Initial ZeroGPU Gradio Space

.gitattributes

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+Data/DataForThermoforming.xlsx filter=lfs diff=lfs merge=lfs -text
+figures/forming_angle.png filter=lfs diff=lfs merge=lfs -text
+model_checkpoint.pth filter=lfs diff=lfs merge=lfs -text
+assets/model/best_model.pt filter=lfs diff=lfs merge=lfs -text
+assets/model/test_data.npz filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/
+*.pyc
+.gradio/

Data/DataForThermoforming.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d1276d5f5f5f64a0548c8eebcfc4355b7438c5372c8d7b7faf63e3d366506d82
+size 82096

Dataset.py

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+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+np.random.seed(42)
+epsilon = 1e-8
+
+class Dataset:
+    def __init__(self, inverse=False):
+        filename = Path(__file__).resolve().parent / "Data" / "DataForThermoforming.xlsx"
+        self.df = pd.read_excel(filename, sheet_name='CFRP')
+        # remove rows by index
+        # self.df = self.df.drop([20, 48], axis=0)
+        self.df = self.df.drop([7, 78, 101, 129], axis=0)
+        self.materials_map = {'CF/PEEK': 0, 'CF/PA6': 1}
+        self.df['Materials'] = self.df['Materials'].map(self.materials_map)
+
+        # normalize data
+        if inverse:
+            self.input_columns = ['Materials', 'Ply_Number', 'Fiber_Volume_Fractions', 'A1(abs)', 'B1(abs)', 'C1(abs)', 'Stress(Max) MPa']
+            self.output_columns = ['Initial_Temp (degree celsius)', 'Punch_Velocity (mm/s)', 'Cooling_Time (s)']
+        else:
+            self.input_columns = ['Materials', 'Ply_Number', 'Fiber_Volume_Fractions', 'Initial_Temp (degree celsius)', 'Punch_Velocity (mm/s)', 'Cooling_Time (s)']
+            self.output_columns = ['A1(abs)', 'B1(abs)', 'C1(abs)', 'Stress(Max) MPa']
+
+        self.input_mean = self.df[self.input_columns].mean().to_numpy(dtype=np.float32)
+        self.input_std = self.df[self.input_columns].std().to_numpy(dtype=np.float32) + epsilon
+        self.output_mean = self.df[self.output_columns].mean().to_numpy(dtype=np.float32)
+        self.output_std = self.df[self.output_columns].std().to_numpy(dtype=np.float32) + epsilon
+
+
+    def get_input(self, normalize=False):
+        data = self.df[self.input_columns].to_numpy(dtype=np.float32)
+        if normalize:
+            data = self.normalize_input(data)
+        return data
+    
+
+    def get_output(self, normalize=False):
+        data = self.df[self.output_columns].to_numpy(dtype=np.float32)
+        if normalize:
+            data = self.normalize_output(data)
+        return data
+
+    def __str__(self):
+        return str(self.df.head())
+    
+    def normalize_input(self, input_data):
+        return (input_data - self.input_mean) / self.input_std
+    
+    def normalize_output(self, output_data):
+        return (output_data - self.output_mean) / self.output_std
+    
+    def denormalize_input(self, normalized_input):
+        return normalized_input * self.input_std + self.input_mean
+    
+    def denormalize_output(self, normalized_output):
+        return normalized_output * self.output_std + self.output_mean
+
+if __name__ == "__main__":
+    dataset = Dataset()
+    
+    # Example usage
+    input_data = dataset.get_input(normalize=True)
+    output_data = dataset.get_output(normalize=True)
+    
+    print("Input shape:", input_data.shape)
+    print("Output shape:", output_data.shape)

README.md

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+---
+title: Quarter V5 Inverse Design ZeroGPU
+sdk: gradio
+app_file: app.py
+pinned: false
+---
+
+# Quarter V5 Inverse Design ZeroGPU
+
+Local Gradio-first scaffold for a private Hugging Face Space under `ChemFM`.
+
+Key points:
+
+- uses the quarter-v5 cubic model
+- keeps the `7`-curve requirement flow
+- keeps `vxy` and `vyx` as `2`-input requirements
+- generates `3` candidates and selects the closest one
+- runs thermoforming from the selected design
+- is structured for Hugging Face ZeroGPU by decorating the generation step with `@spaces.GPU`
+
+## Local run
+
+```bash
+cd /project/luofeng/feiyang/MaterialGeneration/hf_space_generation_quarter_zerogpu_gradio
+/home/feiyang/miniconda3/envs/ChemFMNew/bin/python app.py
+```
+
+## Notes for HF Space deployment
+
+- ZeroGPU is Gradio-only and requires the `spaces` package plus a `@spaces.GPU` wrapper on GPU work.
+- This folder now bundles the quarter-v5 checkpoint, the minimal metadata/test files needed for inference, and the quarter simulation assets under `assets/`.
+- Local absolute paths are optional overrides through `MG_CHECKPOINT_DIR`, `MG_DATA_DIR`, `MG_CURVE_DIR`, and `MG_QUARTER_DATA_GEN_DIR`.

app.py

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+from __future__ import annotations
+
+import json
+import os
+from functools import lru_cache
+from typing import Any, Dict, List
+
+import gradio as gr
+import numpy as np
+import torch
+
+from model_inverse import inverse_design as thermoforming_inverse_design
+from space_lib.cubic_curves import (
+    EPS33_ROW,
+    LATERAL_ROW,
+    MODE_TO_ROW,
+    default_x_for_mode,
+    evaluate_cubic_no_intercept,
+    fit_cubic_no_intercept,
+)
+from space_lib.infer import load_model_bundle, postprocess_sample, sample
+from space_lib.metadata_v5 import load_metadata_v5, normalize_condition_v5
+from space_lib.plots_v5 import plot_condition_and_simulations_v5, plot_required_curves_from_series
+from space_lib.simulate import simulate_instances
+
+try:
+    import spaces  # type: ignore
+except ImportError:  # local / non-ZeroGPU fallback
+    class _SpacesCompat:
+        @staticmethod
+        def GPU(*_args, **_kwargs):
+            def decorator(fn):
+                return fn
+
+            return decorator
+
+    spaces = _SpacesCompat()  # type: ignore
+
+
+APP_DIR = os.path.dirname(os.path.abspath(__file__))
+ASSETS_DIR = os.path.join(APP_DIR, "assets")
+MODEL_ASSETS_DIR = os.path.join(ASSETS_DIR, "model")
+SIM_ASSETS_DIR = os.path.join(ASSETS_DIR, "quarter_sim")
+DEFAULT_CHECKPOINT_DIR = os.environ.get(
+    "MG_CHECKPOINT_DIR",
+    MODEL_ASSETS_DIR,
+)
+DEFAULT_DATA_DIR = os.environ.get(
+    "MG_DATA_DIR",
+    MODEL_ASSETS_DIR,
+)
+DEFAULT_CURVE_DIR = os.environ.get(
+    "MG_CURVE_DIR",
+    os.path.join(SIM_ASSETS_DIR, "RVE_datasets"),
+)
+
+CHECKPOINT_DIR = DEFAULT_CHECKPOINT_DIR
+DATA_DIR = DEFAULT_DATA_DIR
+CURVE_DIR = DEFAULT_CURVE_DIR
+NORMALIZATION_METHOD = "zscore"
+ANGLE_RESOLUTION = 1.0
+DEFAULT_N_GENERATE = 3
+MAX_N_GENERATE = 10
+GPU_DURATION_SEC = int(os.environ.get("ZEROGPU_DURATION", "90"))
+DEFAULT_TEST_INDEX = int(os.environ.get("MG_DEFAULT_TEST_INDEX", "512"))
+MATERIAL_TYPE_NAMES = {0: "CPP", 1: "CHDPE", 2: "GPP", 3: "GHDPE"}
+
+
+REQUIREMENTS: List[Dict[str, Any]] = [
+    {"id": "11_sig11", "title": "Mode 11 Stress", "mode": "11", "response": "stress", "input_kind": "curve3"},
+    {"id": "11_eps22", "title": "Mode 11 Lateral", "mode": "11", "response": "lateral", "input_kind": "ratio2", "ratio_name": "Poisson's ratio (vxy)"},
+    {"id": "11_eps33", "title": "Mode 11 eps33", "mode": "11", "response": "eps33", "input_kind": "curve3"},
+    {"id": "22_sig22", "title": "Mode 22 Stress", "mode": "22", "response": "stress", "input_kind": "curve3"},
+    {"id": "22_eps11", "title": "Mode 22 Lateral", "mode": "22", "response": "lateral", "input_kind": "ratio2", "ratio_name": "Poisson's ratio (vyx)"},
+    {"id": "22_eps33", "title": "Mode 22 eps33", "mode": "22", "response": "eps33", "input_kind": "curve3"},
+    {"id": "12_sig12", "title": "Mode 12 Stress", "mode": "12", "response": "stress", "input_kind": "curve3"},
+]
+
+
+def _curve_field_labels(mode: str, response: str) -> tuple[str, str, str]:
+    x_end = float(default_x_for_mode(mode)[-1])
+    pct = int(round(x_end * 100))
+    if response == "stress":
+        return "Initial stiffness", f"{pct}% strain stiffness", f"{pct}% strain stress"
+    if response == "lateral":
+        return "Initial strain ratio", f"{pct}% strain ratio", f"{pct}% strain response"
+    return "Initial eps33 ratio", f"{pct}% strain ratio", f"{pct}% strain eps33"
+
+
+def _coeffs_from_requirements(initial_slope: float, end_slope: float, end_value: float, x_end: float) -> np.ndarray:
+    A = np.array([[2.0 * x_end, 3.0 * x_end * x_end], [x_end * x_end, x_end * x_end * x_end]], dtype=np.float32)
+    b = np.array([end_slope - initial_slope, end_value - initial_slope * x_end], dtype=np.float32)
+    quad, cubic = np.linalg.solve(A, b)
+    return np.asarray([cubic, quad, initial_slope], dtype=np.float32)
+
+
+def _requirements_from_coeffs(coeffs_7x3: np.ndarray) -> dict[str, float]:
+    coeffs = np.asarray(coeffs_7x3, dtype=np.float32).reshape(7, 3)
+    out: dict[str, float] = {}
+    for idx, spec in enumerate(REQUIREMENTS):
+        mode = spec["mode"]
+        x_end = float(default_x_for_mode(mode)[-1])
+        c3, c2, c1 = [float(v) for v in coeffs[idx]]
+        if spec["input_kind"] == "ratio2":
+            out[f"{spec['id']}_initial"] = c1
+            out[f"{spec['id']}_end_ratio"] = 3.0 * c3 * x_end * x_end + 2.0 * c2 * x_end + c1
+        else:
+            initial = c1
+            end_slope = 3.0 * c3 * x_end * x_end + 2.0 * c2 * x_end + c1
+            end_value = c3 * x_end**3 + c2 * x_end**2 + c1 * x_end
+            out[f"{spec['id']}_initial"] = initial
+            out[f"{spec['id']}_end_slope"] = end_slope
+            out[f"{spec['id']}_end_value"] = end_value
+    return out
+
+
+@lru_cache(maxsize=4)
+def _default_requirements(data_dir: str) -> dict[str, float]:
+    arr = np.load(os.path.join(data_dir, "test_data.npz"))["polynomial_coefficients"]
+    idx = int(DEFAULT_TEST_INDEX) % int(len(arr))
+    coeffs = np.asarray(arr[idx], dtype=np.float32).reshape(7, 3)
+    return _requirements_from_coeffs(coeffs)
+
+
+@lru_cache(maxsize=4)
+def _get_meta(data_dir: str):
+    return load_metadata_v5(data_dir)
+
+
+_MODEL_CACHE: dict[tuple[str, str, float], Any] = {}
+
+
+def _runtime_device() -> str:
+    return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def _get_model(checkpoint_dir: str, device: str, angle_resolution: float):
+    key = (checkpoint_dir, device, float(angle_resolution))
+    if key not in _MODEL_CACHE:
+        _MODEL_CACHE[key] = load_model_bundle(checkpoint_dir, device=device, angle_resolution=angle_resolution)
+    return _MODEL_CACHE[key]
+
+
+def _angle_categories_from_meta(meta, angle_resolution: float) -> np.ndarray:
+    angle_min_raw = float(meta.raw.get("angle_min", 0.0))
+    angle_max_raw = float(meta.raw.get("angle_max", 90.0))
+    res = float(angle_resolution)
+    ang_min = np.floor(angle_min_raw / res) * res
+    ang_max = np.ceil(angle_max_raw / res) * res
+    num = int((ang_max - ang_min) / res) + 1
+    return np.linspace(ang_min, ang_max, num, dtype=np.float32)
+
+
+def _balance_partner(angle: float) -> float:
+    a = abs(float(angle)) % 180.0
+    if np.isclose(a, 0.0):
+        return 0.0
+    if np.isclose(a, 90.0):
+        return 90.0
+    return -float(angle)
+
+
+def _full_angles_from_quarter(quarter_angles: list[float]) -> list[float]:
+    ordered_quarter = [float(a) for a in sorted(quarter_angles)]
+    half_angles = ordered_quarter + [_balance_partner(a) for a in reversed(ordered_quarter)]
+    return half_angles + list(reversed(half_angles))
+
+
+def _mat_type_to_matrix_fiber(mat_type: str) -> tuple[str, str]:
+    mt = (mat_type or "").upper().strip()
+    fiber = "Carbon" if mt.startswith("C") else ("Glass" if mt.startswith("G") else mt[:1])
+    matrix = "HDPE" if mt.endswith("HDPE") else ("PP" if mt.endswith("PP") else mt[1:])
+    return matrix, fiber
+
+
+def _score_design(cond_coeffs: np.ndarray, sim_modes: Dict[str, Dict[str, np.ndarray]]) -> float:
+    cond = np.asarray(cond_coeffs, dtype=np.float32).reshape(7, 3)
+    losses: list[float] = []
+    for mode, data in sim_modes.items():
+        strain = np.asarray(data["strain"], dtype=np.float32)
+        stress_pred = evaluate_cubic_no_intercept(cond[MODE_TO_ROW[mode]], strain)
+        losses.append(float(np.mean((stress_pred - np.asarray(data["stress"], dtype=np.float32)) ** 2)))
+        if mode in ("11", "22") and data.get("lateral") is not None:
+            lateral_pred = evaluate_cubic_no_intercept(cond[int(LATERAL_ROW[mode])], strain)
+            losses.append(float(np.mean((lateral_pred - np.asarray(data["lateral"], dtype=np.float32)) ** 2)))
+        if mode in ("11", "22") and data.get("eps33") is not None:
+            eps33_pred = evaluate_cubic_no_intercept(cond[int(EPS33_ROW[mode])], strain)
+            losses.append(float(np.mean((eps33_pred - np.asarray(data["eps33"], dtype=np.float32)) ** 2)))
+    return float(np.mean(losses)) if losses else float("inf")
+
+
+def _build_required_curves_and_condition(values: List[float]) -> tuple[dict[str, np.ndarray], np.ndarray]:
+    coeff_rows = []
+    curves: dict[str, np.ndarray] = {}
+    cursor = 0
+    for idx, spec in enumerate(REQUIREMENTS):
+        mode = spec["mode"]
+        x = default_x_for_mode(mode)
+        x_end = float(x[-1])
+        if spec["input_kind"] == "ratio2":
+            initial = float(values[cursor])
+            end_ratio = float(values[cursor + 1])
+            cursor += 2
+            quad = (end_ratio - initial) / (2.0 * x_end) if x_end > 0 else 0.0
+            y = initial * x + quad * x * x
+        else:
+            initial = float(values[cursor])
+            end_slope = float(values[cursor + 1])
+            end_value = float(values[cursor + 2])
+            cursor += 3
+            coeffs = _coeffs_from_requirements(initial, end_slope, end_value, x_end)
+            y = evaluate_cubic_no_intercept(coeffs, x)
+        fitted = fit_cubic_no_intercept(x, y, degree=3)
+        if fitted is None:
+            raise RuntimeError(f"Failed to fit cubic coefficients for requirement index {idx}")
+        coeff_rows.append(fitted)
+
+        req_id = spec["id"]
+        if req_id == "11_sig11":
+            curves["eps11"] = x
+            curves["sig11_mpa"] = y
+        elif req_id == "11_eps22":
+            curves["eps22_from_eps11"] = y
+        elif req_id == "11_eps33":
+            curves["eps33_from_eps11"] = y
+        elif req_id == "22_sig22":
+            curves["eps22"] = x
+            curves["sig22_mpa"] = y
+        elif req_id == "22_eps11":
+            curves["eps11_from_eps22"] = y
+        elif req_id == "22_eps33":
+            curves["eps33_from_eps22"] = y
+        elif req_id == "12_sig12":
+            curves["eps12"] = x
+            curves["sig12_mpa"] = y
+    return curves, np.stack(coeff_rows, axis=0).astype(np.float32)
+
+
+def _load_condition_defaults_from_test(idx: int) -> List[float]:
+    arr = np.load(os.path.join(DATA_DIR, "test_data.npz"))["polynomial_coefficients"]
+    coeffs = np.asarray(arr[int(idx) % len(arr)], dtype=np.float32).reshape(7, 3)
+    reqs = _requirements_from_coeffs(coeffs)
+    values: List[float] = []
+    for spec in REQUIREMENTS:
+        req_id = spec["id"]
+        if spec["input_kind"] == "ratio2":
+            values.extend([float(reqs[f"{req_id}_initial"]), float(reqs[f"{req_id}_end_ratio"])])
+        else:
+            values.extend(
+                [
+                    float(reqs[f"{req_id}_initial"]),
+                    float(reqs[f"{req_id}_end_slope"]),
+                    float(reqs[f"{req_id}_end_value"]),
+                ]
+            )
+    return values
+
+
+def _load_exact_test_coeffs(idx: int) -> np.ndarray:
+    arr = np.load(os.path.join(DATA_DIR, "test_data.npz"))["polynomial_coefficients"]
+    coeffs = np.asarray(arr[int(idx) % len(arr)], dtype=np.float32).reshape(7, 3)
+    return coeffs
+
+
+def _load_exact_test_payload(idx: int) -> str:
+    data = np.load(os.path.join(DATA_DIR, "test_data.npz"))
+    idx = int(idx) % int(len(data["polynomial_coefficients"]))
+    coeffs = np.asarray(data["polynomial_coefficients"][idx], dtype=np.float32).reshape(7, 3)
+    mat_id = int(data["material_type"][idx])
+    vf = float(data["volume_fraction"][idx])
+    seq_len = int(data["stacking_sequence_lengths"][idx])
+    quarter_angles = [float(a) for a in data["stacking_sequence"][idx][:seq_len]]
+    return json.dumps(
+        {
+            "coeffs": coeffs.tolist(),
+            "ground_truth": {
+                "test_index": idx,
+                "mat_type": MATERIAL_TYPE_NAMES.get(mat_id, str(mat_id)),
+                "vf": vf,
+                "quarter_angles": quarter_angles,
+                "full_angles": _full_angles_from_quarter(quarter_angles),
+            },
+        }
+    )
+
+
+def _curves_from_coeffs(cond_coeffs: np.ndarray) -> dict[str, np.ndarray]:
+    coeffs = np.asarray(cond_coeffs, dtype=np.float32).reshape(7, 3)
+    curves: dict[str, np.ndarray] = {}
+    mapping = [
+        ("11_sig11", "11"),
+        ("11_eps22", "11"),
+        ("11_eps33", "11"),
+        ("22_sig22", "22"),
+        ("22_eps11", "22"),
+        ("22_eps33", "22"),
+        ("12_sig12", "12"),
+    ]
+    for idx, (req_id, mode) in enumerate(mapping):
+        x = default_x_for_mode(mode)
+        y = evaluate_cubic_no_intercept(coeffs[idx], x)
+        if req_id == "11_sig11":
+            curves["eps11"] = x
+            curves["sig11_mpa"] = y
+        elif req_id == "11_eps22":
+            curves["eps22_from_eps11"] = y
+        elif req_id == "11_eps33":
+            curves["eps33_from_eps11"] = y
+        elif req_id == "22_sig22":
+            curves["eps22"] = x
+            curves["sig22_mpa"] = y
+        elif req_id == "22_eps11":
+            curves["eps11_from_eps22"] = y
+        elif req_id == "22_eps33":
+            curves["eps33_from_eps22"] = y
+        elif req_id == "12_sig12":
+            curves["eps12"] = x
+            curves["sig12_mpa"] = y
+    return curves
+
+
+def _serialize_designs(designs: list[Dict[str, Any]]) -> str:
+    return json.dumps(designs)
+
+
+def _deserialize_designs(payload: str) -> list[Dict[str, Any]]:
+    if not payload:
+        return []
+    return json.loads(payload)
+
+
+def _format_design_choice(design: Dict[str, Any]) -> str:
+    return (
+        f"Gen {design['design_id']} | score={design['score']:.4g} | "
+        f"{design['mat_type']} | vf={design['vf']:.4f} | q={design['quarter_angles']}"
+    )
+
+
+def _format_ground_truth_summary(gt: Dict[str, Any]) -> str:
+    return (
+        f"Gen GT: {gt['mat_type']}, vf={float(gt['vf']):.4f}, "
+        f"q={gt['quarter_angles']}, score=ground truth"
+    )
+
+
+def _design_markdown(design: Dict[str, Any]) -> str:
+    quarter = ", ".join(f"{a:.1f}" for a in design["quarter_angles"])
+    full = ", ".join(f"{a:.1f}" for a in design["full_angles"])
+    return (
+        f"Selected design: Gen {design['design_id']}\n\n"
+        f"Material: {design['mat_type']} ({design['matrix']} matrix, {design['fiber']} fiber)\n\n"
+        f"VF: {design['vf']:.4f}\n\n"
+        f"Quarter angles: {quarter}\n\n"
+        f"Full stack used by simulation: {full}\n\n"
+        f"Similarity score: {design['score']:.6g}"
+    )
+
+
+@spaces.GPU(duration=GPU_DURATION_SEC)
+def _run_generation_model(cond_coeffs: np.ndarray, n_generate: int) -> list[dict[str, Any]]:
+    meta = _get_meta(DATA_DIR)
+    device = _runtime_device()
+    bundle = _get_model(CHECKPOINT_DIR, device=device, angle_resolution=ANGLE_RESOLUTION)
+    cond_norm = normalize_condition_v5(cond_coeffs, meta, NORMALIZATION_METHOD)
+    n_generate = int(max(1, min(int(n_generate), MAX_N_GENERATE)))
+    cond_t = torch.tensor(cond_norm, dtype=torch.float32, device=device).view(1, -1).repeat(n_generate, 1)
+    angle_categories = _angle_categories_from_meta(meta, ANGLE_RESOLUTION) if bundle.use_discrete_angles else None
+    out = sample(
+        model=bundle.model,
+        disc_diff_mat=bundle.disc_diff_mat,
+        disc_diff_vf_category=bundle.disc_diff_vf_category,
+        disc_diff_layer=bundle.disc_diff_layer,
+        disc_diff_angle=bundle.disc_diff_angle,
+        cont_diff=bundle.cont_diff,
+        cond=cond_t,
+        mask_ids=bundle.mask_ids,
+        device=device,
+        remask_prob=0.1,
+        use_discrete_angles=bundle.use_discrete_angles,
+    )
+
+    generated: list[dict[str, Any]] = []
+    for i in range(n_generate):
+        sample_i = {
+            "material_t": out["material_t"][i : i + 1],
+            "vf_category_t": out["vf_category_t"][i : i + 1],
+            "layer_t": out["layer_t"][i : i + 1],
+            "angle_t": out["angle_t"][i : i + 1],
+        }
+        mat_type, vf, quarter_angles = postprocess_sample(
+            sample_i,
+            use_discrete_angles=bundle.use_discrete_angles,
+            angle_categories_deg=angle_categories,
+        )
+        generated.append(
+            {
+                "design_id": i + 1,
+                "mat_type": mat_type,
+                "vf": float(vf),
+                "quarter_angles": [float(a) for a in quarter_angles],
+            }
+        )
+    return generated
+
+
+def ui_generate_required_curves(*values: float):
+    curves, _ = _build_required_curves_and_condition(list(values))
+    return plot_required_curves_from_series(curves)
+
+
+def ui_clear_exact_test_coeffs():
+    return ""
+
+
+def ui_set_buttons(preview: bool, load: bool, design: bool, thermo: bool):
+    return (
+        gr.update(interactive=preview),
+        gr.update(interactive=load),
+        gr.update(interactive=design),
+        gr.update(interactive=thermo),
+    )
+
+
+def ui_disable_all_buttons():
+    return ui_set_buttons(False, False, False, False)
+
+
+def ui_input_mode_changed(mode: str):
+    manual = mode == "Define by Input Values"
+    status = ""
+    if manual:
+        status = "Manual requirement mode is active."
+    else:
+        status = "Load-from-test mode is active."
+    return (
+        gr.update(visible=manual),
+        gr.update(visible=not manual),
+        "",
+        status,
+        gr.update(interactive=manual),
+        gr.update(interactive=not manual),
+        gr.update(interactive=False),
+        gr.update(interactive=False),
+    )
+
+
+def ui_load_test_condition(idx: int):
+    vals = _load_condition_defaults_from_test(int(idx))
+    coeffs = _load_exact_test_coeffs(int(idx))
+    curves = _curves_from_coeffs(coeffs)
+    payload = _load_exact_test_payload(int(idx))
+    return (*vals, f"Loaded v5 test condition `{int(idx)}`.", plot_required_curves_from_series(curves), payload)
+
+
+def ui_after_requirement_ready(mode: str):
+    manual = mode == "Define by Input Values"
+    return ui_set_buttons(manual, not manual, True, False)
+
+
+def ui_inverse_design(n_generate: float, *values_and_state: float):
+    if len(values_and_state) < 2:
+        raise RuntimeError("Missing UI inputs/state.")
+    exact_payload = values_and_state[-1]
+    values = list(values_and_state[:-1])
+
+    gt_summary = ""
+    if exact_payload:
+        payload = json.loads(exact_payload)
+        cond_coeffs = np.asarray(payload["coeffs"], dtype=np.float32).reshape(7, 3)
+        curves = _curves_from_coeffs(cond_coeffs)
+        gt = payload.get("ground_truth", {})
+        if gt:
+            gt_summary = _format_ground_truth_summary(gt)
+    else:
+        curves, cond_coeffs = _build_required_curves_and_condition(values)
+    req_plot = plot_required_curves_from_series(curves)
+    n_generate_int = int(max(1, min(int(n_generate), MAX_N_GENERATE)))
+    generated = _run_generation_model(cond_coeffs, n_generate_int)
+
+    simulations_by_design: Dict[int, Dict[str, Dict[str, np.ndarray]]] = {}
+    ranked: list[Dict[str, Any]] = []
+    summaries: list[str] = []
+    logs: list[str] = []
+    for design in generated:
+        mat_type = design["mat_type"]
+        vf = float(design["vf"])
+        quarter_angles = [float(a) for a in design["quarter_angles"]]
+        try:
+            sim = simulate_instances(
+                curve_dir=CURVE_DIR,
+                mat_type=mat_type,
+                vf=vf,
+                quarter_angles=quarter_angles,
+                instances=[int(design["design_id"])],
+                num_output_points=20,
+            )
+            simulations_by_design.update(sim)
+            score = _score_design(cond_coeffs, sim.get(int(design["design_id"]), {}))
+            matrix, fiber = _mat_type_to_matrix_fiber(mat_type)
+            ranked.append(
+                {
+                    **design,
+                    "score": float(score),
+                    "matrix": matrix,
+                    "fiber": fiber,
+                    "full_angles": _full_angles_from_quarter(quarter_angles),
+                }
+            )
+            summaries.append(
+                f"Gen {design['design_id']}: {mat_type}, vf={vf:.4f}, q={quarter_angles}, score={score:.6g}"
+            )
+            logs.append(f"Simulation complete for generated design {design['design_id']}.")
+        except Exception as exc:
+            logs.append(f"Simulation failed for generated design {design['design_id']}: {type(exc).__name__}: {exc}")
+
+    ranked = sorted(ranked, key=lambda d: d["score"])
+    fig = plot_condition_and_simulations_v5(cond_coeffs, simulations_by_design)
+    if ranked:
+        selected = ranked[0]
+        choices = [_format_design_choice(d) for d in ranked]
+        payload = _serialize_designs(ranked)
+        return (
+            req_plot,
+            fig,
+            ((gt_summary + "\n") if gt_summary else "") + "\n".join(summaries),
+            "\n".join(logs) if logs else "Done.",
+            gr.update(choices=choices, value=choices[0]),
+            _design_markdown(selected),
+            payload,
+        )
+
+    return (
+        req_plot,
+        fig,
+        "No valid generated designs were scored.",
+        "\n".join(logs) if logs else "Generation failed.",
+        gr.update(choices=[], value=None),
+        "No selected design is available.",
+        "",
+    )
+
+
+def ui_after_design_complete(mode: str, payload: str):
+    manual = mode == "Define by Input Values"
+    thermo_ready = bool(payload)
+    return ui_set_buttons(manual, not manual, True, thermo_ready)
+
+
+def ui_select_design(selected_label: str, payload: str):
+    designs = _deserialize_designs(payload)
+    if not designs:
+        return "No selected design is available."
+    for design in designs:
+        if _format_design_choice(design) == selected_label:
+            return _design_markdown(design)
+    return _design_markdown(designs[0])
+
+
+def ui_thermoforming(selected_label: str, payload: str, angle_a: float, angle_b: float, angle_c: float, max_stress: float):
+    designs = _deserialize_designs(payload)
+    if not designs:
+        return "No selected design is available.", "Thermoforming cannot run before inverse design."
+
+    selected = designs[0]
+    for design in designs:
+        if _format_design_choice(design) == selected_label:
+            selected = design
+            break
+
+    domain_issues: list[str] = []
+    if selected["fiber"] != "Carbon":
+        domain_issues.append(
+            f"unsupported fiber `{selected['fiber']}`; thermoforming surrogate was trained only on carbon-fiber systems"
+        )
+    if not (0.4 <= float(selected["vf"]) <= 0.6):
+        domain_issues.append(
+            f"volume fraction {float(selected['vf']):.4f} is outside the surrogate training range [0.4, 0.6]"
+        )
+    n_layers = int(len(selected["full_angles"]))
+    if not (2 <= n_layers <= 8):
+        domain_issues.append(
+            f"ply count {n_layers} is outside the surrogate training range [2, 8]"
+        )
+
+    cf_mat = "CF/PEEK" if selected["matrix"] == "HDPE" else "CF/PA6"
+    best = thermoforming_inverse_design(
+        material=cf_mat,
+        ply_number=n_layers,
+        fiber_vf=float(selected["vf"]),
+        y_target=[float(angle_a), float(angle_b), float(angle_c), float(max_stress)],
+        n_restarts=10,
+        epochs=500,
+    )
+    warning_md = ""
+    if domain_issues:
+        warning_md = "Warning: surrogate is extrapolating outside its training domain.\n\n" + "\n".join(
+            f"- {issue}" for issue in domain_issues
+        ) + "\n\n"
+
+    result_md = (
+        warning_md
+        + f"Thermoforming surrogate material: {cf_mat}\n\n"
+        f"Forming temperature (C): {best['input'][0]:.3f}\n\n"
+        f"Punching velocity (mm/s): {best['input'][1]:.3f}\n\n"
+        f"Cooling time (s): {best['input'][2]:.3f}\n\n"
+        f"Predicted angle A (deg): {best['output'][0]:.3f}\n\n"
+        f"Predicted angle B (deg): {best['output'][1]:.3f}\n\n"
+        f"Predicted angle C (deg): {best['output'][2]:.3f}\n\n"
+        f"Predicted max residual stress (MPa): {best['output'][3]:.3f}"
+    )
+    return _design_markdown(selected), result_md
+
+
+def build_app():
+    defaults = _default_requirements(DATA_DIR)
+    input_components: list[gr.components.Component] = []
+
+    css = """
+    .curve-input-row { gap: 8px !important; flex-wrap: nowrap !important; }
+    .curve-card { min-width: 180px !important; }
+    .gradio-container .gr-number input[type="number"] {
+      width: 96px !important;
+      min-width: 96px !important;
+    }
+    """
+
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown("# Quarter Material Inverse Design")
+        gr.Markdown(
+            "ZeroGPU-targeted Gradio app for the quarter-v5 model. "
+            "Requirement curves are defined first, then batched candidates are generated, "
+            "the closest design is selected, and thermoforming is run from that selected design."
+        )
+
+        gr.Markdown("## 1. Define Material Requirements")
+        input_mode = gr.Radio(
+            choices=["Define by Input Values", "Load from Test Data"],
+            value="Define by Input Values",
+            label="Requirement source",
+        )
+
+        with gr.Group(visible=True) as manual_panel:
+            gr.Markdown("### Manual Requirement Input")
+            with gr.Row(elem_classes=["curve-input-row"]):
+                for spec in REQUIREMENTS:
+                    req_id = spec["id"]
+                    with gr.Column(elem_classes=["curve-card"]):
+                        gr.Markdown(f"**{spec['title']}**")
+                        if spec["input_kind"] == "ratio2":
+                            pct = int(round(float(default_x_for_mode(spec["mode"])[-1]) * 100))
+                            ratio_name = str(spec["ratio_name"])
+                            input_components.append(
+                                gr.Number(value=float(defaults[f"{req_id}_initial"]), label=f"Initial {ratio_name}", precision=6)
+                            )
+                            input_components.append(
+                                gr.Number(value=float(defaults[f"{req_id}_end_ratio"]), label=f"{pct}% strain {ratio_name}", precision=6)
+                            )
+                        else:
+                            l1, l2, l3 = _curve_field_labels(spec["mode"], spec["response"])
+                            input_components.append(gr.Number(value=float(defaults[f"{req_id}_initial"]), label=l1, precision=6))
+                            input_components.append(gr.Number(value=float(defaults[f"{req_id}_end_slope"]), label=l2, precision=6))
+                            input_components.append(gr.Number(value=float(defaults[f"{req_id}_end_value"]), label=l3, precision=6))
+            preview_btn = gr.Button("Generate Required Stress-Strain Curves")
+
+        with gr.Group(visible=False) as test_panel:
+            gr.Markdown("### Load Requirement Curves from Saved Test Data")
+            with gr.Row():
+                load_test_idx = gr.Number(value=DEFAULT_TEST_INDEX, precision=0, label="Load v5 test condition index")
+                load_btn = gr.Button("Load Test Condition")
+
+        n_generate = gr.Slider(
+            minimum=1,
+            maximum=MAX_N_GENERATE,
+            step=1,
+            value=DEFAULT_N_GENERATE,
+            label="Number of generated candidates",
+        )
+        design_btn = gr.Button("Generate Parameter Sets")
+
+        load_status = gr.Markdown("")
+        req_plot = gr.Plot(label="Required Stress-Strain Curves")
+        exact_test_coeffs = gr.State("")
+
+        gr.Markdown("## 2. Material Inverse Design")
+        design_plot = gr.Plot(label="Conditioned vs Generated Curves")
+        design_summary = gr.Textbox(label="Generated Designs", lines=6)
+        run_log = gr.Textbox(label="Run Log", lines=8, interactive=False)
+
+        gr.Markdown("## 3. Choose the Closest Generated Design")
+        design_choice = gr.Radio(choices=[], label="Generated designs")
+        selected_design_md = gr.Markdown("No selected design is available.")
+        design_payload = gr.State("")
+
+        gr.Markdown("## 4. Thermoforming Requirements")
+        with gr.Row():
+            thermo_image = gr.Image(
+                value=os.path.join(APP_DIR, "figures", "forming_angle.png"),
+                label="Thermoforming geometry",
+                interactive=False,
+                show_download_button=False,
+                show_share_button=False,
+            )
+            angle_a = gr.Number(value=0.0, label="Maximum warpage angle A (degree)", precision=3)
+            angle_b = gr.Number(value=0.0, label="Maximum warpage angle B (degree)", precision=3)
+            angle_c = gr.Number(value=0.0, label="Maximum warpage angle C (degree)", precision=3)
+            max_stress = gr.Number(value=0.0, label="Maximum residual stress (MPa)", precision=3)
+        thermo_btn = gr.Button("Thermoforming Process Design")
+        thermo_selected_md = gr.Markdown("No selected design is available.")
+        thermo_result_md = gr.Markdown("Thermoforming has not run yet.")
+
+        input_mode.change(
+            fn=ui_input_mode_changed,
+            inputs=[input_mode],
+            outputs=[manual_panel, test_panel, exact_test_coeffs, load_status, preview_btn, load_btn, design_btn, thermo_btn],
+            show_progress=False,
+        )
+        load_btn.click(fn=ui_disable_all_buttons, outputs=[preview_btn, load_btn, design_btn, thermo_btn], show_progress=False).then(
+            fn=ui_load_test_condition,
+            inputs=[load_test_idx],
+            outputs=input_components + [load_status, req_plot, exact_test_coeffs],
+            show_progress=False,
+        ).then(
+            fn=ui_after_requirement_ready,
+            inputs=[input_mode],
+            outputs=[preview_btn, load_btn, design_btn, thermo_btn],
+            show_progress=False,
+        )
+        preview_btn.click(
+            fn=ui_disable_all_buttons,
+            outputs=[preview_btn, load_btn, design_btn, thermo_btn],
+            show_progress=False,
+        ).then(
+            fn=ui_generate_required_curves,
+            inputs=input_components,
+            outputs=[req_plot],
+        ).then(
+            fn=ui_clear_exact_test_coeffs,
+            outputs=[exact_test_coeffs],
+            show_progress=False,
+        ).then(
+            fn=ui_after_requirement_ready,
+            inputs=[input_mode],
+            outputs=[preview_btn, load_btn, design_btn, thermo_btn],
+            show_progress=False,
+        )
+        design_btn.click(
+            fn=ui_disable_all_buttons,
+            outputs=[preview_btn, load_btn, design_btn, thermo_btn],
+            show_progress=False,
+        ).then(
+            fn=ui_inverse_design,
+            inputs=[n_generate] + input_components + [exact_test_coeffs],
+            outputs=[req_plot, design_plot, design_summary, run_log, design_choice, selected_design_md, design_payload],
+        ).then(
+            fn=ui_after_design_complete,
+            inputs=[input_mode, design_payload],
+            outputs=[preview_btn, load_btn, design_btn, thermo_btn],
+            show_progress=False,
+        )
+        design_choice.change(
+            fn=ui_select_design,
+            inputs=[design_choice, design_payload],
+            outputs=[selected_design_md],
+            show_progress=False,
+        )
+        thermo_btn.click(
+            fn=ui_disable_all_buttons,
+            outputs=[preview_btn, load_btn, design_btn, thermo_btn],
+            show_progress=False,
+        ).then(
+            fn=ui_thermoforming,
+            inputs=[design_choice, design_payload, angle_a, angle_b, angle_c, max_stress],
+            outputs=[thermo_selected_md, thermo_result_md],
+        ).then(
+            fn=ui_after_design_complete,
+            inputs=[input_mode, design_payload],
+            outputs=[preview_btn, load_btn, design_btn, thermo_btn],
+            show_progress=False,
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    build_app().launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", "7860")))

assets/model/best_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14c90b431fa5e47f57c6c8415ccd58c0ec037c6f07d7c9a0a9824f228000ec52
+size 108669286

assets/model/metadata.json

@@ -0,0 +1 @@
+{"coefficient_min": [-1336542.5759384537, -370989.7238115664, 947.5529106715172, -816.1991913874676, -208.04604832297184, -6.440726626026699, -1158.0661688245555, -69.61503102141909, -3.7110281079127443, -1336542.5759384537, -370989.7238115664, 947.5529106715172, -806.6953754940542, -208.04604832297184, -6.440726626026699, -1158.0661688245555, -69.07576872854699, -3.7110281079127443, -687251.041133879, -99541.30320133248, 550.9335908871876], "coefficient_max": [3389748.711189173, 421478.8566422272, 100460.49643423711, 1365.4308428400166, 81.15096684497989, 3.5611276778975767, 700.1052948607733, 176.8832872871231, 4.777908367737771, 3389748.711189173, 421478.8566422272, 100460.49643423711, 1365.4308428400166, 80.52454617123655, 3.5611276778975767, 691.930293906481, 176.8832872871231, 4.777908367737771, 166552.73950926174, 39144.65586780644, 50934.745352336715], "coefficient_mean": [101120.96032493451, -23269.924689670344, 20477.00387301237, 4.720287877934389, -1.1169651476400622, -0.14525108792628819, -4.671882933584733, 1.100051440863099, -0.6580143268913964, 101375.51538912015, -23302.181069942115, 20439.27571989428, 4.765792592236022, -1.126861951663068, -0.1450464462741492, -4.71155844344554, 1.108629199635049, -0.6583027108207908, 75735.61335592021, -15646.169776040819, 2798.13317620378], "coefficient_std": [105921.87768741499, 16012.286930571192, 19488.114026498577, 73.03719234264952, 10.501208624832898, 0.28763187615916513, 62.33564214699167, 8.949903397930505, 0.2718516369069587, 106341.76164185075, 16060.072999070784, 19445.30331086972, 73.55290507251158, 10.570817475374852, 0.2885619591766938, 62.779951244343984, 9.009779862745946, 0.2725334974008911, 39740.004555657324, 7077.809092508768, 3286.4254598785915], "polynomial_degree": 3, "angle_min": 0.0, "angle_max": 90.0}

assets/model/test_data.npz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:418e2372b5a6e47fb984b5bc0b616210c83133770d8f4a7bd465e848abfc44bf
+size 17039372

assets/model/training_config.json

@@ -0,0 +1,44 @@
+{
+  "data_dir": "/project/luofeng/feiyang/MaterialGeneration/hybrid_diffusion_material_generation_quarter_version_5_cubic/processed_data/",
+  "output_dir": "/project/luofeng/feiyang/MaterialGeneration/hybrid_diffusion_material_generation_quarter_version_5_cubic/checkpoints/config_1_quarter_v5_cubic/",
+  "experiment_dir": "/project/luofeng/feiyang/MaterialGeneration/hybrid_diffusion_material_generation_quarter_version_5_cubic/checkpoints/config_1_quarter_v5_cubic/exp_20260430_134155",
+  "timestamp": "20260430_134155",
+  "batch_size": 32,
+  "num_epochs": 100,
+  "learning_rate": 0.0001,
+  "T": 100,
+  "n_max_layer": 5,
+  "beta_start": 0.0001,
+  "beta_end": 0.02,
+  "lambda_angle": 1.0,
+  "lambda_material": 1.0,
+  "lambda_vf_category": 1.0,
+  "lambda_layer": 1.0,
+  "save_every": 5,
+  "normalization_method": "zscore",
+  "random_seed": 533,
+  "device": "cuda:0",
+  "gen_eval_n_samples": 100,
+  "gen_eval_every": 5,
+  "use_discrete_angles": false,
+  "save_visualizations": true,
+  "warmup_ratio": 0.01,
+  "log_every_n_steps": 100,
+  "angle_resolution": 1.0,
+  "model_config": {
+    "n_conditions": 21,
+    "n_materials": 4,
+    "n_vf_categories": 5,
+    "n_max_layer": 5,
+    "d_model": 256,
+    "n_heads": 4,
+    "n_layers": 6,
+    "dropout": 0.0
+  },
+  "mask_ids": {
+    "material": 4,
+    "vf_category": 5,
+    "layer": 2
+  },
+  "n_relationships": 7
+}

assets/quarter_sim/RVE_datasets/CHDPE_0.1000_11.txt

@@ -0,0 +1,203 @@
+Strain_11	Stress_11	Strain_22	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
+4.981025e-03	1.025632e+02	-2.292777e-03	-2.293962e-03
+9.962049e-03	2.049406e+02	-4.568769e-03	-4.571146e-03
+1.494307e-02	3.069680e+02	-6.832884e-03	-6.836500e-03
+1.992410e-02	4.080468e+02	-9.102475e-03	-9.107578e-03
+2.490512e-02	5.080874e+02	-1.138038e-02	-1.138738e-02
+2.988615e-02	6.076290e+02	-1.365181e-02	-1.366101e-02
+3.486717e-02	7.069254e+02	-1.590954e-02	-1.592112e-02
+3.984820e-02	8.060281e+02	-1.815241e-02	-1.816654e-02
+4.482922e-02	9.049523e+02	-2.038017e-02	-2.039700e-02
+4.981025e-02	1.003697e+03	-2.259315e-02	-2.261281e-02
+5.479127e-02	1.102267e+03	-2.479133e-02	-2.481398e-02
+5.977230e-02	1.200667e+03	-2.697483e-02	-2.700061e-02
+6.475332e-02	1.298899e+03	-2.914379e-02	-2.917283e-02
+6.973435e-02	1.396965e+03	-3.129832e-02	-3.133077e-02
+7.471537e-02	1.494868e+03	-3.343856e-02	-3.347453e-02
+7.969639e-02	1.592610e+03	-3.556463e-02	-3.560426e-02
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assets/quarter_sim/RVE_datasets/CHDPE_0.1000_12.txt

@@ -0,0 +1,132 @@
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assets/quarter_sim/RVE_datasets/CHDPE_0.1000_22.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.1000_22c.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.2000_11.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.2000_12.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.2000_22.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.2000_22c.txt

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+volume fraction= 0.200000

assets/quarter_sim/RVE_datasets/CHDPE_0.3000_11.txt

@@ -0,0 +1,203 @@
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assets/quarter_sim/RVE_datasets/CHDPE_0.3000_12.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.3000_22.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.3000_22c.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.4000_11.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.4000_12.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.4000_22.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.4000_22c.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.5000_11.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.5000_12.txt

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assets/quarter_sim/RVE_datasets/CHDPE_0.5000_22c.txt

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assets/quarter_sim/RVE_datasets/CPP_0.1000_11.txt

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assets/quarter_sim/RVE_datasets/CPP_0.1000_12.txt

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assets/quarter_sim/RVE_datasets/CPP_0.1000_22.txt

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assets/quarter_sim/RVE_datasets/CPP_0.1000_22c.txt

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assets/quarter_sim/RVE_datasets/CPP_0.2000_11.txt

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assets/quarter_sim/RVE_datasets/CPP_0.2000_12.txt

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assets/quarter_sim/RVE_datasets/CPP_0.2000_22.txt

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assets/quarter_sim/RVE_datasets/CPP_0.2000_22c.txt

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assets/quarter_sim/RVE_datasets/CPP_0.3000_11.txt

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assets/quarter_sim/RVE_datasets/CPP_0.3000_12.txt

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assets/quarter_sim/RVE_datasets/CPP_0.3000_22.txt

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assets/quarter_sim/RVE_datasets/CPP_0.3000_22c.txt

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assets/quarter_sim/RVE_datasets/CPP_0.4000_11.txt

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assets/quarter_sim/RVE_datasets/CPP_0.4000_12.txt

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assets/quarter_sim/RVE_datasets/CPP_0.4000_22.txt

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assets/quarter_sim/RVE_datasets/CPP_0.4000_22c.txt

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assets/quarter_sim/RVE_datasets/CPP_0.5000_11.txt

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assets/quarter_sim/RVE_datasets/CPP_0.5000_12.txt

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+4.050000e-01	8.076175e+01	-3.128632e-04	-2.835564e-01
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assets/quarter_sim/RVE_datasets/CPP_0.5000_22c.txt

@@ -0,0 +1,39 @@
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+volume fraction= 0.500000