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__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']
import os
import gradio as gr
import pandas as pd
import json
import tempfile
from constants import *
from huggingface_hub import Repository
print(gr.__version__)
print(pd.__version__)
HF_TOKEN = os.environ.get("HF_TOKEN")
global data_component, filter_component
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_type: str,
model_link: str,
model_size: str,
LLM_type: str,
LLM_name_textbox: str,
):
if input_file is None:
return "Error! Empty file!"
upload_data=json.loads(input_file)
submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
submission_repo.git_pull()
csv_data = pd.read_csv(CSV_DIR)
if LLM_type == 'Other':
LLM_name = LLM_name_textbox
else:
LLM_name = LLM_type
if revision_name_textbox == '':
col = csv_data.shape[0]
model_name = model_name_textbox
else:
model_name = revision_name_textbox
model_name_list = csv_data['Model']
name_list = [name.split(']')[0][1:] 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_type,
model_name,
LLM_name
]
for key in TASK_INFO:
if key in upload_data:
new_data.append(upload_data[key])
else:
new_data.append(0)
csv_data.loc[col] = new_data
csv_data = csv_data.to_csv(CSV_DIR, index=False)
submission_repo.push_to_hub()
return 0
def get_baseline_df():
submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
submission_repo.git_pull()
df = pd.read_csv(CSV_DIR)
df = df.sort_values(by="Avg", ascending=False)
present_columns = MODEL_INFO + checkbox_group.value
df = df[present_columns]
return df
def get_all_df():
submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
submission_repo.git_pull()
df = pd.read_csv(CSV_DIR)
df = df.sort_values(by="Avg", 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("π MVBench", elem_id="mvbench-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",
)
gr.Markdown(
TABLE_INTRODUCTION
)
# selection for column part:
checkbox_group = gr.CheckboxGroup(
choices=TASK_INFO,
value=AVG_INFO,
label="Evaluation Dimension",
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_filter_model_size_method_change(selected_columns):
updated_data = get_all_df()
# columns:
selected_columns = [item for item in TASK_INFO if item in selected_columns]
present_columns = MODEL_INFO + selected_columns
print("selected_columns",'|'.join(selected_columns))
updated_data = updated_data[present_columns]
updated_data = updated_data.sort_values(by=selected_columns[0], 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.value
checkbox_group.change(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)
# table 2
with gr.TabItem("π About", elem_id="mvbench-tab-table", id=2):
gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
# table 3
with gr.TabItem("π Submit here! ", elem_id="mvbench-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("# βοΈβ¨ 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="LLaMA-7B"
)
revision_name_textbox = gr.Textbox(
label="Revision Model Name", placeholder="LLaMA-7B"
)
model_type = gr.Dropdown(
choices=[
"LLM",
"ImageLLM",
"VideoLLM",
"Other",
],
label="Model type",
multiselect=False,
value="ImageLLM",
interactive=True,
)
with gr.Column():
LLM_type = gr.Dropdown(
choices=["Vicuna-7B", "Flan-T5-XL", "LLaMA-7B", "InternLM-7B", "Other"],
label="LLM type",
multiselect=False,
value="LLaMA-7B",
interactive=True,
)
LLM_name_textbox = gr.Textbox(
label="LLM model (for Other)",
placeholder="LLaMA-13B"
)
model_link = gr.Textbox(
label="Model Link", placeholder="https://huggingface.co/decapoda-research/llama-7b-hf"
)
model_size = gr.Textbox(
label="Model size", placeholder="7B(Input content format must be 'number+B' or '-')"
)
with gr.Column():
input_file = gr.components.File(label = "Click to Upload a json File", file_count="single", 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_type,
model_link,
model_size,
LLM_type,
LLM_name_textbox,
],
)
def refresh_data():
value1 = get_baseline_df()
return value1
with gr.Row():
data_run = gr.Button("Refresh")
data_run.click(
refresh_data, outputs=[data_component]
)
block.launch() |