source/app.py

from __future__ import annotations

import json
from pathlib import Path

import gradio as gr
import plotly.graph_objects as go
import torch
from model import CalibratedDiscreteTransition, NeuralVectorField
from safetensors.torch import load_file
from train import rollout, true_rollout

ARTIFACT_DIR = Path(__file__).resolve().parent / "artifacts" / "neural-ode-pocket"
ODE = NeuralVectorField()
ODE.load_state_dict(load_file(ARTIFACT_DIR / "neural_ode_rk4.safetensors"))
ODE.eval()
DISCRETE = CalibratedDiscreteTransition()
DISCRETE.load_state_dict(
    load_file(ARTIFACT_DIR / "discrete_transition.safetensors")
)
DISCRETE.eval()
REPORT = json.loads((ARTIFACT_DIR / "evaluation.json").read_text(encoding="utf-8"))


@torch.inference_mode()
def phase_portrait(
    initial_x: float,
    initial_velocity: float,
    delta_time: float,
    physical_time: float,
) -> tuple[go.Figure, dict]:
    initial = torch.tensor([[initial_x, initial_velocity]], dtype=torch.float32)
    steps = max(1, int(physical_time / delta_time))
    truth = true_rollout(initial, delta_time, steps)[0]
    ode = rollout(ODE, initial, delta_time, steps, continuous=True)[0]
    discrete = rollout(DISCRETE, initial, delta_time, steps, continuous=False)[0]
    figure = go.Figure()
    figure.add_trace(go.Scatter(x=truth[:, 0], y=truth[:, 1], name="True"))
    figure.add_trace(go.Scatter(x=ode[:, 0], y=ode[:, 1], name="Neural ODE"))
    figure.add_trace(
        go.Scatter(x=discrete[:, 0], y=discrete[:, 1], name="Discrete model")
    )
    figure.update_layout(
        template="plotly_dark",
        title="Van der Pol phase portrait",
        xaxis_title="Position",
        yaxis_title="Velocity",
    )
    metrics = {
        "steps": steps,
        "neural_ode_live_rmse": float((ode - truth).square().mean().sqrt()),
        "discrete_live_rmse": float((discrete - truth).square().mean().sqrt()),
        "verified_unseen_dt_ode_rmse": REPORT["results"]["neural_ode_rk4"][
            "unseen_four_x_timestep"
        ]["trajectory_rmse"],
    }
    return figure, metrics


with gr.Blocks(title="Neural ODE Pocket") as demo:
    gr.Markdown(
        "# Neural ODE Pocket\n"
        "Compare a learned continuous vector field integrated with RK4 against an "
        "exactly parameter-matched discrete transition network."
    )
    with gr.Row():
        initial_x = gr.Slider(-3, 3, value=2.0, step=0.1, label="Initial position")
        initial_v = gr.Slider(-3, 3, value=0.0, step=0.1, label="Initial velocity")
        dt = gr.Slider(0.05, 0.20, value=0.20, step=0.05, label="Timestep")
        duration = gr.Slider(5, 30, value=20, step=1, label="Physical time")
    initial = phase_portrait(2.0, 0.0, 0.20, 20)
    chart = gr.Plot(value=initial[0])
    metrics = gr.JSON(value=initial[1])
    button = gr.Button("Integrate dynamics", variant="primary")
    button.click(
        phase_portrait,
        inputs=[initial_x, initial_v, dt, duration],
        outputs=[chart, metrics],
    )


if __name__ == "__main__":
    demo.launch()