first commit

Dockerfile

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+FROM python:3.10-slim
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    cmake \
+    libnetcdf-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN useradd -m -u 1000 user
+USER user
+
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+RUN pip install --no-cache-dir --upgrade pip
+COPY --chown=user . $HOME/app
+
+RUN pip install -r requirements.txt
+
+EXPOSE 7860
+ENV GRADIO_SERVER_NAME="0.0.0.0"
+
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+import plotly.graph_objects as go
+from huggingface_hub import hf_hub_download
+from datasets import load_dataset
+import pathlib
+import json
+import pandas as pd
+from evaluation import load_boundary, load_boundaries
+from constellaration import forward_model, initial_guess
+from constellaration.boozer import boozer
+from constellaration.utils import (
+    file_exporter,
+    visualization,
+    visualization_utils,
+)
+
+
+organization = 'proxima-fusion'
+results_repo = f'{organization}/constellaration-bench-results'
+
+def read_result_from_hub(filename):
+    local_path = hf_hub_download(
+        repo_id=results_repo,
+        repo_type="dataset",
+        filename=filename,
+    )
+    return local_path
+
+def make_visual(boundary):
+    vis = visualization.plot_surface(boundary)
+    return vis
+
+def gradio_interface() -> gr.Blocks:
+    with gr.Blocks() as demo:
+        gr.Markdown("""
+                    # Welcome to the ConStellaration Boundary Explorer!
+
+                    ### Here, you can visualize submissions to the ConStellaration Leaderboard, generate and visualize new random boundaries, or upload and visualize your own!
+                    """)
+        
+        ds = load_dataset(results_repo, split='train')
+        full_df = pd.DataFrame(ds)
+        filenames = full_df['result_filename'].to_list()
+        with gr.Row():
+            with gr.Column():
+                dropdown = gr.Dropdown(choices=filenames, label="Choose a leaderboard entry", value=filenames[0])
+                rld_btn = gr.Button(value="Reload")
+
+            with gr.Column():
+                plot = gr.Plot()
+
+        def get_boundary_vis(selected_file):
+
+            row = full_df[full_df['result_filename'] == selected_file].iloc[0]
+
+            if row['problem_type'] == 'mhd_stable':
+                raise gr.Error("Sorry this isn't implemented for mhd_stable submissions yet!")
+            else:
+                boundary = load_boundary(row['boundary_json'])
+
+            vis = make_visual(boundary)
+            return vis
+
+        demo.load(get_boundary_vis, dropdown, plot)
+        rld_btn.click(get_boundary_vis, dropdown, plot)
+        
+
+    return demo
+
+
+if __name__ == "__main__":
+    gradio_interface().launch()

evaluation.py

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+import json
+from pathlib import Path
+
+from constellaration import problems
+from constellaration.geometry import surface_rz_fourier
+
+PROBLEM_TYPES = ["geometrical", "simple_to_build", "mhd_stable"]
+
+def load_boundary(data: str) -> surface_rz_fourier.SurfaceRZFourier:
+    return surface_rz_fourier.SurfaceRZFourier.model_validate_json(data)
+
+def load_boundaries(data: str) -> list[surface_rz_fourier.SurfaceRZFourier]:
+    data_json = json.loads(data)
+    return [
+        surface_rz_fourier.SurfaceRZFourier.model_validate_json(b) for b in data_json
+    ]
+
+def evaluate_problem(
+    problem_type: str, input_file: str
+) -> problems.EvaluationSingleObjective | problems.EvaluationMultiObjective:
+    with Path(input_file).open("r") as f:
+        raw = f.read()
+        data_dict = json.loads(raw)
+        data = data_dict['boundary_json']
+
+    print("Starting evaluation.")
+
+    match problem_type:
+        case "geometrical":
+            boundary = load_boundary(data)
+            result = problems.GeometricalProblem().evaluate(boundary)
+        case "simple_to_build":
+            boundary = load_boundary(data)
+            result = problems.SimpleToBuildQIStellarator().evaluate(boundary)
+        case "mhd_stable":
+            boundaries = load_boundaries(data)
+            result = problems.MHDStableQIStellarator().evaluate(boundaries)
+        case _:
+            raise ValueError(f"Unknown problem type: {problem_type}")
+        
+    print("Finished evaluation.")
+    return result

requirements.txt

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+constellaration==0.2.1
+gradio
+datasets
+huggingface_hub
+gradio-leaderboard
+plotly