server.py

"""Gradio API replacing kimodo_demo's Viser entrypoint.

Exposes a single endpoint at `/gradio_api/call/kimodo_motion` that accepts:
    (prompt, num_frames, seed, cfg, num_steps, constraints_json)

and returns a JSON envelope:
    {
      "status": "ok",
      "numFrames": int,
      "fps": 30,
      "rootTranslation": [[x,y,z], ...],     # [N, 3]
      "jointRotMats":    [[[[...]]]],        # [N, 30, 3, 3]  local rotations
      "globalRotMats":   [[[[...]]]],        # [N, 30, 3, 3]  world-space rotations
                                              # (kimodo's SOMA-77 FK output sliced
                                              # to SOMA-30; finger-tip joints carry
                                              # relaxed_hands_rest compounding)
      "footContacts":    [[lh, lt, lte, rh, rt, rte]], # [N, 6]  (optional;
                                              # SOMA-77 layout — toe-end copies
                                              # toe-base contact, see
                                              # kimodo.skeleton.definitions
                                              # .output_to_SOMASkeleton77)
      "summary": str
    }

The webapp's src/lib/services/kimodo.ts polls
`/gradio_api/call/kimodo_motion/<event_id>` for the SSE event stream.
"""

from __future__ import annotations

import json
import os
import sys
import traceback

import gradio as gr
import numpy as np
import torch

from constraints_schema import parse_constraints

# Lazy imports of kimodo so import-time failures (e.g. missing CUDA on the
# Space build container) don't kill `python server.py --help`.
_model = None
_skeleton = None
_device = None


def _load_model():
    global _model, _skeleton, _device
    if _model is not None:
        return _model, _skeleton, _device
    print("[server] loading Kimodo-SOMA-RP-v1.1 ...", file=sys.stderr, flush=True)
    from kimodo import load_model

    # Must be a string (kimodo passes this through Hydra/OmegaConf which
    # rejects non-primitive types like torch.device).
    _device = "cuda:0" if torch.cuda.is_available() else "cpu"
    print(f"[server] device = {_device}", file=sys.stderr, flush=True)

    model, resolved = load_model(
        "Kimodo-SOMA-RP-v1.1",
        device=_device,
        default_family="Kimodo",
        return_resolved_name=True,
    )
    print(f"[server] resolved model = {resolved}", file=sys.stderr, flush=True)
    _model = model
    _skeleton = model.skeleton
    return _model, _skeleton, _device


def kimodo_motion(
    prompt: str,
    num_frames: int,
    seed: int,
    cfg: float,
    num_steps: int,
    constraints_json: str,
    progress: gr.Progress = gr.Progress(),  # noqa: B008 — Gradio convention
) -> dict:
    """Generate one SOMA motion sample. Heavy work runs on the GPU; constraint
    parsing on the CPU. Returns the JSON envelope the webapp expects."""
    try:
        if not prompt or not prompt.strip():
            return {"status": "error", "error": "prompt is empty"}
        n = int(num_frames)
        if n < 1 or n > 300:
            return {
                "status": "error",
                "error": f"num_frames must be in [1, 300]; got {n}",
            }

        # Validate the constraints payload up front so a bad request doesn't
        # waste GPU time. We accept the same JSON the kimodo CLI accepts —
        # extra cross-list validation in constraints_schema bounds-checks frame
        # indices against num_frames.
        try:
            raw = json.loads(constraints_json) if constraints_json else []
            parse_constraints(raw, n)  # validates shape + bounds
        except (ValueError, json.JSONDecodeError) as e:
            return {"status": "error", "error": f"constraint validation: {e}"}

        progress(0.02, desc="Loading model...")
        model, skeleton, device = _load_model()

        # Convert the JSON list of dicts into kimodo constraint objects via
        # the official loader — accepts a list-of-dicts directly.
        from kimodo.constraints import load_constraints_lst

        constraint_lst = load_constraints_lst(raw, skeleton, device=device)

        if seed is not None and int(seed) >= 0:
            from kimodo.tools import seed_everything

            seed_everything(int(seed))

        progress(0.10, desc=f"Diffusion ({int(num_steps)} steps)...")
        cfg_kwargs = {"cfg_type": "regular", "cfg_weight": float(cfg)}
        # Single sample, single prompt. If you want multi-prompt later, this is
        # where you'd thread it through.
        output = model(
            [prompt.strip()],
            [n],
            constraint_lst=constraint_lst,
            num_denoising_steps=int(num_steps),
            num_samples=1,
            multi_prompt=True,
            num_transition_frames=20,
            return_numpy=True,
            **cfg_kwargs,
        )

        progress(0.92, desc="Serializing...")

        # Kimodo's SOMA-RP model trains on the 30-joint SOMA skeleton but emits
        # output at 77 joints (the somaskel77 representation, with relaxed
        # hand poses added). We need to:
        #   1. Get/compute 77-joint local rotation matrices.
        #   2. Convert back to the 30-joint subset via from_SOMASkeleton77.
        #   3. Root position from posed_joints[:, 0, :] (joint 0 is Hips in both).
        if "posed_joints" not in output or "global_rot_mats" not in output:
            return {
                "status": "error",
                "error": f"unexpected model output keys: {list(output.keys())}",
            }
        posed_joints = output["posed_joints"]
        global_rot_mats = output["global_rot_mats"]
        if posed_joints.ndim != 4 or global_rot_mats.ndim != 5:
            return {
                "status": "error",
                "error": (
                    f"unexpected shapes: posed_joints={posed_joints.shape}, "
                    f"global_rot_mats={global_rot_mats.shape}"
                ),
            }

        # Step 1: 77-joint local rotation matrices.
        joints_pos_t = torch.from_numpy(posed_joints[0]).to(device)
        if "local_rot_mats" in output:
            local_rot_mats_77 = torch.from_numpy(output["local_rot_mats"][0]).to(device)
        else:
            from kimodo.skeleton import global_rots_to_local_rots

            joints_rot_t = torch.from_numpy(global_rot_mats[0]).to(device)
            # Use the somaskel77 kintree (joints_rot was emitted at 77 joints).
            local_rot_mats_77 = global_rots_to_local_rots(joints_rot_t, skeleton.somaskel77)

        # Step 2: 77 → 30 via the official slicing helper.
        local_rot_mats_30 = skeleton.from_SOMASkeleton77(local_rot_mats_77)
        # `@ensure_batched` may have added a leading batch dim; drop it if so.
        if local_rot_mats_30.ndim == 5 and local_rot_mats_30.shape[0] == 1:
            local_rot_mats_30 = local_rot_mats_30[0]
        local_rot_mats = local_rot_mats_30.detach().cpu().numpy().astype(np.float32)

        # Step 2b: same slice on the global rotation tensor. from_SOMASkeleton77
        # is just per-joint indexing (definitions.py:from_SOMASkeleton77), so it
        # works on globals too. Webapp uses this for FK-parity validation.
        joints_rot_t = torch.from_numpy(global_rot_mats[0]).to(device)
        global_rot_mats_30_t = skeleton.from_SOMASkeleton77(joints_rot_t)
        if global_rot_mats_30_t.ndim == 5 and global_rot_mats_30_t.shape[0] == 1:
            global_rot_mats_30_t = global_rot_mats_30_t[0]
        global_rot_mats_30 = (
            global_rot_mats_30_t.detach().cpu().numpy().astype(np.float32)
        )

        # Step 3: root translation = Hips (joint 0) in posed_joints.
        root_translation = (
            joints_pos_t[:, 0, :].detach().cpu().numpy().astype(np.float32)
        )

        # Spot-check the SOMA shape: 30 joints expected for SOMA-RP-v1.1.
        T, J = local_rot_mats.shape[0], local_rot_mats.shape[1]
        if (T, J) != (n, 30):
            return {
                "status": "error",
                "error": (
                    f"expected ({n}, 30, 3, 3) for local_rot_mats, got "
                    f"{local_rot_mats.shape}"
                ),
            }

        # Optional foot_contacts if the model emitted them.
        foot_contacts_out = None
        if "foot_contacts" in output:
            fc = output["foot_contacts"]
            # Drop the leading sample dim if present -> [T, 4]
            if fc.ndim == 3:
                fc = fc[0]
            fc = np.asarray(fc, dtype=np.float32)
            # 4 -> 6 channel expand for SOMA-77 (mirrors
            # kimodo.skeleton.definitions.output_to_SOMASkeleton77):
            # [L_heel, L_toe, L_toe_end(=L_toe), R_heel, R_toe, R_toe_end(=R_toe)]
            fc6 = np.concatenate(
                [fc[..., :2], fc[..., 1:2], fc[..., 2:4], fc[..., 3:4]], axis=-1
            )
            foot_contacts_out = fc6.tolist()

        progress(1.0, desc="Done")
        return {
            "status": "ok",
            "numFrames": int(T),
            "fps": int(getattr(model, "fps", 30)),
            "rootTranslation": root_translation.tolist(),
            "jointRotMats": local_rot_mats.tolist(),
            "globalRotMats": global_rot_mats_30.tolist(),
            "footContacts": foot_contacts_out,
            "summary": prompt.strip(),
        }
    except Exception as e:
        traceback.print_exc()
        return {"status": "error", "error": f"{type(e).__name__}: {e}"}


def _historical_probe_g1(progress: gr.Progress = gr.Progress()) -> dict:  # noqa: B008
    """Historical probe — confirmed Kimodo-G1-RP-v1 model loads cleanly and
    g1skel34 ships with per-link STL meshes (~30 MB total) at
    /usr/local/lib/python3.10/dist-packages/kimodo/assets/skeletons/g1skel34/.
    G1 has 34 DOF (pelvis, hips, knees, ankles+toes, waist 3-axis, shoulders
    3-axis, elbows, wrists 3-axis, hand-roll). G1 motion needs a different
    renderer (rigid links transformed by joint rotations vs SOMA's LBS skin).
    Kept as documentation only; not registered as a Gradio endpoint.
    """
    import importlib
    import os

    try:
        out: dict = {"status": "ok"}
        import kimodo as kpkg
        root = os.path.dirname(kpkg.__file__)

        # 1) Look for any g1 / G1 assets on disk.
        candidates: list[str] = []
        for dirpath, _dn, filenames in os.walk(root):
            for fn in filenames:
                low = fn.lower()
                if "g1" in low or "g1" in dirpath.lower():
                    full = os.path.join(dirpath, fn)
                    try:
                        sz = os.path.getsize(full)
                    except OSError:
                        sz = -1
                    candidates.append(f"{full}\t{sz}")
        out["g1_files"] = candidates[:200]

        # 2) Try importing G1-related modules.
        for mn in ("kimodo.viz.g1_skin", "kimodo.skeleton", "kimodo.skeleton.g1", "kimodo.assets.skeletons.g1"):
            try:
                mod = importlib.import_module(mn)
                out[f"{mn}_attrs"] = [a for a in dir(mod) if not a.startswith("_")][:60]
            except Exception as e:
                out[f"{mn}_err"] = f"{type(e).__name__}: {e}"

        # 3) Try loading a G1 model.
        progress(0.5, desc="Trying to load G1 model ...")
        try:
            from kimodo import load_model
            g1_model, g1_resolved = load_model("Kimodo-G1-RP-v1", device="cpu", default_family="Kimodo", return_resolved_name=True)
            out["g1_model_resolved"] = g1_resolved
            out["g1_model_attrs"] = [a for a in dir(g1_model) if not a.startswith("_")][:50]
            sk = getattr(g1_model, "skeleton", None)
            if sk is not None:
                out["g1_skeleton_type"] = type(sk).__name__
                out["g1_skeleton_attrs"] = [a for a in dir(sk) if not a.startswith("_")][:80]
                # Try the standard "joint count" attr names.
                for k in ("bone_order_names", "joint_names", "names"):
                    v = getattr(sk, k, None)
                    if v is not None:
                        out[f"g1_skeleton_{k}"] = list(v)
                        break
        except Exception as e:
            out["g1_load_err"] = f"{type(e).__name__}: {e}"

        return out
    except Exception as e:
        traceback.print_exc()
        return {"status": "error", "error": f"{type(e).__name__}: {e}"}


def kimodo_motion_seq(
    prompts_json: str,
    frames_json: str,
    seed: int,
    cfg: float,
    num_steps: int,
    constraints_json: str,
    transition_frames: int = 20,
    progress: gr.Progress = gr.Progress(),  # noqa: B008
) -> dict:
    """Multi-prompt sequence variant of kimodo_motion. Generates a single
    motion that transitions through each prompt segment in order.

    prompts_json: JSON list of strings, e.g. '["walk forward", "wave hello"]'
    frames_json:  JSON list of ints (per-segment frame counts), same length.
    transition_frames: how many frames the model uses to blend between segments.

    Returns the same envelope as kimodo_motion. The total numFrames is
    sum(frames). If a single segment is provided this is equivalent to
    kimodo_motion.
    """
    try:
        prompts = json.loads(prompts_json) if prompts_json else []
        if not isinstance(prompts, list) or not all(isinstance(p, str) and p.strip() for p in prompts):
            return {"status": "error", "error": "prompts_json must be a JSON list of non-empty strings"}
        frames = json.loads(frames_json) if frames_json else []
        if not isinstance(frames, list) or len(frames) != len(prompts) or not all(isinstance(n, int) and 1 <= n <= 300 for n in frames):
            return {"status": "error", "error": "frames_json must be a JSON list of ints (1..300) matching prompts length"}
        total_n = sum(frames)
        if total_n > 600:
            return {"status": "error", "error": f"total frames {total_n} exceeds 600 cap"}

        try:
            raw = json.loads(constraints_json) if constraints_json else []
            parse_constraints(raw, total_n)
        except (ValueError, json.JSONDecodeError) as e:
            return {"status": "error", "error": f"constraint validation: {e}"}

        progress(0.02, desc="Loading model...")
        model, skeleton, device = _load_model()

        from kimodo.constraints import load_constraints_lst
        constraint_lst = load_constraints_lst(raw, skeleton, device=device)

        if seed is not None and int(seed) >= 0:
            from kimodo.tools import seed_everything
            seed_everything(int(seed))

        progress(0.10, desc=f"Diffusion ({len(prompts)} segments × {int(num_steps)} steps)...")
        cfg_kwargs = {"cfg_type": "regular", "cfg_weight": float(cfg)}
        output = model(
            [p.strip() for p in prompts],
            list(frames),
            constraint_lst=constraint_lst,
            num_denoising_steps=int(num_steps),
            num_samples=1,
            multi_prompt=True,
            num_transition_frames=int(transition_frames),
            return_numpy=True,
            **cfg_kwargs,
        )

        progress(0.92, desc="Serializing...")
        if "posed_joints" not in output or "global_rot_mats" not in output:
            return {"status": "error", "error": f"unexpected model output keys: {list(output.keys())}"}

        posed_joints = output["posed_joints"]
        global_rot_mats = output["global_rot_mats"]
        joints_pos_t = torch.from_numpy(posed_joints[0]).to(device)
        if "local_rot_mats" in output:
            local_rot_mats_77 = torch.from_numpy(output["local_rot_mats"][0]).to(device)
        else:
            from kimodo.skeleton import global_rots_to_local_rots
            joints_rot_t = torch.from_numpy(global_rot_mats[0]).to(device)
            local_rot_mats_77 = global_rots_to_local_rots(joints_rot_t, skeleton.somaskel77)
        local_rot_mats_30 = skeleton.from_SOMASkeleton77(local_rot_mats_77)
        if local_rot_mats_30.ndim == 5 and local_rot_mats_30.shape[0] == 1:
            local_rot_mats_30 = local_rot_mats_30[0]
        local_rot_mats = local_rot_mats_30.detach().cpu().numpy().astype(np.float32)

        # Same slice on global_rot_mats; webapp uses this for FK-parity validation.
        joints_rot_t = torch.from_numpy(global_rot_mats[0]).to(device)
        global_rot_mats_30_t = skeleton.from_SOMASkeleton77(joints_rot_t)
        if global_rot_mats_30_t.ndim == 5 and global_rot_mats_30_t.shape[0] == 1:
            global_rot_mats_30_t = global_rot_mats_30_t[0]
        global_rot_mats_30 = (
            global_rot_mats_30_t.detach().cpu().numpy().astype(np.float32)
        )

        root_translation = joints_pos_t[:, 0, :].detach().cpu().numpy().astype(np.float32)

        T, J = local_rot_mats.shape[0], local_rot_mats.shape[1]
        # Note: the model may return slightly more or fewer frames than total_n
        # depending on transition handling; report whatever it gave us.
        foot_contacts_out = None
        if "foot_contacts" in output:
            fc = output["foot_contacts"]
            # Drop the leading sample dim if present -> [T, 4]
            if fc.ndim == 3:
                fc = fc[0]
            fc = np.asarray(fc, dtype=np.float32)
            # 4 -> 6 channel expand for SOMA-77 (mirrors
            # kimodo.skeleton.definitions.output_to_SOMASkeleton77):
            # [L_heel, L_toe, L_toe_end(=L_toe), R_heel, R_toe, R_toe_end(=R_toe)]
            fc6 = np.concatenate(
                [fc[..., :2], fc[..., 1:2], fc[..., 2:4], fc[..., 3:4]], axis=-1
            )
            foot_contacts_out = fc6.tolist()

        progress(1.0, desc="Done")
        return {
            "status": "ok",
            "numFrames": int(T),
            "fps": int(getattr(model, "fps", 30)),
            "rootTranslation": root_translation.tolist(),
            "jointRotMats": local_rot_mats.tolist(),
            "globalRotMats": global_rot_mats_30.tolist(),
            "footContacts": foot_contacts_out,
            "summary": " → ".join(p.strip() for p in prompts),
            "segments": [{"prompt": p.strip(), "frames": int(n)} for p, n in zip(prompts, frames)],
        }
    except Exception as e:
        traceback.print_exc()
        return {"status": "error", "error": f"{type(e).__name__}: {e}"}


def _historical_extract_soma_skin(progress: gr.Progress = gr.Progress()) -> dict:  # noqa: B008
    """One-shot dump of kimodo's somaskel77/skin_standard.npz to base64 so the
    webapp can ship a real SkinnedMesh. Already run; binaries live at
    genga-webapp/public/assets/soma/. Kept as build-history reference, NOT
    registered as a Gradio endpoint.
    """
    import base64
    import importlib
    import os

    try:
        progress(0.2, desc="Locating skin asset...")
        import kimodo as kpkg
        root = os.path.dirname(kpkg.__file__)
        skin_path = os.path.join(root, "assets/skeletons/somaskel77/skin_standard.npz")
        out: dict = {"status": "ok", "skin_path": skin_path, "exists": os.path.isfile(skin_path)}
        if not out["exists"]:
            return {"status": "error", "error": f"missing {skin_path}"}

        progress(0.4, desc="Loading skin npz ...")
        skin = np.load(skin_path, allow_pickle=True)
        out["skin_keys"] = sorted(list(skin.files))
        shapes: dict = {}
        for k in skin.files:
            arr = skin[k]
            shapes[k] = {"shape": list(arr.shape), "dtype": str(arr.dtype)}
        out["skin_shapes"] = shapes

        # Inspect the viz modules so we know how to use this asset.
        for mn in ("kimodo.viz.soma_skin", "kimodo.viz.soma_layer_skin", "kimodo.viz.smplx_skin"):
            try:
                mod = importlib.import_module(mn)
                out[f"{mn}_attrs"] = [a for a in dir(mod) if not a.startswith("_")]
            except Exception as e:
                out[f"{mn}_err"] = f"{type(e).__name__}: {e}"

        # Try to load via soma_skin module (it likely has a builder fn).
        try:
            soma_skin = importlib.import_module("kimodo.viz.soma_skin")
            # Source-grep would help; just dump the module source (small file).
            src_path = soma_skin.__file__
            with open(src_path, "r") as f:
                out["soma_skin_src"] = f.read()
        except Exception as e:
            out["soma_skin_src_err"] = f"{type(e).__name__}: {e}"

        # Same for the SMPL-X skin module.
        try:
            smplx_skin = importlib.import_module("kimodo.viz.smplx_skin")
            with open(smplx_skin.__file__, "r") as f:
                out["smplx_skin_src"] = f.read()
        except Exception as e:
            out["smplx_skin_src_err"] = f"{type(e).__name__}: {e}"

        # Encode the most important arrays as base64 so the webapp can fetch
        # in one round-trip if the shapes look right (V_template, faces, weights).
        progress(0.85, desc="Encoding ...")
        encoded: dict = {}
        for k in skin.files:
            arr = np.ascontiguousarray(skin[k])
            encoded[k] = {
                "dtype": str(arr.dtype),
                "shape": list(arr.shape),
                "b64": base64.b64encode(arr.tobytes()).decode("ascii"),
            }
        out["skin_encoded"] = encoded

        # Also dump skeleton.neutral_joints (the real SOMA-30 rest pose).
        try:
            model, skeleton, _ = _load_model()
            nj = skeleton.neutral_joints.detach().cpu().numpy().astype(np.float32)
            out["soma30_neutral_joints"] = nj.tolist()
        except Exception as e:
            out["neutral_joints_err"] = f"{type(e).__name__}: {e}"

        return out
    except Exception as e:
        traceback.print_exc()
        return {"status": "error", "error": f"{type(e).__name__}: {e}"}


def _historical_probe_soma_body(progress: gr.Progress = gr.Progress()) -> dict:  # noqa: B008
    """One-shot kimodo introspection that found skin_standard.npz. Kept as
    build-history reference, NOT registered as a Gradio endpoint.

    Aggressive probe — walks the kimodo package's filesystem and importable
    submodules looking for any body-model assets (v_template / faces / lbs_weights /
    J_regressor) so the webapp can ship a smooth SkinnedMesh instead of a
    procedural capsule humanoid.

    Returns paths + first-discovered attribute hits + a mapping of any candidate
    objects we find. We iterate from there.
    """
    import importlib
    import os
    import pkgutil
    import sys

    try:
        progress(0.1, desc="Loading model + walking package...")
        model, skeleton, device = _load_model()

        out: dict = {"status": "ok"}

        # 1) Filesystem scan: list every .pkl/.npz/.npy/.obj/.ply/.glb/.json
        # under the kimodo package root + the HF snapshot caches.
        roots: list[str] = []
        try:
            import kimodo as _k  # noqa: F401
            roots.append(os.path.dirname(_k.__file__))
        except Exception:
            pass
        for env_var in ("HF_HOME", "XDG_CACHE_HOME"):
            v = os.environ.get(env_var)
            if v and os.path.isdir(v):
                roots.append(v)

        body_exts = (".pkl", ".npz", ".npy", ".obj", ".ply", ".glb", ".gltf")
        candidates: list[str] = []
        for root in roots:
            for dirpath, _dirnames, filenames in os.walk(root):
                for fn in filenames:
                    low = fn.lower()
                    if any(low.endswith(e) for e in body_exts) or "smpl" in low or "soma" in low or "body" in low or "template" in low:
                        full = os.path.join(dirpath, fn)
                        try:
                            sz = os.path.getsize(full)
                        except OSError:
                            sz = -1
                        candidates.append(f"{full}\t{sz}")
                        if len(candidates) > 400:
                            break
                if len(candidates) > 400:
                    break
        out["fs_candidates_count"] = len(candidates)
        out["fs_candidates"] = candidates[:300]

        # 2) Importable submodule walk under `kimodo`. Catch import errors
        # silently; we want every reachable attribute name to inspect.
        try:
            import kimodo as kpkg
            mods: list[str] = [kpkg.__name__]
            for finder, name, ispkg in pkgutil.walk_packages(kpkg.__path__, prefix=kpkg.__name__ + "."):
                mods.append(name)
            out["module_count"] = len(mods)

            # Look for submodules whose name contains body/mesh/smpl/template.
            interesting = [m for m in mods if any(k in m for k in ("body", "mesh", "smpl", "template", "skin", "asset"))]
            out["interesting_modules"] = interesting[:60]

            # Try importing each interesting one and dump attribute names.
            mod_attrs: dict[str, list[str]] = {}
            for m in interesting[:20]:
                try:
                    mod = importlib.import_module(m)
                    mod_attrs[m] = [a for a in dir(mod) if not a.startswith("_")][:40]
                except Exception as e:
                    mod_attrs[m] = [f"<import failed: {type(e).__name__}: {e!s:.80}>"]
            out["module_attrs"] = mod_attrs
        except Exception as e:
            out["module_walk_err"] = f"{type(e).__name__}: {e}"

        # 3) Probe model + skeleton internals for any object that looks like a
        # body model (recursively, one level deep on attributes).
        progress(0.6, desc="Probing model attrs ...")
        candidates_attrs: list[dict] = []

        def _probe_obj(name: str, obj, depth=0) -> None:
            if depth > 1 or obj is None:
                return
            for attr in dir(obj):
                if attr.startswith("_"):
                    continue
                try:
                    v = getattr(obj, attr, None)
                except Exception:
                    continue
                if v is None:
                    continue
                # Detect tensor-like body model attrs.
                cls = type(v).__name__
                if hasattr(v, "shape") and hasattr(v, "ndim"):
                    shape = list(getattr(v, "shape", []))
                    if shape and len(shape) <= 3 and all(isinstance(d, int) for d in shape):
                        if shape[0] in (6890, 10475, 10778) or (len(shape) >= 2 and shape[1] in (3, 30, 24, 52, 55)):
                            candidates_attrs.append({
                                "path": f"{name}.{attr}",
                                "cls": cls,
                                "shape": shape,
                            })
                # Recurse into module-like objects with body/mesh in the type name.
                lower_cls = cls.lower()
                if depth == 0 and any(k in lower_cls for k in ("body", "mesh", "smpl", "skel")):
                    _probe_obj(f"{name}.{attr}", v, depth + 1)

        _probe_obj("model", model)
        _probe_obj("skeleton", skeleton)
        out["tensor_candidates"] = candidates_attrs[:60]

        # 4) Try importing `smplx` / `smpl` / `body_models` modules that kimodo
        # might rely on as soft deps.
        soft_deps = {}
        for name in ("smplx", "smpl", "body_models", "body_visualizer", "human_body_prior"):
            try:
                m = importlib.import_module(name)
                soft_deps[name] = {"path": getattr(m, "__file__", None), "attrs": [a for a in dir(m) if not a.startswith("_")][:30]}
            except Exception as e:
                soft_deps[name] = f"<not importable: {type(e).__name__}>"
        out["soft_deps"] = soft_deps

        return out
    except Exception as e:
        traceback.print_exc()
        return {"status": "error", "error": f"{type(e).__name__}: {e}"}


with gr.Blocks(title="Genga Kimodo") as demo:
    gr.Markdown(
        "# Genga × Kimodo\n"
        "API-only Space. Inference endpoint at `/gradio_api/call/kimodo_motion`.\n\n"
        "This Space backs the GengaMachines webapp and is not a public sandbox. "
        "For the official interactive Kimodo demo, see "
        "[nvidia/Kimodo](https://huggingface.co/spaces/nvidia/Kimodo)."
    )
    in_prompt = gr.Textbox(label="Prompt", value="A person waves hello with their right hand.")
    in_frames = gr.Slider(30, 300, value=90, step=6, label="num_frames (30 fps)")
    in_seed = gr.Number(value=42, label="seed (use -1 to skip seeding)", precision=0)
    in_cfg = gr.Slider(1.0, 10.0, value=5.0, step=0.5, label="cfg_weight")
    in_steps = gr.Slider(10, 50, value=30, step=1, label="num_denoising_steps")
    in_constraints = gr.Textbox(label="constraints_json", value="[]", lines=4)
    btn = gr.Button("Generate")
    out = gr.JSON(label="result")

    btn.click(
        fn=kimodo_motion,
        inputs=[in_prompt, in_frames, in_seed, in_cfg, in_steps, in_constraints],
        outputs=out,
        api_name="kimodo_motion",
    )

    # Multi-prompt sequence endpoint — header-only inputs (no UI form widgets;
    # the webapp posts JSON directly to /gradio_api/call/kimodo_motion_seq).
    in_prompts_json = gr.Textbox(label="prompts_json", value='["A person walks forward","A person waves hello"]', visible=False)
    in_frames_json = gr.Textbox(label="frames_json", value="[45,45]", visible=False)
    in_transition = gr.Number(value=20, label="transition_frames", precision=0, visible=False)
    out_seq = gr.JSON(label="seq result", visible=False)
    seq_btn = gr.Button("Generate sequence", visible=False)
    seq_btn.click(
        fn=kimodo_motion_seq,
        inputs=[in_prompts_json, in_frames_json, in_seed, in_cfg, in_steps, in_constraints, in_transition],
        outputs=out_seq,
        api_name="kimodo_motion_seq",
    )




if __name__ == "__main__":
    demo.queue(max_size=4).launch(
        server_name="0.0.0.0",
        server_port=int(os.environ.get("PORT", 7860)),
    )