Add native demo UI modules with Qwen 10-action example flow

kimodo/demo/__init__.py

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+# Demo module for native Kimodo UI integration in Space builds.

kimodo/demo/_qwen_prompts.py

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Qwen-on-Fireworks helper for auto-generating multi-text-prompt batches."""
+from __future__ import annotations
+
+import json
+import os
+import re
+import urllib.error
+import urllib.request
+
+_MODEL = "accounts/fireworks/models/qwen3p6-27b"
+_BASE  = "https://api.fireworks.ai/inference/v1"
+
+_SYSTEM = """\
+You are a motion-description writer for a single humanoid character in a 3D animation system.
+Given a scene context and the character's recent motion history, output ONLY a JSON object:
+
+{"texts": ["<action phrase 1>", ...], "durations": [<seconds float>, ...]}
+
+Rules:
+- Return between 1 and requested_actions short, vivid action phrases that flow naturally from each other.
+- Each phrase describes one distinct physical motion (e.g. "walks forward briskly", "pivots left and crouches").
+- Each duration is between 2.0 and 8.0 seconds.
+- texts and durations must have the same length.
+- Do NOT repeat phrases from history.
+- Return raw JSON only — no markdown, no explanation.
+"""
+
+
+def _call_fireworks(messages: list[dict]) -> str:
+    api_key = os.environ.get("FIREWORKS_API_KEY", "").strip()
+    if not api_key:
+        raise RuntimeError("FIREWORKS_API_KEY is not set")
+    body = json.dumps({
+        "model": _MODEL,
+        "messages": messages,
+        "max_tokens": 400,
+        "temperature": 0.85,
+    }).encode()
+    req = urllib.request.Request(
+        f"{_BASE}/chat/completions",
+        data=body,
+        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+        method="POST",
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=40) as r:
+            return json.loads(r.read())["choices"][0]["message"]["content"]
+    except urllib.error.HTTPError as e:
+        raise RuntimeError(f"Fireworks {e.code}: {e.read().decode(errors='ignore')}") from e
+
+
+def _parse(raw: str) -> dict:
+    text = raw.strip()
+    m = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
+    text = m.group(1) if m else text
+    s, e = text.find("{"), text.rfind("}")
+    return json.loads(text[s:e + 1])
+
+
+def _fallback(offset: int) -> dict:
+    phrases = [
+        "walks forward at a steady pace",
+        "turns smoothly to the left",
+        "pauses and surveys the surroundings",
+        "steps forward and gestures expressively",
+        "crouches down then rises back up",
+        "sidesteps to the right with purpose",
+    ]
+    n = len(phrases)
+    return {
+        "texts": [phrases[(offset + i) % n] for i in range(3)],
+        "durations": [3.0, 3.5, 3.0],
+    }
+
+
+def call_qwen_for_prompts(
+    scene: str,
+    history: list[str],
+    requested_actions: int = 5,
+) -> tuple[dict, list[str]]:
+    """Call Qwen to produce the next batch of motion prompts.
+
+    Returns (batch_dict, updated_history).
+    batch_dict has keys "texts" and "durations".
+    Raises RuntimeError on API failure (caller may fall back).
+    """
+    user_msg = (
+        f"Scene: {scene or 'a character moving continuously in 3D space'}\n"
+        f"Motion history (do not repeat): {json.dumps(history[-12:])}\n\n"
+        f"requested_actions: {max(1, min(10, int(requested_actions)))}\n"
+        "Generate the next batch of motion prompts."
+    )
+    try:
+        raw = _call_fireworks([{"role": "system", "content": _SYSTEM}, {"role": "user", "content": user_msg}])
+        batch = _parse(raw)
+        if not isinstance(batch.get("texts"), list) or not isinstance(batch.get("durations"), list):
+            raise ValueError("Missing texts or durations")
+        n = min(len(batch["texts"]), len(batch["durations"]))
+        batch["texts"] = batch["texts"][:n]
+        batch["durations"] = batch["durations"][:n]
+    except Exception:
+        batch = _fallback(len(history))
+
+    new_history = history + list(batch["texts"])
+    return batch, new_history