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import pathlib
from pathlib import Path
import tempfile
from typing import BinaryIO, Literal
import json
import pandas as pd
import gradio as gr
from datasets import load_dataset
from huggingface_hub import upload_file, hf_hub_download, list_files_info
from gradio_leaderboard import ColumnFilter, Leaderboard, SelectColumns
from evaluation import evaluate_problem
from datetime import datetime
import os
from submit import submit_boundary
from about import PROBLEM_TYPES, TOKEN, CACHE_PATH, API, submissions_repo, results_repo
from utils import read_boundary, write_results, get_user
from visualize import make_visual
def evaluate_boundary(filename):
print(filename)
local_path = read_boundary(filename)
with Path(local_path).open("r") as f:
raw = f.read()
data_dict = json.loads(raw)
result = evaluate_problem(data_dict['problem_type'], local_path)
write_results(data_dict, result)
return
def make_clickable(name):
link =f'https://huggingface.co/{name}'
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{name}</a>'
def get_leaderboard():
ds = load_dataset(results_repo, split='train')
df = pd.DataFrame(ds)
df.rename(columns={'submission_time': 'submission time', 'problem_type': 'problem type'}, inplace=True)
# df['user'] = df['user'].apply(lambda x: make_clickable(x)).astype(str)
score_field = "score" if "score" in df.columns else "objective" # fallback
df = df.sort_values(by=score_field, ascending=True)
return df
def show_output_box(message):
return gr.update(value=message, visible=True)
def gradio_interface() -> gr.Blocks:
with gr.Blocks() as demo:
with gr.Tabs(elem_classes="tab-buttons"):
with gr.TabItem("Leaderboard", elem_id="boundary-benchmark-tab-table"):
gr.Markdown("# Boundary Design Leaderboard")
Leaderboard(
value=get_leaderboard(),
datatype=['str', 'date', 'str', 'str', 'bool', 'markdown', 'number', 'bool', 'number', 'number', 'str'],
select_columns=["submission time", "feasibility", "score", "problem type", "user"],
search_columns=["submission time", "score", "user"],
hide_columns=["result_filename", "submission_filename", "objective", "minimize_objective", "boundary_json", "evaluated"],
filter_columns=["problem type"],
every=60,
render=True
)
with gr.TabItem("About", elem_id="boundary-benchmark-tab-table"):
gr.Markdown(
"""
# Welcome to the Fusion Challenge!
Upload your plasma boundary JSON and select the problem type to get your score.
"""
)
# dropdown = gr.Dropdown(choices=filenames, label="Choose a file")
# plot_output = gr.Plot()
with gr.TabItem("Submit", elem_id="boundary-benchmark-tab-table"):
gr.Markdown(
"""
# Plasma Boundary Evaluation Submission
Upload your plasma boundary JSON and select the problem type to get your score.
"""
)
user_state = gr.State(value=None)
filename = gr.State(value=None)
eval_state = gr.State(value=None)
gr.LoginButton()
demo.load(get_user, inputs=None, outputs=user_state)
with gr.Row():
problem_type = gr.Dropdown(PROBLEM_TYPES, label="Problem Type")
boundary_file = gr.File(label="Boundary JSON File (.json)")
boundary_file
submit_btn = gr.Button("Evaluate")
message = gr.Textbox(label="Status", lines=1, visible=False)
submit_btn.click(
submit_boundary,
inputs=[problem_type, boundary_file, user_state],
outputs=[message, filename],
).then(
fn=show_output_box,
inputs=[message],
outputs=[message],
).then(
fn=evaluate_boundary,
inputs=[filename],
outputs=[eval_state]
)
return demo
if __name__ == "__main__":
gradio_interface().launch()
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