
input_data = [float(i) for i in input_data]
csv_data = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH_150)
if revision_name_textbox == '':
col = csv_data.shape[0]
model_name = model_name_textbox
name_list = [name.split(']')[0][1:] if name.endswith(')') else name for name in csv_data['Model']]
assert model_name not in name_list
else:
model_name = revision_name_textbox
model_name_list = csv_data['Model']
name_list = [name.split(']')[0][1:] if name.endswith(')') else name for name in model_name_list]
if revision_name_textbox not in name_list:
col = csv_data.shape[0]
else:
col = name_list.index(revision_name_textbox)
if model_link == '':
model_name = model_name # no url
else:
model_name = '[' + model_name + '](' + model_link + ')'
backbone = backbone_type_dropdown
new_data = [
model_name,
backbone,
input_data[3],
input_data[4],
input_data[0],
input_data[1],
input_data[2],
]
csv_data.loc[col] = new_data
csv_data.to_csv(CSV_DIR_CHRONOMAGIC_BENCH_150, index=False)
return "Evaluation (150) successfully submitted!"
def get_baseline_df():
df = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH)
df = df.sort_values(by="MTScore↑", ascending=False)
present_columns = MODEL_INFO + checkbox_group.value
df = df[present_columns]
return df
def get_baseline_df_150():
df = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH_150)
df = df.sort_values(by="MTScore↑", ascending=False)
present_columns = MODEL_INFO + checkbox_group_150.value
df = df[present_columns]
return df
def get_all_df():
df = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH)
df = df.sort_values(by="MTScore↑", ascending=False)
return df
def get_all_df_150():
df = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH_150)
df = df.sort_values(by="MTScore↑", ascending=False)
return df
block = gr.Blocks()
with block:
gr.Markdown(
LEADERBORAD_INTRODUCTION
)
with gr.Tabs(elem_classes="tab-buttons") as tabs:
# table 1
with gr.TabItem("🏅 ChronoMagic-Bench", elem_id="ChronoMagic-Bench-tab-table", id=0):
with gr.Row():
with gr.Accordion("Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
elem_id="citation-button",
show_copy_button=True
)
gr.Markdown(
TABLE_INTRODUCTION
)
checkbox_group = gr.CheckboxGroup(
choices=ALL_RESULTS,
value=SELECTED_RESULTS,
label="Select options",
interactive=True,
)
data_component = gr.components.Dataframe(
value=get_baseline_df,
headers=COLUMN_NAMES,
type="pandas",
datatype=DATA_TITILE_TYPE,
interactive=False,
visible=True,
)
def on_checkbox_group_change(selected_columns):
selected_columns = [item for item in ALL_RESULTS if item in selected_columns]
present_columns = MODEL_INFO + selected_columns
updated_data = get_all_df()[present_columns]
updated_data = updated_data.sort_values(by=present_columns[1], ascending=False)
updated_headers = present_columns
update_datatype = [DATA_TITILE_TYPE[COLUMN_NAMES.index(x)] for x in updated_headers]
filter_component = gr.components.Dataframe(
value=updated_data,
headers=updated_headers,
type="pandas",
datatype=update_datatype,
interactive=False,
visible=True,
)
return filter_component
checkbox_group.change(fn=on_checkbox_group_change, inputs=checkbox_group, outputs=data_component)
# table 2
with gr.TabItem("🏅 ChronoMagic-Bench-150", elem_id="ChronoMagic-Bench-150-tab-table", id=1):
with gr.Row():
with gr.Accordion("Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
elem_id="citation-button",
show_copy_button=True
)
gr.Markdown(
TABLE_INTRODUCTION
)
checkbox_group_150 = gr.CheckboxGroup(
choices=ALL_RESULTS,
value=SELECTED_RESULTS_150,
label="Select options",
interactive=True,
)
data_component_150 = gr.components.Dataframe(
value=get_baseline_df_150,
headers=COLUMN_NAMES,
type="pandas",
datatype=DATA_TITILE_TYPE,
interactive=False,
visible=True,
)
def on_checkbox_group_150_change(selected_columns):
selected_columns = [item for item in ALL_RESULTS if item in selected_columns]
present_columns = MODEL_INFO + selected_columns
updated_data = get_all_df_150()[present_columns]
updated_data = updated_data.sort_values(by=present_columns[1], ascending=False)
updated_headers = present_columns
update_datatype = [DATA_TITILE_TYPE[COLUMN_NAMES.index(x)] for x in updated_headers]
filter_component = gr.components.Dataframe(
value=updated_data,
headers=updated_headers,
type="pandas",
datatype=update_datatype,
interactive=False,
visible=True,
)
return filter_component
checkbox_group_150.change(fn=on_checkbox_group_150_change, inputs=checkbox_group_150, outputs=data_component_150)
# table 3
with gr.TabItem("🚀 Submit here! ", elem_id="seed-benchmark-tab-table", id=2):
with gr.Row():
gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
with gr.Row():
gr.Markdown("# ✉️✨ Submit your model evaluation json file here!", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(
label="Model name", placeholder="MagicTime"
)
revision_name_textbox = gr.Textbox(
label="Revision Model Name", placeholder="MagicTime"
)
backbone_type_dropdown = gr.Dropdown(
label="Backbone Type",
choices=["DiT", "U-Net"],
value="DiT"
)
model_link = gr.Textbox(
label="Model Link", placeholder="https://github.com/PKU-YuanGroup/MagicTime"
)
with gr.Column():
input_file = gr.File(label="Click to Upload a json File", type='binary')
submit_button = gr.Button("Submit Eval (ChronoMagic-Bench)")
submit_button_150 = gr.Button("Submit Eval (ChronoMagic-Bench-150)")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
inputs=[
input_file,
model_name_textbox,
revision_name_textbox,
backbone_type_dropdown,
model_link,
],
outputs=submission_result,
)
submit_button_150.click(
add_new_eval_150,
inputs=[
input_file,
model_name_textbox,
revision_name_textbox,
backbone_type_dropdown,
model_link,
],
outputs = submission_result,
)
with gr.Row():
data_run = gr.Button("Refresh")
data_run.click(
get_baseline_df, outputs=data_component
)
data_run.click(
get_baseline_df_150, outputs=data_component_150
)
block.launch() |