Main_app_trame_final.py

# ========= Headless / Offscreen safety (before any VTK import) =========
import os
os.environ.setdefault("VTK_DEFAULT_RENDER_WINDOW_OFFSCREEN", "1")
os.environ.setdefault("LIBGL_ALWAYS_SOFTWARE", "1")
os.environ.setdefault("MESA_LOADER_DRIVER_OVERRIDE", "llvmpipe")
os.environ.setdefault("MESA_GL_VERSION_OVERRIDE", "3.3")
os.environ.setdefault("DISPLAY", "")

# ========= Core setup =========
import shutil, time, tempfile, json, base64, threading, re, html as _html, asyncio
import numpy as np
import torch
import pyvista as pv
from scipy.spatial import cKDTree
from vtk.util import numpy_support as nps
import matplotlib.cm as cm

from omegaconf import OmegaConf
from huggingface_hub import hf_hub_download
from accelerate import Accelerator
from accelerate.utils import DistributedDataParallelKwargs
import pickle
from sklearn.metrics import pairwise_distances
from train import get_single_latent
from sklearn.neighbors import NearestNeighbors

from utils.app_utils2 import (
    create_visualization_points,
    create_visualization_stl,
    camera_from_bounds,
    bounds_from_points,
    convert_vtp_to_glb,
    convert_vtp_to_stl,
    time_function,
    print_timing,
    mesh_get_variable,
    mph_to_ms,
    get_boundary_conditions_text,
    compute_confidence_score,
    compute_cosine_score,
    decimate_mesh,
)

# ========= trame =========
from trame.app import TrameApp
from trame.decorators import change
from trame.ui.vuetify3 import SinglePageLayout
from trame.widgets import vuetify3 as v3, html
from trame_vtk.widgets.vtk import VtkRemoteView

# ========= VTK =========
from vtkmodules.vtkRenderingCore import (
    vtkRenderer,
    vtkRenderWindow,
    vtkPolyDataMapper,
    vtkActor,
    vtkRenderWindowInteractor,
)
from vtkmodules.vtkRenderingAnnotation import vtkScalarBarActor
from vtkmodules.vtkIOGeometry import vtkSTLReader
from vtkmodules.vtkFiltersCore import vtkTriangleFilter
from vtkmodules.vtkCommonCore import vtkLookupTable
from vtkmodules.vtkInteractionStyle import vtkInteractorStyleTrackballCamera

# ========= Writable paths / caches =========
DATA_DIR = os.path.join(tempfile.gettempdir(), "appdata")
os.makedirs(DATA_DIR, exist_ok=True)
os.environ.setdefault("MPLCONFIGDIR", DATA_DIR)

GEOM_DIR = os.path.join(DATA_DIR, "geometry")
SOLN_DIR = os.path.join(DATA_DIR, "solution")
WEIGHTS_DIR = os.path.join(DATA_DIR, "weights")
for d in (GEOM_DIR, SOLN_DIR, WEIGHTS_DIR):
    os.makedirs(d, exist_ok=True)

HF_DIR = os.path.join(DATA_DIR, "hf_home")
os.environ.setdefault("HF_HOME", HF_DIR)
os.environ.setdefault("HUGGINGFACE_HUB_CACHE", HF_DIR)
os.environ.setdefault("TRANSFORMERS_CACHE", HF_DIR)
os.makedirs(HF_DIR, exist_ok=True)
for p in (GEOM_DIR, SOLN_DIR, WEIGHTS_DIR, HF_DIR):
    if not os.access(p, os.W_OK):
        raise RuntimeError(f"Not writable: {p}")

# ========= Auto-decimation ladder =========
def auto_target_reduction(num_cells: int) -> float:
    if num_cells <= 10_000:
        return 0.0
    elif num_cells <= 20_000:
        return 0.2
    elif num_cells <= 50_000:
        return 0.4
    elif num_cells <= 100_000:
        return 0.5
    elif num_cells <= 500_000:
        return 0.6
    elif num_cells < 1_000_000:
        return 0.8
    else:
        return 0.9

# ========= Registry / choices =========
REGISTRY = {
    "Incompressible flow inside artery": {
        "repo_id": "ansysresearch/pretrained_models",
        "config": "configs/app_configs/Incompressible flow inside artery/config.yaml",
        "model_attr": "ansysLPFMs",
        "checkpoints": {"best_val": "ckpt_artery.pt"},
        "out_variable": ["pressure", "x_velocity", "y_velocity", "z_velocity"],
    },
    "Vehicle crash analysis": {
        "repo_id": "ansysresearch/pretrained_models",
        "config": "configs/app_configs/Vehicle crash analysis/config.yaml",
        "model_attr": "ansysLPFMs",
        "checkpoints": {"best_val": "ckpt_vehiclecrash.pt"},
        "out_variable": [
            "impact_force",
            "deformation",
            "energy_absorption",
            "x_displacement",
            "y_displacement",
            "z_displacement",
        ],
    },
    "Compressible flow over plane": {
        "repo_id": "ansysresearch/pretrained_models",
        "config": "configs/app_configs/Compressible flow over plane/config.yaml",
        "model_attr": "ansysLPFMs",
        "checkpoints": {"best_val": "ckpt_plane_transonic_v3.pt"},
        "out_variable": ["pressure"],
    },
    "Incompressible flow over car": {
        "repo_id": "ansysresearch/pretrained_models",
        "config": "configs/app_configs/Incompressible flow over car/config.yaml",
        "model_attr": "ansysLPFMs",
        "checkpoints": {"best_val": "ckpt_cadillac_v3.pt"},
        "out_variable": ["pressure"],
    },
}

def variables_for(dataset: str):
    spec = REGISTRY.get(dataset, {})
    ov = spec.get("out_variable")
    if isinstance(ov, str):
        return [ov]
    if isinstance(ov, (list, tuple)):
        return list(ov)
    return list(spec.get("checkpoints", {}).keys())

ANALYSIS_TYPE_MAPPING = {
    "External flow": ["Incompressible flow over car", "Compressible flow over plane"],
    "Internal flow": ["Incompressible flow inside artery"],
    "Crash analysis": ["Vehicle crash analysis"],
}
ANALYSIS_TYPE = list(ANALYSIS_TYPE_MAPPING.keys())
DEFAULT_ANALYSIS_TYPE = "External flow"
DEFAULT_DATASET = "Incompressible flow over car"
VAR_CHOICES0 = variables_for(DEFAULT_DATASET)
DEFAULT_VARIABLE = VAR_CHOICES0[0] if VAR_CHOICES0 else None

# ========= Simple cache =========
class GeometryCache:
    def __init__(self):
        self.original_mesh = None   # uploaded, cleaned (normals), BEFORE user re-decimation
        self.current_mesh = None    # what the app is actually using right now

GEOMETRY_CACHE = GeometryCache()

# ========= Model store =========
class ModelStore:
    def __init__(self):
        self._cache = {}

    def _build(self, dataset: str, progress_cb=None):
        def tick(x):
            if progress_cb:
                try:
                    progress_cb(int(x))
                except:
                    pass

        if dataset in self._cache:
            tick(12)
            return self._cache[dataset]

        print(f"🔧 Building model for {dataset}")
        start_time = time.time()
        try:
            spec = REGISTRY[dataset]
            repo_id = spec["repo_id"]
            ckpt_name = spec["checkpoints"]["best_val"]

            tick(6)
            t0 = time.time()
            ckpt_path_hf = hf_hub_download(
                repo_id=repo_id,
                filename=ckpt_name,
                repo_type="model",
                local_dir=HF_DIR,
                local_dir_use_symlinks=False,
            )
            print_timing("Model checkpoint download", t0)
            tick(8)

            t0 = time.time()
            ckpt_local_dir = os.path.join(WEIGHTS_DIR, dataset)
            os.makedirs(ckpt_local_dir, exist_ok=True)
            ckpt_path = os.path.join(ckpt_local_dir, ckpt_name)
            if not os.path.exists(ckpt_path):
                shutil.copy(ckpt_path_hf, ckpt_path)
            print_timing("Local model copy setup", t0)
            tick(9)

            t0 = time.time()
            cfg_path = spec["config"]
            if not os.path.exists(cfg_path):
                raise FileNotFoundError(f"Missing config: {cfg_path}")
            cfg = OmegaConf.load(cfg_path)
            cfg.save_latent = True
            print_timing("Configuration loading", t0)
            tick(11)

            t0 = time.time()
            os.environ["CUDA_VISIBLE_DEVICES"] = str(getattr(cfg, "gpu_id", 0))
            ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
            accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            print_timing("Device init", t0)
            tick(12)

            t0 = time.time()
            import models
            model_cls_name = spec["model_attr"]
            if not hasattr(models, model_cls_name):
                raise ValueError(f"Model '{model_cls_name}' not found")
            model = getattr(models, model_cls_name)(cfg).to(device)
            print_timing("Model build", t0)
            tick(14)

            t0 = time.time()
            state = torch.load(ckpt_path, map_location=device)
            model.load_state_dict(state)
            model.eval()
            print_timing("Weights load", t0)
            tick(15)

            result = (cfg, model, device, accelerator)
            self._cache[dataset] = result
            print_timing(f"Total model build for {dataset}", start_time)
            return result
        except Exception as e:
            print_timing(f"Model build failed for {dataset}", e)
            raise RuntimeError(f"Failed to load model for dataset '{dataset}': {e}")

    def get(self, dataset: str, variable: str, progress_cb=None):
        return self._build(dataset, progress_cb=progress_cb)

MODEL_STORE = ModelStore()

# ========= Inference pipeline =========
def _variable_index(dataset: str, variable: str) -> int:
    ov = REGISTRY[dataset]["out_variable"]
    return 0 if isinstance(ov, str) else ov.index(variable)

@time_function("Mesh Processing")
def process_mesh_fast(mesh: pv.DataSet, cfg, variable, dataset, boundary_conditions=None):
    jpath = os.path.join("configs/app_configs/", dataset, "full_transform_params.json")
    json_data = json.load(open(jpath, "r"))

    pts = np.asarray(mesh.points, dtype=np.float32)
    N = pts.shape[0]
    rng = np.random.default_rng(42)
    idx = rng.permutation(N)
    points = pts[idx]
    tgt_np = mesh_get_variable(mesh, variable, N)[idx]
    pos = torch.from_numpy(points)
    target = torch.from_numpy(tgt_np).unsqueeze(-1)

    if getattr(cfg, "diff_input_velocity", False) and boundary_conditions is not None:
        if "freestream_velocity" in boundary_conditions:
            inlet_x_velocity = torch.tensor(boundary_conditions["freestream_velocity"]).float().reshape(1, 1)
            inlet_x_velocity = inlet_x_velocity.repeat(N, 1)[idx]
            pos = torch.cat((pos, inlet_x_velocity), dim=1)

    if getattr(cfg, "input_normalization", None) == "shift_axis":
        coords = pos[:, :3].clone()
        coords[:, 0] = coords[:, 0] - coords[:, 0].min()
        coords[:, 2] = coords[:, 2] - coords[:, 2].min()
        y_center = (coords[:, 1].max() + coords[:, 1].min()) / 2.0
        coords[:, 1] = coords[:, 1] - y_center
        pos[:, :3] = coords

    if getattr(cfg, "pos_embed_sincos", False):
        mins = torch.tensor(json_data["mesh_stats"]["min"], dtype=torch.float32)
        maxs = torch.tensor(json_data["mesh_stats"]["max"], dtype=torch.float32)
        pos = 1000.0 * (pos - mins) / (maxs - mins)
        pos = torch.clamp(pos, 0, 1000)

    cosine_score = compute_cosine_score(mesh, dataset)
    return pos, target, points, cosine_score

@time_function("Inference")
def run_inference_fast(dataset: str, variable: str, boundary_conditions=None, progress_cb=None):
    def p(v):
        if progress_cb:
            try:
                progress_cb(int(v))
            except Exception:
                pass

    if GEOMETRY_CACHE.current_mesh is None:
        raise ValueError("No geometry loaded")

    p(5)
    cfg, model, device, _ = MODEL_STORE.get(dataset, variable, progress_cb=p)
    p(15)

    pos, target, points, cosine_score = process_mesh_fast(
        GEOMETRY_CACHE.current_mesh, cfg, variable, dataset, boundary_conditions
    )
    p(25)

    confidence_score = 0.0
    try:
        if dataset not in ["Incompressible flow inside artery"]:
            geom_path = os.path.join(GEOM_DIR, "geometry.stl")
            latent_features = get_single_latent(
                mesh_path=geom_path,
                config_path=os.path.join("configs/app_configs/", dataset, "config.yaml"),
                device=device,
                custom_velocity=boundary_conditions["freestream_velocity"] if boundary_conditions else None,
                use_training_velocity=False,
                model=model,
            )

            embedding_path = os.path.join("configs/app_configs/", dataset, "pca_embedding.npy")
            pca_reducer_path = os.path.join("configs/app_configs/", dataset, "pca_reducer.pkl")
            scaler_path = os.path.join("configs/app_configs/", dataset, "pca_scaler.pkl")

            embedding = np.load(embedding_path)
            pca_reducer = pickle.load(open(pca_reducer_path, "rb"))
            scaler = pickle.load(open(scaler_path, "rb"))

            train_pair_dists = pairwise_distances(embedding)
            sigma = float(np.median(train_pair_dists)) if train_pair_dists.size > 0 else 1.0

            n_points, n_features = latent_features.shape
            np.random.seed(42)
            target_len = int(pca_reducer.n_features_in_ / 256)
            if n_points > target_len:
                latent_features = latent_features[np.random.choice(n_points, target_len, replace=False)]
            elif n_points < target_len:
                num_extra = target_len - n_points
                extra_indices = np.random.choice(n_points, num_extra, replace=True)
                latent_features = np.vstack([latent_features, latent_features[extra_indices]])

            latent_features = latent_features.flatten()

            confidence_score = compute_confidence_score(
                latent_features, embedding, scaler, pca_reducer, sigma
            )
    except Exception:
        confidence_score = 0.0

    data = {
        "input_pos": pos.unsqueeze(0).to(device),
        "output_feat": target.unsqueeze(0).to(device),
    }

    with torch.no_grad():
        inp = data["input_pos"]
        _, N, _ = inp.shape
        chunk = int(getattr(cfg, "num_points", 10000))

        if getattr(cfg, "chunked_eval", False) and chunk < N:
            input_pos = data["input_pos"]
            chunk_size = cfg.num_points
            out_chunks = []
            total = (N + chunk_size - 1) // chunk_size

            for k, i in enumerate(range(0, N, chunk_size)):
                ch = input_pos[:, i : i + chunk_size, :]
                n_valid = ch.shape[1]

                if n_valid < chunk_size:
                    pad = input_pos[:, : chunk_size - n_valid, :]
                    ch = torch.cat([ch, pad], dim=1)

                data["input_pos"] = ch
                out_chunk = model(data)
                if isinstance(out_chunk, (tuple, list)):
                    out_chunk = out_chunk[0]
                out_chunks.append(out_chunk[:, :n_valid, :])

                p(25 + 60 * (k + 1) / max(1, total))

            outputs = torch.cat(out_chunks, dim=1)

        else:
            p(40)
            outputs = model(data)
            if isinstance(outputs, (tuple, list)):
                outputs = outputs[0]
            if torch.cuda.is_available():
                torch.cuda.synchronize()
            p(85)

    vi = _variable_index(dataset, variable)
    pred = outputs[0, :, vi : vi + 1]

    if getattr(cfg, "normalization", "") == "std_norm":
        fp = os.path.join("configs/app_configs/", dataset, "full_transform_params.json")
        j = json.load(open(fp, "r"))
        mu = torch.tensor(float(j["scalars"][variable]["mean"]), device=pred.device)
        sd = torch.tensor(float(j["scalars"][variable]["std"]), device=pred.device)
        pred = pred * sd + mu

    pred_np = pred.squeeze().detach().cpu().numpy()
    tgt_np = target.squeeze().numpy()

    pred_t = torch.from_numpy(pred_np).unsqueeze(-1)
    tgt_t = torch.from_numpy(tgt_np).unsqueeze(-1)
    rel_l2 = torch.mean(
        torch.norm(pred_t.squeeze(-1) - tgt_t.squeeze(-1), p=2, dim=-1)
        / torch.norm(tgt_t.squeeze(-1), p=2, dim=-1)
    )
    tgt_mean = torch.mean(tgt_t)
    ss_tot = torch.sum((tgt_t - tgt_mean) ** 2)
    ss_res = torch.sum((tgt_t - pred_t) ** 2)
    r2 = 1 - (ss_res / ss_tot) if ss_tot > 0 else torch.tensor(0.0)

    p(100)
    return {
        "points": np.asarray(points),
        "pred": np.asarray(pred_np),
        "tgt": np.asarray(tgt_np),
        "cosine_score": float(cosine_score),
        "confidence_score": float(confidence_score),
        "abs_err": float(np.mean(np.abs(pred_np - tgt_np))),
        "mse_err": float(np.mean((pred_np - tgt_np) ** 2)),
        "rel_l2": float(rel_l2.item()),
        "r_squared": float(r2.item()),
    }

# ========= VTK helpers =========
def make_actor_from_stl(stl_path: str, color=(0.85, 0.85, 0.85)):
    r = vtkSTLReader()
    r.SetFileName(stl_path)
    r.Update()
    tri = vtkTriangleFilter()
    tri.SetInputConnection(r.GetOutputPort())
    tri.Update()
    m = vtkPolyDataMapper()
    m.SetInputConnection(tri.GetOutputPort())
    a = vtkActor()
    a.SetMapper(m)
    a.GetProperty().SetColor(*color)
    return a

def build_jet_lut(vmin, vmax):
    lut = vtkLookupTable()
    lut.SetRange(float(vmin), float(vmax))
    lut.SetNumberOfTableValues(256)
    lut.Build()
    cmap = cm.get_cmap("jet", 256)
    for i in range(256):
        r_, g_, b_, _ = cmap(i)
        lut.SetTableValue(i, float(r_), float(g_), float(b_), 1.0)
    return lut

def color_actor_with_scalars_from_prediction(stl_path, points_xyz, pred_vals, array_name, vmin, vmax, lut=None):
    r = vtkSTLReader()
    r.SetFileName(stl_path)
    r.Update()
    poly = r.GetOutput()

    stl_pts = nps.vtk_to_numpy(poly.GetPoints().GetData())
    tree = cKDTree(points_xyz)
    _, nn_idx = tree.query(stl_pts, k=1)
    scalars = np.asarray(pred_vals, dtype=np.float32)[nn_idx]

    vtk_arr = nps.numpy_to_vtk(scalars, deep=True)
    vtk_arr.SetName(array_name)
    poly.GetPointData().AddArray(vtk_arr)
    poly.GetPointData().SetActiveScalars(array_name)

    mapper = vtkPolyDataMapper()
    mapper.SetInputData(poly)
    mapper.SetScalarModeToUsePointData()
    mapper.ScalarVisibilityOn()
    mapper.SetScalarRange(float(vmin), float(vmax))
    if lut is None:
        lut = build_jet_lut(vmin, vmax)
    mapper.SetLookupTable(lut)
    mapper.UseLookupTableScalarRangeOn()

    actor = vtkActor()
    actor.SetMapper(mapper)
    return actor

def add_or_update_scalar_bar(renderer, lut, title, label_fmt="%-0.2f", n_labels=8):
    existing = []
    ca = renderer.GetActors2D()
    ca.InitTraversal()
    for _ in range(ca.GetNumberOfItems()):
        a = ca.GetNextItem()
        if isinstance(a, vtkScalarBarActor):
            existing.append(a)
    for a in existing:
        renderer.RemoveActor2D(a)

    sbar = vtkScalarBarActor()
    sbar.SetLookupTable(lut)
    sbar.SetOrientationToVertical()
    sbar.SetLabelFormat(label_fmt)
    sbar.SetNumberOfLabels(int(n_labels))
    sbar.SetTitle(title)
    sbar.SetPosition(0.92, 0.05)
    sbar.SetPosition2(0.06, 0.90)

    tp = sbar.GetTitleTextProperty()
    tp.SetColor(1, 1, 1)
    tp.SetBold(True)
    tp.SetFontSize(22)
    lp = sbar.GetLabelTextProperty()
    lp.SetColor(1, 1, 1)
    lp.SetFontSize(18)

    renderer.AddActor2D(sbar)
    return sbar


@change("velocity_mph")
def _on_velocity_change(self, velocity_mph, **_):
    """Keep velocity_mph numeric and in a reasonable range."""
    try:
        v = float(velocity_mph)
    except Exception:
        v = 45.0  # fallback default
    v = max(0.0, min(300.0, v))
    if v != self.state.velocity_mph:
        self.state.velocity_mph = v

# ---------- Small helpers ----------
def poly_count(mesh: pv.PolyData) -> int:
    if hasattr(mesh, "n_faces_strict"):
        return mesh.n_faces_strict
    return mesh.n_cells

def md_to_html(txt: str) -> str:
    if not txt:
        return ""
    safe = _html.escape(txt)
    safe = re.sub(r"\*\*(.+?)\*\*", r"<b>\1</b>", safe)
    return "<br/>".join(safe.splitlines())

def bc_text_right(dataset: str) -> str:
    if dataset == "Incompressible flow over car":
        return (
            "<b>Reference Density:</b> 1.225 kg/m³<br><br>"
            "<b>Reference Viscosity:</b> 1.789e-5 Pa·s<br><br>"
            "<b>Operating Pressure:</b> 101325 Pa"
        )
    if dataset == "Compressible flow over plane":
        return (
            "<b>Ambient Temperature:</b> 218 K<br><br>"
            "<b>Cruising velocity:</b> 250.0 m/s or 560 mph"
        )
    return ""

def bc_text_left(dataset: str) -> str:
    if dataset == "Compressible flow over plane":
        return (
            "<b>Reference Density:</b> 0.36 kg/m³<br><br>"
            "<b>Reference viscosity:</b> 1.716e-05 kg/(m·s)<br><br>"
            "<b>Operating Pressure:</b> 23842 Pa"
        )
    return ""

# =====================================================================
# =======================  APP  =======================================
# =====================================================================
class PFMDemo(TrameApp):
    def __init__(self, server=None):
        super().__init__(server)

        # ---------------- VTK RENDERERS ----------------
        self.ren_geom = vtkRenderer()
        self.ren_geom.SetBackground(0.10, 0.16, 0.22)
        self.rw_geom = vtkRenderWindow()
        self.rw_geom.SetOffScreenRendering(1)
        self.rw_geom.AddRenderer(self.ren_geom)
        self.rwi_geom = vtkRenderWindowInteractor()
        self.rwi_geom.SetRenderWindow(self.rw_geom)
        self.rwi_geom.SetInteractorStyle(vtkInteractorStyleTrackballCamera())
        try:
            self.rwi_geom.Initialize()
            self.rwi_geom.Enable()
        except Exception:
            pass

        self.ren_pred = vtkRenderer()
        self.ren_pred.SetBackground(0.10, 0.16, 0.22)
        self.rw_pred = vtkRenderWindow()
        self.rw_pred.SetOffScreenRendering(1)
        self.rw_pred.AddRenderer(self.ren_pred)
        self.rwi_pred = vtkRenderWindowInteractor()
        self.rwi_pred.SetRenderWindow(self.rw_pred)
        self.rwi_pred.SetInteractorStyle(vtkInteractorStyleTrackballCamera())
        try:
            self.rwi_pred.Initialize()
            self.rwi_pred.Enable()
        except Exception:
            pass

        self.scalar_bar = None

        # timers / flags
        self._predict_t0 = None
        self._infer_thread = None
        self._pre_upload_thread = None
        self._infer_heartbeat_on = False
        self._loop = None

        # ---------------- TRAME STATE ----------------
        s = self.state
        s.theme_dark = True

        s.analysis_types = ANALYSIS_TYPE
        s.analysis_type = DEFAULT_ANALYSIS_TYPE
        s.dataset_choices = ANALYSIS_TYPE_MAPPING[DEFAULT_ANALYSIS_TYPE]
        s.dataset = DEFAULT_DATASET
        s.variable_choices = variables_for(DEFAULT_DATASET)
        s.variable = s.variable_choices[0] if s.variable_choices else None

        # dialog (still kept)
        s.show_decimation_dialog = False
        s.decim_override_enabled = False
        s.decim_override_mode = "medium"
        s.decim_override_custom = 0.5

        # menu decimation defaults
        s.decim_enable = False        # user MUST toggle to override auto
        s.decim_target = 0.5
        s.decim_min_faces = 5000         # <= important: 0 so small meshes can be reduced
        s.decim_max_faces = int(1e7)

        # register controller properly
        # self.server.controller.decimate_again = self.decimate_again
        # self.server.controller.add("decimate_again", self.decimate_again)
        ctrl = self.server.controller

        # ✅ this actually registers the trigger
        ctrl.add("decimate_again", self.decimate_again)
        ctrl.add("reset_mesh", self.reset_mesh)



        s.show_velocity = (DEFAULT_DATASET == "Incompressible flow over car")
        s.is_plane = (DEFAULT_DATASET == "Compressible flow over plane")
        s.velocity_mph = 45.0

        s.bc_text = get_boundary_conditions_text(DEFAULT_DATASET)
        s.bc_left = bc_text_left(DEFAULT_DATASET)
        s.bc_right = bc_text_right(DEFAULT_DATASET)
        s.bc_text_html = s.bc_right or md_to_html(s.bc_text)

        s.stats_html = "👋 Upload a geometry, then click Predict."
        s.upload = None

        # upload
        s.is_uploading = False
        s.pm_upload = 0
        s.pm_elapsed_upload = 0.0
        s.upload_msg = ""

        # predict
        s.is_predicting = False
        s.predict_progress = 0
        s.predict_msg = ""
        s.predict_elapsed = 0.0
        s.predict_est_total = 0.0
        s.pm_infer = 0
        s.pm_elapsed_infer = 0.0

        self._build_ui()

    def _ensure_loop(self):
        if self._loop is not None:
            return self._loop
        try:
            loop = asyncio.get_event_loop()
        except RuntimeError:
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
        self._loop = loop
        return loop

    def _run_coro(self, coro):
        loop = self._ensure_loop()
        if loop.is_running():
            return asyncio.ensure_future(coro, loop=loop)
        return loop.run_until_complete(coro)

    async def _flush_async(self):
        try:
            self.server.state.flush()
        except Exception:
            pass
        await asyncio.sleep(0)

    def _build_ui(self):
        ctrl = self.server.controller
        with SinglePageLayout(self.server, full_height=True) as layout:
            layout.title.set_text("")    # clear
            layout.title.hide = True     # hide default
            with layout.toolbar:
                with v3.VContainer(
                    fluid=True,
                    style=(
                        "max-width: 1800px;"      # overall width
                        "margin: 0 auto;"         # center it
                        "padding: 0 8px;"        # ← left/right margin
                        "box-sizing: border-box;"
                    ),
                ):
                    v3.VSpacer()
                    html.Div(
                        "Ansys: Physics Foundation Model",
                        style=(
                            "width:100%;"
                            "text-align:center;"
                            "font-size:34px;"
                            "font-weight:900;"
                            "letter-spacing:0.4px;"
                            "line-height:1.2;"
                        ),
                    )
                    v3.VSpacer()

            # toolbar
            with layout.toolbar:
                # ← same margin container for the second toolbar row
                with v3.VContainer(
                    fluid=True,
                    style=(
                        "max-width: 1800px;"
                        "margin: 0 auto;"
                        "padding: 0 8px;"
                        "box-sizing: border-box;"
                    ),
                ):
                    v3.VSwitch(
                        v_model=("theme_dark",),
                        label="Dark Theme",
                        inset=True,
                        density="compact",
                        hide_details=True,
                    )
                    v3.VSpacer()

            with layout.content:
                html.Style("""
                /* Small side padding for the whole app */
                .v-application__wrap {
                    padding-left: 8px;
                    padding-right: 8px;
                    padding-bottom: 8px;
                }

                :root {
                    --pfm-font-ui: 'Inter', 'IBM Plex Sans', 'Segoe UI', Roboto, Helvetica, Arial, sans-serif;
                    --pfm-font-mono: 'JetBrains Mono', 'IBM Plex Mono', monospace;
                }

                html, body, .v-application {
                    margin: 0;
                    padding: 0;
                    font-family: var(--pfm-font-ui) !important;
                    font-weight: 500;
                    letter-spacing: .25px;
                    -webkit-font-smoothing: antialiased;
                    -moz-osx-font-smoothing: grayscale;
                    text-rendering: optimizeLegibility;
                    line-height: 1.5;
                    font-size: 15.5px;
                    color: #ECEFF4;
                }

                /* ... keep all your other typography / button / slider styles here ... */

                .v-theme--dark { background-color: #14171C !important; color: #ECEFF4 !important; }
                .v-theme--light { background-color: #F6F7FA !important; color: #1F1F1F !important; }

                /* (rest of your .pfm-* classes unchanged) */
                """)

                # html.Style("""
                # .v-theme--dark { background: #1F232B !important; }
                # .v-theme--light { background: #f5f6f8 !important; }
                # .v-theme--dark .pfm-card { background: #23272F !important; color: #fff !important; }
                # .v-theme--light .pfm-card { background: #ffffff !important; color: #1f232b !important; }
                # .v-theme--dark .pfm-viewer { background: #15171d !important; }
                # .v-theme--light .pfm-viewer { background: #e9edf3 !important; }
                # .pfm-card { border-radius: 16px !important; box-shadow: 0 6px 24px rgba(0,0,0,0.12); }
                # .pfm-progress .v-progress-linear { height: 22px !important; border-radius: 999px !important; }
                # .pfm-btn-big.v-btn {
                #     height: 48px !important;
                #     font-size: 18px !important;
                #     font-weight: 600 !important;
                #     letter-spacing: 1.2px;
                #     text-transform: none !important;
                #     border-radius: 999px !important;
                # }
                # .pfm-viewer { min-height: 420px; height: 650px !important; border-radius: 16px; }
                # """)
                html.Link(
                    rel="stylesheet",
                    href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=JetBrains+Mono:wght@400;600&display=swap",
                )

                with v3.VThemeProvider(theme=("theme_dark ? 'dark' : 'light'",)):
                    with v3.VContainer(
                        fluid=True,
                        class_="pa-6",
                        style=(
                            "max-width: 2200px;"                     # max width of content
                            "margin: 8px auto 16px auto;"          # top / left-right / bottom
                            "padding: 0 8px;"                      # inner left/right padding
                            "box-sizing: border-box;"
                            "background: rgba(255,255,255,0.02);"
                            "border-radius: 16px;"
                        ),
                    ):


                        # 1) Physics Application
                        with v3.VSheet(class_="pa-6 mb-4 pfm-card", rounded=True, elevation=3):
                            html.Div(
                                "🧪 <b>Physics Application</b>",
                                style="font-size:28px;font-weight:700;letter-spacing:1.1px;margin-bottom:10px;",
                            )
                            html.Div(
                                "Select the type of analysis",
                                style="font-size:24px;opacity:.82;margin-bottom:18px;",
                            )
                            toggle = v3.VBtnToggle(
                                v_model=("analysis_type", self.state.analysis_type),
                                class_="mt-1",
                                mandatory=True,
                                rounded=True,
                            )
                            # with toggle:
                            #     for at in ANALYSIS_TYPE:
                            #         v3.VBtn(
                            #             at,
                            #             value=at,
                            #             variant=(f"analysis_type===`{at}` ? 'elevated' : 'tonal'"),
                            #             class_="mr-2 pfm-toggle-xxl",
                            #         )

                            with toggle:
                                for at in ANALYSIS_TYPE:
                                    v3.VBtn(
                                        at,
                                        value=at,
                                        variant=(f"analysis_type===`{at}` ? 'elevated' : 'tonal'"),
                                        class_="mr-2 pfm-toggle-xxl",
                                        style=(
                                            "font-size:18px;"
                                            "font-weight:800;"
                                            "letter-spacing:0.4px;"
                                            "text-transform:none;"
                                        ),
                                    )

                        # 2) Dataset + Variable
                        with v3.VRow(dense=True, class_="mb-3"):
                            with v3.VCol(cols=6):
                                with v3.VSheet(class_="pa-6 pfm-card", rounded=True, elevation=3):
                                    html.Div(
                                        "🧩 Sub Application",
                                        style="font-weight:700;font-size:24px;margin-bottom:14px;",
                                    )
                                    v3.VSelect(
                                        v_model=("dataset", self.state.dataset),
                                        items=("dataset_choices", self.state.dataset_choices),
                                        hide_details=True,
                                        density="comfortable",
                                        style=(
                                            "font-size:24px;"
                                            "font-weight:800;"
                                            "height:56px;"
                                            "display:flex;"
                                            "align-items:center;"
                                        ),
                                        class_="pfm-big-select-subapp",
                                        menu_props={"content_class": "pfm-subapp-list"},  # <— key for dropdown items
                                    )
                                    # v3.VSelect(
                                    #     v_model=("dataset", self.state.dataset),
                                    #     items=("dataset_choices", self.state.dataset_choices),
                                    #     hide_details=True,
                                    #     density="comfortable",
                                    #     class_="pfm-big-select-subapp pfm-subapp-list",
                                    #     style="font-size:21px;",
                                    # )
                            with v3.VCol(cols=6):
                                with v3.VSheet(class_="pa-6 pfm-card", rounded=True, elevation=3):
                                    html.Div(
                                        "📊 Variable to Predict",
                                        style="font-weight:700;font-size:20px;margin-bottom:14px;",
                                    )

                                    v3.VSelect(
                                        v_model=("variable", self.state.variable),
                                        items=("variable_choices", self.state.variable_choices),
                                        hide_details=True,
                                        density="comfortable",
                                        class_="pfm-var-select",
                                        style=(
                                            "font-size:20px;"
                                            "font-weight:800;"
                                            "height:56px;"
                                            "display:flex;"
                                            "align-items:center;"
                                        ),
                                        menu_props={"content_class": "pfm-var-list"},
                                    )
                                                                        # v3.VSelect(
                                    #     v_model=("variable", self.state.variable),
                                    #     items=("variable_choices", self.state.variable_choices),
                                    #     hide_details=True,
                                    #     density="comfortable",
                                    #     style="font-size:16px;",
                                    # )

                        # 3) Boundary Conditions
                        with v3.VSheet(class_="pa-6 mb-4 pfm-card", rounded=True, elevation=3):
                            html.Div(
                                "🧱 Boundary Conditions",
                                style="font-weight:700;font-size:22px;margin-bottom:16px;",
                            )

                            # two columns: Left = velocity controls, Right = reference text
                            with v3.VRow(class_="align-start", dense=True):
                                # ---- LEFT: velocity slider / readout ----
                                with v3.VCol(cols=7, class_="pfm-vel"):
                                    html.Div(
                                        "🚗 Velocity (mph)",
                                        class_="pfm-vel-title",
                                        style="margin-bottom:8px;font-weight:800;font-size:21px;letter-spacing:.3px;",
                                    )
                                    html.Div(
                                        "Set the inlet velocity in miles per hour",
                                        class_="pfm-vel-sub",
                                        style="margin-bottom:10px;font-size:20px;opacity:.95;",
                                    )
                                    with v3.VRow(class_="align-center mt-3 mb-2", v_if=("show_velocity",)):
                                        with v3.VCol(cols=8):
                                            v3.VSlider(
                                                v_model=("velocity_mph",),
                                                min=30.0,
                                                max=80.0,
                                                step=0.1,
                                                thumb_label=True,
                                                style="height:54px;max-width:540px;",
                                                class_="pfm-vel-slider",
                                            )
                                        with v3.VCol(cols=4):
                                            v3.VTextField(
                                                v_model=("velocity_mph",),
                                                type="number",
                                                min=0,
                                                max=300,
                                                step="0.1",
                                                density="comfortable",
                                                variant="outlined",
                                                hide_details=True,
                                                suffix="mph",
                                                style="max-width:160px;margin-left:8px;",
                                            )

                                    html.Div(
                                        "{{ Number(velocity_mph).toFixed(0) }} / 80 "
                                        "<span style='opacity:.95'>"
                                        "({{ (Number(velocity_mph) * 0.44704).toFixed(2) }} m/s)</span>",
                                        v_if=("show_velocity",),
                                        class_="pfm-vel-readout",
                                        style="font-size:18px;font-weight:900;letter-spacing:.3px;margin-top:4px;",
                                    )

                                # ---- RIGHT: fixed reference values (HTML from bc_text_right / bc_text_left) ----
                                with v3.VCol(cols=5, class_="pfm-bc-right"):
                                    html.Div(
                                        v_html=("bc_text_html", ""),
                                        style=(
                                            "margin-top:6px;"
                                            "font-size:18px;"
                                            "line-height:1.7;"
                                            "min-width:260px;"
                                            "max-width:360px;"
                                            "text-align:left;"
                                        ),
                                    )

                        

                        # 4) Two viewers
                        with v3.VRow(style="margin-top: 24px;"):
                            # LEFT = upload
                            with v3.VCol(cols=6):
                                with v3.VRow(class_="align-center justify-space-between mb-2"):
                                    html.Div(
                                        "<span style='font-size:26px;font-weight:700;letter-spacing:1.1px;'>📤 Input Geometry</span>",
                                    )

                                    # ✅ working gear menu
                                    with v3.VMenu(
                                        location="bottom end",
                                        close_on_content_click=False,
                                        offset="4 8",
                                    ):
                                        # activator slot MUST expose { props } and we MUST bind them to the button
                                        with v3.Template(v_slot_activator="{ props }"):
                                            with v3.VBtn(
                                                icon=True,
                                                variant="text",
                                                density="comfortable",
                                                style="min-width:32px;",
                                                v_bind="props",               # 👈 this is the key
                                            ):
                                                v3.VIcon("mdi-cog", size="22")

                                        # menu content
                                        with v3.VCard(class_="pa-4", style="min-width: 280px;"):
                                            html.Div("<b>Mesh decimation</b>", class_="mb-3", style="font-size:14px;")

                                            v3.VSwitch(
                                                v_model=("decim_enable",),
                                                label="Enable decimation",
                                                inset=True,
                                                hide_details=True,
                                                class_="mb-4",
                                            )

                                            html.Div(
                                                "Target reduction (fraction of faces to remove)",
                                                class_="mb-1",
                                                style="font-size:12px;color:#9ca3af;",
                                            )
                                            v3.VSlider(
                                                v_model=("decim_target",),
                                                min=0.0,
                                                max=0.999,
                                                step=0.001,
                                                hide_details=True,
                                                class_="mb-2",
                                            )
                                            html.Div("{{ decim_target.toFixed(3) }}", style="font-size:11px;", class_="mb-3")

                                            with v3.VRow(dense=True, class_="mb-3"):
                                                with v3.VCol(cols=6):
                                                    html.Div("Min faces", style="font-size:11px;color:#9ca3af;", class_="mb-1")
                                                    v3.VTextField(
                                                        v_model=("decim_min_faces",),
                                                        type="number",
                                                        density="compact",
                                                        hide_details=True,
                                                    )
                                                with v3.VCol(cols=6):
                                                    html.Div("Max faces", style="font-size:11px;color:#9ca3af;", class_="mb-1")
                                                    v3.VTextField(
                                                        v_model=("decim_max_faces",),
                                                        type="number",
                                                        density="compact",
                                                        hide_details=True,
                                                    )

                                            v3.VBtn(
                                                "Apply to current mesh",
                                                block=True,
                                                color="primary",
                                                class_="mt-2",
                                                click=self.decimate_again,
                                            )

                                            v3.VBtn(
                                                "Reset to original mesh",
                                                block=True,
                                                variant="tonal",
                                                class_="mt-2",
                                                click=self.reset_mesh,       # 👈 will call the controller you added
                                            )


                                v3.VFileInput(
                                    label="Select 3D File",
                                    style="font-size:17px;padding:12px;height:50px;margin-bottom:20px;",
                                    multiple=False,
                                    show_size=True,
                                    accept=".stl,.vtk,.vtp,.ply,.obj,.vtu,.glb",
                                    v_model=("upload", None),
                                    clearable=True,
                                )
                                with v3.VSheet(height=620, rounded=True, class_="pa-0 pfm-viewer"):
                                    self.view_geom = VtkRemoteView(
                                        self.rw_geom,
                                        interactive=True,
                                        interactive_ratio=1,
                                        server=self.server,
                                    )
                                with v3.VSheet(class_="mt-3 pa-4 pfm-card pfm-progress",
                                            rounded=True, elevation=3):
                                    html.Div("<b>Upload</b>", style="font-size:18px;")

                                    # progress bar: only while uploading
                                    v3.VProgressLinear(
                                        v_model=("pm_upload", 0),
                                        height=22,
                                        style="margin-top:10px;margin-bottom:10px;",
                                        color="primary",
                                        rounded=True,
                                        v_show=("is_uploading",),   # 👈 bar disappears after upload
                                    )

                                    # text: percentage + time + message, only while uploading
                                    html.Div(
                                        "{{ pm_upload }}% — {{ pm_elapsed_upload.toFixed(2) }}s — {{ upload_msg }}",
                                        style="font-size:14px;",
                                        v_show=("is_uploading",),   # 👈 hide text after completion
                                    )

                                v3.VBtn(
                                    "🗑️ CLEAR",
                                    block=True,
                                    variant="tonal",
                                    class_="mt-3 pfm-btn-big",
                                    style="--v-btn-height:38px;--v-btn-size:1.35rem;padding:0 32px;",
                                    click=self.clear,
                                )
                            # RIGHT = prediction
                            with v3.VCol(cols=6):
                                html.Div(
                                    "<span style='font-size:26px;font-weight:700;letter-spacing:1.1px;'>📈 Prediction Results</span>",
                                    style="margin-bottom:10px;",
                                )
                                html.Div(
                                    v_html=("stats_html",),
                                    class_="mb-3",
                                    style="font-size:20px;line-height:1.4;",
                                )
                                # v3.VProgressLinear(
                                #     v_model=("predict_progress", 0),
                                #     height=22,
                                #     style="margin-top:6px;margin-bottom:12px;",
                                #     color="primary",
                                #     rounded=True,
                                #     indeterminate=("predict_progress < 10",),
                                #     v_show=("is_predicting",),
                                # )
                                # html.Div(
                                #     "Predicting: {{ predict_progress }}%",
                                #     style="font-size:14px;margin-bottom:10px;",
                                #     v_show=("is_predicting",),
                                # )
                                with v3.VSheet(height=620, rounded=True, class_="pa-0 pfm-viewer"):
                                    self.view_pred = VtkRemoteView(
                                        self.rw_pred,
                                        interactive=True,
                                        interactive_ratio=1,
                                        server=self.server,
                                    )
                                with v3.VSheet(class_="mt-3 pa-4 pfm-card pfm-progress",
                                            rounded=True, elevation=3):
                                    html.Div("<b>Inference</b>", style="font-size:18px;")

                                    # 🔴 OLD: v_model=("predict_progress", 0), indeterminate=...
                                    # 🟢 NEW: use pm_infer and a normal (non-indeterminate) bar
                                    v3.VProgressLinear(
                                        v_model=("pm_infer", 0),
                                        height=22,
                                        style="margin-top:6px;margin-bottom:12px;",
                                        color="success",
                                        rounded=True,
                                        indeterminate=("predict_progress <= 0",),
                                        v_show=("is_predicting",),   # 👈 bar only visible while predicting
                                    )

                                    # text line: % + elapsed time + current stage message
                                    html.Div(
                                        "{{ pm_infer }}% — {{ pm_elapsed_infer.toFixed(2) }}s — {{ predict_msg }}",
                                        style="font-size:14px;margin-bottom:10px;",
                                        # ❗ if you want the *text* to also disappear at the end, keep v_show;
                                        # if you want the final "✅ Prediction complete — 1.23s" to stay, REMOVE v_show
                                        v_show=("is_predicting",),
                                    )
                                v3.VBtn(
                                    "🚀 PREDICT",
                                    block=True,
                                    color="primary",
                                    class_="mt-3 pfm-btn-big",
                                    style="--v-btn-height:38px;--v-btn-size:1.35rem;padding:0 32px;",
                                    click=self.predict,
                                )

            layout.on_ready = self._first_paint

    def _first_paint(self, **_):
        for rw, view in ((self.rw_geom, self.view_geom), (self.rw_pred, self.view_pred)):
            try:
                rw.Render()
            except Exception:
                pass
            view.update()

    # ---------------------------------------------------------
    # UPLOAD (async)
    # ---------------------------------------------------------
    def _write_upload_to_disk(self, payload) -> str:
        if payload is None:
            raise ValueError("No file payload")
        if isinstance(payload, (list, tuple)):
            payload = payload[0]
        if isinstance(payload, str):
            return payload
        if not isinstance(payload, dict):
            raise ValueError(f"Unsupported payload: {type(payload)}")
        if payload.get("path"):
            return payload["path"]
        name = payload.get("name") or "upload"
        content = payload.get("content")
        if isinstance(content, str) and content.startswith("data:"):
            content = content.split(",", 1)[1]
        raw = base64.b64decode(content) if isinstance(content, str) else bytes(content)
        os.makedirs(GEOM_DIR, exist_ok=True)
        file_path = os.path.join(GEOM_DIR, name)
        with open(file_path, "wb") as f:
            f.write(raw)
        return file_path

    def _pre_upload_spinner_loop(self):
        s = self.state
        phase = 1
        while self._pre_upload_on and not self._upload_actual_started and s.is_uploading:
            s.pm_upload = max(1, min(9, phase))
            s.upload_msg = "Initializing upload..."
            try:
                self.server.state.flush()
            except Exception:
                pass
            phase = 1 if phase >= 9 else phase + 1
            time.sleep(0.15)

    def _start_pre_upload_spinner(self):
        if self._pre_upload_thread and self._pre_upload_thread.is_alive():
            return
        self._upload_actual_started = False
        self._pre_upload_on = True
        self._pre_upload_thread = threading.Thread(
            target=self._pre_upload_spinner_loop, daemon=True
        )
        self._pre_upload_thread.start()

    def _stop_pre_upload_spinner(self):
        self._pre_upload_on = False
        self._pre_upload_thread = None

    async def _fake_upload_bump(self, stop_event: asyncio.Event):
        s = self.state
        while not stop_event.is_set() and s.pm_upload < 9:
            s.pm_upload += 1
            await self._flush_async()
            await asyncio.sleep(0.05)

    async def _upload_worker_async(self, upload):
        s = self.state
        loop = self._ensure_loop()
        t0 = time.time()

        s.is_uploading = True
        s.upload_msg = "Starting upload..."
        s.pm_elapsed_upload = 0.0

        s.pm_upload = 5
        self.server.state.flush()
        await asyncio.sleep(0)

        fake_stop = asyncio.Event()
        fake_task = asyncio.create_task(self._fake_upload_bump(fake_stop))

        try:
            self._upload_actual_started = True
            self._stop_pre_upload_spinner()

            if not fake_stop.is_set():
                fake_stop.set()
                try:
                    await fake_task
                except asyncio.CancelledError:
                    pass

            s.upload_msg = "Writing file to disk..."
            s.pm_upload = 10
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()
            file_path = await loop.run_in_executor(None, self._write_upload_to_disk, upload)

            s.upload_msg = "Reading mesh..."
            s.pm_upload = 20
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()
            mesh = await loop.run_in_executor(None, pv.read, file_path)

            # 3) decimation (auto first)
            try:
                nf = poly_count(mesh)
            except Exception:
                nf = mesh.n_cells

            auto_tr = float(auto_target_reduction(nf))

            # reflect auto in UI
            s.decim_target = auto_tr
            s.decim_min_faces = 5000            # <= allow decimation even for 27k faces
            s.decim_max_faces = int(1e7)

            target = auto_tr
            min_faces = 5000
            max_faces = int(1e7)

            # user override
            if self.state.decim_enable:
                target = float(self.state.decim_target or 0.0)
                min_faces = int(self.state.decim_min_faces or 5000)
                max_faces = int(self.state.decim_max_faces or 1e7)

            if target > 0.0:
                s.upload_msg = f"Decimating mesh ({target:.3f})..."
                s.pm_upload = max(s.pm_upload, 45)
                s.pm_elapsed_upload = time.time() - t0
                await self._flush_async()

                dec_cfg = {
                    "enabled": True,
                    "method": "pro",
                    "target_reduction": target,
                    "min_faces": min_faces,
                    "max_faces": max_faces,
                }
                mesh = await loop.run_in_executor(None, decimate_mesh, mesh, dec_cfg)

            # 4) normals + save
            s.upload_msg = "Preparing geometry..."
            s.pm_upload = 75
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()

            def _normals_and_save(m):
                m_fixed = m.compute_normals(
                    consistent_normals=True,
                    auto_orient_normals=True,
                    point_normals=True,
                    cell_normals=False,
                    inplace=False,
                )
                geom_path_ = os.path.join(GEOM_DIR, "geometry.stl")
                m_fixed.save(geom_path_)
                return geom_path_, m_fixed

            geom_path, mesh_fixed = await loop.run_in_executor(None, _normals_and_save, mesh)

            # 5) update viewer
            self.ren_geom.RemoveAllViewProps()
            self.ren_geom.AddActor(make_actor_from_stl(geom_path))
            self.ren_geom.ResetCamera()
            try:
                self.rw_geom.Render()
            except Exception:
                pass
            self.view_geom.update()
            # GEOMETRY_CACHE.current_mesh = mesh_fixed
            GEOMETRY_CACHE.original_mesh = mesh_fixed.copy(deep=True)
            GEOMETRY_CACHE.current_mesh = mesh_fixed

            s.upload_msg = "✅ Geometry ready."
            s.pm_upload = 100
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()

        except Exception as e:
            s.upload_msg = f"❌ Upload failed: {e}"
            s.pm_upload = 0
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()
        finally:
            s.is_uploading = False
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()

            if not fake_stop.is_set():
                fake_stop.set()
            if not fake_task.done():
                fake_task.cancel()
                try:
                    await fake_task
                except Exception:
                    pass

    @change("upload")
    def _on_upload_change(self, upload, **_):
        if not upload:
            return
        self._run_coro(self._upload_worker_async(upload))

    def decimate_again(self):
        self._run_coro(self._decimate_again_async())

    async def _decimate_again_async(self):
        s = self.state
        loop = self._ensure_loop()

        if GEOMETRY_CACHE.current_mesh is None:
            # nothing to decimate
            s.upload_msg = "No mesh to re-decimate"
            await self._flush_async()
            return

        # --- start "upload-like" progress for manual decimation ---
        t0 = time.time()
        s.is_uploading = True
        s.pm_upload = 5
        s.pm_elapsed_upload = 0.0
        s.upload_msg = "Starting mesh re-decimation..."
        await self._flush_async()

        try:
            # read parameters from UI
            try:
                target = float(s.decim_target)
            except Exception:
                target = 0.0

            try:
                min_faces = int(s.decim_min_faces)
            except Exception:
                min_faces = 5000

            try:
                max_faces = int(s.decim_max_faces)
            except Exception:
                max_faces = int(1e7)

            if (not s.decim_enable) or target <= 0.0:
                s.upload_msg = "Decimation disabled"
                s.pm_upload = 0
                s.pm_elapsed_upload = time.time() - t0
                await self._flush_async()
                return

            # --- bump before heavy decimation call ---
            s.upload_msg = f"Re-decimating mesh ({target:.3f})..."
            s.pm_upload = 25
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()

            dec_cfg = {
                "enabled": True,
                "method": "pro",
                "target_reduction": target,
                "min_faces": min_faces,
                "max_faces": max_faces,
            }

            # heavy work on executor
            mesh = await loop.run_in_executor(
                None, decimate_mesh, GEOMETRY_CACHE.current_mesh, dec_cfg
            )

            # --- normals + save ---
            s.upload_msg = "Recomputing normals & saving..."
            s.pm_upload = 70
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()

            def _normals_and_save(m):
                m_fixed = m.compute_normals(
                    consistent_normals=True,
                    auto_orient_normals=True,
                    point_normals=True,
                    cell_normals=False,
                    inplace=False,
                )
                geom_path_ = os.path.join(GEOM_DIR, "geometry.stl")
                m_fixed.save(geom_path_)
                return geom_path_, m_fixed

            geom_path, mesh_fixed = await loop.run_in_executor(
                None, _normals_and_save, mesh
            )

            # --- update viewer ---
            s.upload_msg = "Updating viewer..."
            s.pm_upload = 90
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()

            self.ren_geom.RemoveAllViewProps()
            self.ren_geom.AddActor(make_actor_from_stl(geom_path))
            self.ren_geom.ResetCamera()
            try:
                self.rw_geom.Render()
            except Exception:
                pass
            self.view_geom.update()

            GEOMETRY_CACHE.current_mesh = mesh_fixed

            # --- final bump ---
            s.upload_msg = "✅ Re-decimated"
            s.pm_upload = 100
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()

        except Exception as e:
            s.upload_msg = f"❌ Re-decimation failed: {e}"
            s.pm_upload = 0
            s.pm_elapsed_upload = time.time() - t0
            await self._flush_async()
        finally:
            # hide bar + text after we’re done
            s.is_uploading = False
            await self._flush_async()


    def reset_mesh(self):
        self._run_coro(self._reset_mesh_async())

    async def _reset_mesh_async(self):
        s = self.state

        if GEOMETRY_CACHE.original_mesh is None:
            s.upload_msg = "No original mesh to reset to"
            await self._flush_async()
            return

        # use the saved original
        orig = GEOMETRY_CACHE.original_mesh

        # save it again as current
        GEOMETRY_CACHE.current_mesh = orig

        # write to disk (so the STL on disk matches the viewer)
        geom_path = os.path.join(GEOM_DIR, "geometry.stl")
        orig.save(geom_path)

        # update viewer
        self.ren_geom.RemoveAllViewProps()
        self.ren_geom.AddActor(make_actor_from_stl(geom_path))
        self.ren_geom.ResetCamera()
        try:
            self.rw_geom.Render()
        except Exception:
            pass
        self.view_geom.update()

        s.upload_msg = "↩️ Reset to original mesh"
        await self._flush_async()

    # ---------------------------------------------------------
    # prediction
    # ---------------------------------------------------------
    def _start_infer_heartbeat(self):
        if self._infer_thread and self._infer_thread.is_alive():
            return

        def loop_fn():
            while self._infer_heartbeat_on:
                if self.state.is_predicting and self._predict_t0 is not None:
                    self.state.pm_elapsed_infer = max(0.0, time.time() - self._predict_t0)
                    try:
                        self.server.state.flush()
                    except Exception:
                        pass
                time.sleep(0.12)

        self._infer_heartbeat_on = True
        self._infer_thread = threading.Thread(target=loop_fn, daemon=True)
        self._infer_thread.start()

    def _stop_infer_heartbeat(self):
        self._infer_heartbeat_on = False
        self._infer_thread = None

    async def _predict_worker_async(self):
        s = self.state
        loop = self._ensure_loop()
        t0 = time.time()

        if GEOMETRY_CACHE.current_mesh is None:
            s.predict_msg = "❌ Please upload geometry first"
            s.is_predicting = False
            await self._flush_async()
            return

        s.is_predicting = True
        s.predict_progress = 1
        s.pm_infer = 1
        s.predict_msg = "Preparing inference..."
        self._predict_t0 = time.time()
        self._start_infer_heartbeat()
        await self._flush_async()

        try:
            dataset = s.dataset
            variable = s.variable
            boundary = (
                {"freestream_velocity": mph_to_ms(s.velocity_mph)}
                if dataset == "Incompressible flow over car"
                else None
            )

            s.predict_msg = "Loading model/checkpoint..."
            s.predict_progress = 5
            s.pm_infer = 5
            await self._flush_async()

            cfg, model, device, _ = await loop.run_in_executor(
                None, MODEL_STORE.get, dataset, variable, None
            )

            s.predict_msg = "Processing mesh for inference..."
            s.predict_progress = 35
            s.pm_infer = 35
            await self._flush_async()

            def _run_full():
                return run_inference_fast(
                    dataset,
                    variable,
                    boundary_conditions=boundary,
                    progress_cb=None,
                )
            viz = await loop.run_in_executor(None, _run_full)

            s.predict_msg = "Preparing visualization..."
            s.predict_progress = 85
            s.pm_infer = 85
            await self._flush_async()

            stl_path = os.path.join(GEOM_DIR, "geometry.stl")
            vmin = float(np.min(viz["pred"]))
            vmax = float(np.max(viz["pred"]))

            if os.path.exists(stl_path):
                _tmp_trimesh, vmin, vmax = create_visualization_stl(viz, stl_path)
                lut = build_jet_lut(vmin, vmax)
                colored_actor = color_actor_with_scalars_from_prediction(
                    stl_path,
                    viz["points"],
                    viz["pred"],
                    variable,
                    vmin,
                    vmax,
                    lut=lut,
                )
                self.ren_pred.AddActor(colored_actor)

                units = {
                    "pressure": "Pa",
                    "x_velocity": "m/s",
                    "y_velocity": "m/s",
                    "z_velocity": "m/s",
                }.get(variable, "")
                title = f"{variable} ({units})" if units else variable
                self.scalar_bar = add_or_update_scalar_bar(
                    self.ren_pred, lut, title, label_fmt="%-0.2f", n_labels=8
                )

                src_cam = self.ren_geom.GetActiveCamera()
                dst_cam = self.ren_pred.GetActiveCamera()
                if src_cam is not None and dst_cam is not None:
                    dst_cam.SetPosition(src_cam.GetPosition())
                    dst_cam.SetFocalPoint(src_cam.GetFocalPoint())
                    dst_cam.SetViewUp(src_cam.GetViewUp())
                    dst_cam.SetParallelScale(src_cam.GetParallelScale())
                    cr = src_cam.GetClippingRange()
                    dst_cam.SetClippingRange(cr)

                try:
                    self.rw_pred.Render()
                except Exception:
                    pass
                self.view_pred.update()

            raw_vmin = float(np.min(viz["pred"]))
            raw_vmax = float(np.max(viz["pred"]))

            s.stats_html = (
                f"<b>{variable} min:</b>{raw_vmin:.3e} "
                f"<b>max:</b> {raw_vmax:.3e} "
                f"<b>Confidence:</b> {viz['confidence_score']:.4f}"
            )

            s.predict_msg = "✅ Prediction complete."
            s.predict_progress = 100
            s.pm_infer = 100
            s.predict_elapsed = time.time() - t0
            s.pm_elapsed_infer = s.predict_elapsed
            await self._flush_async()

        except Exception as e:
            s.predict_msg = f"❌ Prediction failed: {e}"
            s.predict_progress = 0
            s.pm_infer = 0
            await self._flush_async()
        finally:
            s.is_predicting = False
            self._stop_infer_heartbeat()
            await self._flush_async()

    @time_function("Inference and Visualization")
    def predict(self, *_):
        self._run_coro(self._predict_worker_async())

    # ---------------------------------------------------------
    # dataset wiring
    # ---------------------------------------------------------
    @change("analysis_type")
    def _on_analysis_type_change(self, analysis_type=None, **_):
        ds_list = ANALYSIS_TYPE_MAPPING.get(analysis_type or "", [])
        default_ds = ds_list[0] if ds_list else None
        self.state.dataset_choices = ds_list
        if default_ds and self.state.dataset != default_ds:
            self.state.dataset = default_ds
        elif self.state.dataset:
            self._apply_dataset(self.state.dataset)

    @change("dataset")
    def _on_dataset_change(self, dataset=None, **_):
        if not dataset:
            return
        self._apply_dataset(dataset)

    def _apply_dataset(self, ds: str):
        s = self.state
        opts = variables_for(ds) if ds else []
        s.variable_choices = opts
        s.variable = opts[0] if opts else None

        s.show_velocity = (ds == "Incompressible flow over car")
        s.is_plane = (ds == "Compressible flow over plane")

        s.bc_text = get_boundary_conditions_text(ds)
        s.bc_left = bc_text_left(ds)
        s.bc_right = bc_text_right(ds)
        s.bc_text_html = s.bc_right or md_to_html(s.bc_text)

    # ---------------------------------------------------------
    # clear
    # ---------------------------------------------------------
    def clear(self, *_):
        for d in [GEOM_DIR, SOLN_DIR]:
            if os.path.exists(d):
                shutil.rmtree(d)
            os.makedirs(d, exist_ok=True)
        s = self.state
        s.stats_html = "🧹 Cleared. Upload again."
        s.is_uploading = False
        s.pm_upload = 0
        s.upload_msg = ""
        s.pm_elapsed_upload = 0.0
        s.is_predicting = False
        s.predict_progress = 0
        s.predict_msg = ""
        s.pm_infer = 0
        s.pm_elapsed_infer = 0.0
        self.ren_geom.RemoveAllViewProps()
        self.ren_pred.RemoveAllViewProps()
        for rw, view in ((self.rw_geom, self.view_geom), (self.rw_pred, self.view_pred)):
            try:
                rw.Render()
            except Exception:
                pass
            view.update()

# ---------- main ----------
def main():
    app = PFMDemo()
    app.server.controller.add("decimate_again", app.decimate_again)
    app.server.controller.add("reset_mesh", app.reset_mesh)
    app.server.start(7872)

if __name__ == "__main__":
    main()