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import pathlib
import tempfile
from typing import BinaryIO, Literal
import json
import pandas as pd
import gradio as gr
from datasets import load_dataset, Dataset
from huggingface_hub import upload_file, hf_hub_download
from evaluation import evaluate_problem
from datetime import datetime
import os
from huggingface_hub import HfApi
PROBLEM_TYPES = ["geometrical", "simple_to_build", "mhd_stable"]
TOKEN = os.environ.get("HF_TOKEN")
CACHE_PATH=os.getenv("HF_HOME", ".")
API = HfApi(token=TOKEN)
submissions_repo = "cgeorgiaw/constellaration-submissions"
results_repo = "cgeorgiaw/constellaration-results"
def submit_boundary(
problem_type: Literal["geometrical", "simple_to_build", "mhd_stable"],
boundary_file: BinaryIO,
) -> str:
file_path = boundary_file.name
if not file_path:
return "Error: Uploaded file object does not have a valid file path."
path_obj = pathlib.Path(file_path)
timestamp = datetime.utcnow().isoformat()
with (
path_obj.open("rb") as f_in,
tempfile.NamedTemporaryFile(delete=False, suffix=".json") as tmp_boundary,
):
file_content = f_in.read()
tmp_boundary.write(file_content)
tmp_boundary_path = pathlib.Path(tmp_boundary.name)
# write to dataset
filename = f"{problem_type}/{timestamp.replace(':', '-')}_{problem_type}.json"
record = {
"submission_filename": filename,
"submission_time": timestamp,
"problem_type": problem_type,
"boundary_json": file_content.decode("utf-8"),
"evaluated": False,
}
with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as tmp:
json.dump(record, tmp, indent=2)
tmp.flush()
tmp_name = tmp.name
API.upload_file(
path_or_fileobj=tmp_name,
path_in_repo=filename,
repo_id=submissions_repo,
repo_type="dataset",
commit_message=f"Add submission for {problem_type} at {timestamp}"
)
pathlib.Path(tmp_name).unlink()
# then do eval
local_path = read_boundary(filename)
try:
result = evaluate_problem(problem_type, local_path)
write_results(record, result)
output = str(result)
except Exception as e:
output = f"Error during evaluation:\n{e}"
finally:
tmp_boundary_path.unlink()
return output
def read_boundary(filename):
local_path = hf_hub_download(
repo_id=submissions_repo,
repo_type="dataset",
filename=filename,
)
return local_path
def write_results(record, result):
record.update(result)
record['result_filename'] = record['submission_filename'].strip('.json') + '_results.json'
record['evaluated'] = True
with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as tmp:
json.dump(record, tmp, indent=2)
tmp.flush()
tmp_name = tmp.name
API.upload_file(
path_or_fileobj=tmp_name,
path_in_repo=record['result_filename'],
repo_id=results_repo,
repo_type="dataset",
commit_message=f"Add result data for {record['result_filename']}"
)
pathlib.Path(tmp_name).unlink()
return
def get_leaderboard(problem_type: str):
ds = load_dataset(results_repo, split=problem_type)
filtered = ds.filter(lambda x: x["evaluated"])
if len(filtered) == 0:
return pd.DataFrame(columns=["submission_time", "problem_type", "score"])
df = pd.DataFrame(filtered)
score_field = "score" if "score" in df.columns else "objective" # fallback
df = df.sort_values(by=score_field, ascending=True)
leaderboard = df[["submission_time", "problem_type", score_field]].reset_index(drop=True)
return leaderboard
def gradio_interface() -> gr.Blocks:
with gr.Blocks() as demo:
gr.Markdown(
"""
# Plasma Boundary Evaluation App
Upload your plasma boundary JSON and select the problem type to get your score.
"""
)
with gr.Row():
problem_type = gr.Dropdown(
PROBLEM_TYPES, label="Problem Type", value="geometrical"
)
boundary_file = gr.File(label="Boundary JSON File (.json)")
boundary_file
output = gr.Textbox(label="Evaluation Result", lines=10)
submit_btn = gr.Button("Evaluate")
submit_btn.click(
submit_boundary,
inputs=[problem_type, boundary_file],
outputs=output,
)
with gr.Row():
leaderboard_type = gr.Dropdown(PROBLEM_TYPES, label="Leaderboard Problem Type", value="geometrical")
leaderboard_btn = gr.Button("Load Leaderboard")
leaderboard_df = gr.Dataframe(label="Leaderboard")
leaderboard_btn.click(
lambda pt: get_leaderboard(pt).to_dict(orient="records"),
inputs=[leaderboard_type],
outputs=[leaderboard_df]
)
return demo
if __name__ == "__main__":
gradio_interface().launch()
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