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import os.path
import gradio as gr
import numpy as np
import pandas as pd
from constants import *
def get_download_link_model(task, dataset, example):
_task_path = TASK_PATH_MAPPING[task]
_dataset_path = DATASET_PATH_MAPPING[dataset]
_example_path = EXAMPLE_PATH_MAPPING[example]
return os.path.join("data", _task_path, _dataset_path, "weight", f"{_example_path}.zip")
def get_download_link_json(task, dataset, example):
_task_path = TASK_PATH_MAPPING[task]
_dataset_path = DATASET_PATH_MAPPING[dataset]
_example_path = EXAMPLE_PATH_MAPPING[example]
if _task_path == "common":
return os.path.join("data", _task_path, _dataset_path, "json", f"{_example_path}.jsonl")
else:
return os.path.join("data", _task_path, _dataset_path, "json", f"{_example_path}.json")
def get_data(task, dataset, example):
_task_path = TASK_PATH_MAPPING[task]
_dataset_path = DATASET_PATH_MAPPING[dataset]
_example_path = EXAMPLE_PATH_MAPPING[example]
csv_file = os.path.join("data", _task_path, _dataset_path, "csv", f"{_example_path}.csv")
if not os.path.exists(csv_file):
return None
read_data = pd.read_csv(csv_file)
data = pd.DataFrame(columns=COLUMN_NAMES)
if _task_path == "coding":
for index, row in read_data.iterrows():
data = data._append({
"Prompt": row["prompt"],
"Pass@1": round(float(row["pass@1"]) * 100, 3),
"Pass@5": round(float(row["pass@5"]) * 100, 3),
"Pass@10": round(float(row["pass@10"]) * 100, 3),
"Correctness": "N/A"
}, ignore_index=True)
elif _task_path == "common":
for index, row in read_data.iterrows():
data = data._append({
"Prompt": row["prompt"],
"Pass@1": None,
"Pass@5": None,
"Pass@10": None,
"Correctness": "✅" if row["correctness"] else "❌"
}, ignore_index=True)
elif _task_path == "math":
for index, row in read_data.iterrows():
data = data._append({
"Prompt": row["prompt"],
"Pass@1": None,
"Pass@5": None,
"Pass@10": None,
"Correctness": "✅" if row["correctness"] else "❌"
}, ignore_index=True)
return data
with gr.Blocks() as demo_board:
gr.HTML(DND_HEADER)
gr.Markdown(DND_INTRODUCTION)
task = gr.Radio(
label="Task",
choices=TASK_LIST,
value=TASK_LIST[0],
interactive=True,
)
dataset = gr.Radio(
label="Dataset",
choices=TASK_DATASET_LIST[task.value],
value=TASK_DATASET_LIST[task.value][0],
interactive=True
)
example = gr.Radio(
label="Example",
choices=EXAMPLE_LIST,
value=EXAMPLE_LIST[0],
interactive=True,
)
board = gr.components.Dataframe(
value=get_data(task.value, dataset.value, example.value),
column_widths=["60%", "10%", "10%", "10%", "10%"],
headers=COLUMN_NAMES,
type="pandas",
datatype=DATA_TITLE_TYPE,
interactive=False,
visible=True,
max_height=500,
)
task.change(lambda t: gr.Radio(
label="Dataset",
choices=TASK_DATASET_LIST[t],
value=TASK_DATASET_LIST[t][0],
interactive=True,
), inputs=[task], outputs=dataset)
for component in [task, dataset, example]:
component.change(lambda t, d, e: gr.components.Dataframe(
value=get_data(t, d, e),
column_widths=["60%", "10%", "10%", "10%", "10%"],
headers=COLUMN_NAMES,
type="pandas",
datatype=DATA_TITLE_TYPE,
interactive=False,
visible=True,
max_height=500,
), inputs=[task, dataset, example], outputs=board)
with gr.Row():
json_downloader = gr.DownloadButton("Download JSON", visible=True)
model_downloader = gr.DownloadButton("Download Model", visible=True)
json_downloader.click(
fn=get_download_link_json,
inputs=[task, dataset, example],
outputs=json_downloader,
)
model_downloader.click(
fn=get_download_link_model,
inputs=[task, dataset, example],
outputs=model_downloader,
)
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
elem_id="citation-button",
lines=6,
show_copy_button=True,
)
demo_board.launch()
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