__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']
import gradio as gr
import pandas as pd
import re
import pdb
import tempfile
import os
from constants import *
from src.compute import compute_scores
global data_component, filter_component
from huggingface_hub import HfApi
hf_token = os.getenv('HF_TOKEN')
api = HfApi(token=hf_token)
def validate_model_size(s):
pattern = r'^\d+B$|^-$'
if re.match(pattern, s):
return s
else:
return '-'
def upload_file(files):
file_paths = [file.name for file in files]
return file_paths
def add_new_eval(
input_file,
model_name_textbox: str,
revision_name_textbox: str,
model_link: str,
model_type: str,
model_size: str,
num_frame: str,
notes: str,
):
if input_file is None:
return "Error! Empty file!"
else:
# model_size = validate_model_size(model_size)
input_file = compute_scores(input_file)
input_data = input_file[1]
input_data = [float(i) for i in input_data]
csv_data = pd.read_csv(CSV_DIR)
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 + ')'
# add new data
new_data = [
model_name,
model_type,
model_size,
num_frame,
input_data[0],
input_data[1],
input_data[2],
input_data[3],
input_data[4],
input_data[5],
input_data[6],
input_data[7],
input_data[8],
input_data[9],
input_data[10],
input_data[11],
input_data[12],
input_data[13],
input_data[14],
input_data[15],
input_data[16],
input_data[17],
input_data[18],
input_data[19],
input_data[20],
input_data[21],
input_data[22],
input_data[23],
input_data[24],
input_data[25],
input_data[26],
input_data[27],
input_data[28],
input_data[29],
notes,
]
# print(len(new_data), col)
# print(csv_data.loc[col])
# print(model_name, model_type, model_size)
csv_data.loc[col] = new_data
# with open(f'./file/{model_name}.json','w' ,encoding='utf-8') as f:
# json.dump(new_data, f)
csv_data.to_csv(CSV_DIR, index=False)
# push newly added result
api.upload_file(
path_or_fileobj=CSV_DIR,
path_in_repo=CSV_DIR,
repo_id="lyx97/TempCompass",
repo_type="space",
)
return 0
def get_baseline_df():
# pdb.set_trace()
df = pd.read_csv(CSV_DIR)
df = df.sort_values(by="Avg. All", ascending=False)
present_columns = MODEL_INFO + checkbox_group.value
df = df[present_columns]
return df
def get_all_df():
df = pd.read_csv(CSV_DIR)
df = df.sort_values(by="Avg. All", ascending=False)
return df
block = gr.Blocks()
with block:
gr.Markdown(
LEADERBORAD_INTRODUCTION
)
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 TempCompass Benchmark", elem_id="video-benchmark-tab-table", id=0):
gr.Markdown(
TABLE_INTRODUCTION
)
# selection for column part:
checkbox_group = gr.CheckboxGroup(
choices=TASK_INFO,
value=AVG_INFO,
label="Select options",
interactive=True,
)
# 创建数据帧组件
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):
# pdb.set_trace()
selected_columns = [item for item in TASK_INFO 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
print(updated_headers)
print([COLUMN_NAMES.index(x) for x in updated_headers])
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,
)
# pdb.set_trace()
return filter_component.constructor_args['value']
# 将复选框组关联到处理函数
checkbox_group.change(fn=on_checkbox_group_change, inputs=checkbox_group, outputs=data_component)
'''
# table 2
with gr.TabItem("📝 About", elem_id="seed-benchmark-tab-table", id=2):
gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
'''
# table 3
with gr.TabItem("🚀 Submit here! ", elem_id="seed-benchmark-tab-table", id=3):
# gr.Markdown(LEADERBORAD_INTRODUCTION, elem_classes="markdown-text")
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="Video-LLaVA-7B"
)
revision_name_textbox = gr.Textbox(
label="Revision Model Name", placeholder="Video-LLaVA-7B"
)
model_link = gr.Textbox(
label="Model Link", placeholder="https://huggingface.co/LanguageBind/Video-LLaVA-7B"
)
model_type = gr.Dropdown(
choices=[
"LLM",
"ImageLLM",
"VideoLLM",
"Other",
],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
model_size = gr.Textbox(
label="Model size", placeholder="7B(Input content format must be 'number+B' or '-', default is '-')"
)
num_frame = gr.Textbox(
label="Frames", placeholder="The number of frames sampled from video, default is '-')"
)
notes = gr.Textbox(
label="Notes", placeholder="Other details of the model or evaluation, e.g., which answer prompt is used."
)
with gr.Column():
input_file = gr.File(label="Click to Upload a json File", type='binary')
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
inputs=[
input_file,
model_name_textbox,
revision_name_textbox,
model_link,
model_type,
model_size,
num_frame,
notes,
],
# outputs = submission_result,
)
with gr.Row():
data_run = gr.Button("Refresh")
data_run.click(
get_baseline_df, outputs=data_component
)
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,
)
# block.load(get_baseline_df, outputs=data_title)
block.launch()