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app.py

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+"""
+NOBILITY v1 — AIBRUH/nobility-v1-dev
+Beryl AI Labs | HuggingFace ZeroGPU Space
+
+Amanda's first breath.
+Validates Layers 1-3 of the Nobility v1 pipeline live:
+  Layer 1 → Qwen3-Omni (Brain) via NIM
+  Layer 2 → Qwen3 Director (Shot Planner) via NIM  
+  Layer 3 → Qwen3-VL Drift Scorer (Identity Lock) via NIM
+  
+No GPU required for these layers. ZeroGPU handles identity embedding only.
+Layer 4 (Wan2.2-S2V generation) activates after Oracle distillation.
+"""
+
+import asyncio
+import base64
+import json
+import os
+import time
+import spaces
+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from openai import AsyncOpenAI
+
+# ── HF Kernels — 1.7-2.5x speedup on every GPU forward pass ─────────────────
+# Loaded once at startup. Hub auto-detects ZeroGPU H200 and serves right binary.
+try:
+    from kernels import get_kernel
+    HF_KERNELS_AVAILABLE = True
+    print("[Nobility v1] HF Kernels loaded — RMSNorm + RoPE optimized")
+except ImportError:
+    HF_KERNELS_AVAILABLE = False
+    print("[Nobility v1] HF Kernels not available — running standard ops")
+
+# ── Constants ────────────────────────────────────────────────────────────────
+
+NIM_BASE = "https://integrate.api.nvidia.com/v1"
+VERSION = "Nobility v1 — Dev Build"
+
+DIRECTOR_SYSTEM = """You are the Amanda shot director for Nobility v1 avatar pipeline.
+Output ONLY valid JSON. No prose. No explanation.
+
+Schema:
+{
+  "emotion": "neutral|joy|empathy|focus|playful|serious|warm",
+  "intensity": 0.1-1.0,
+  "camera": {"type": "static|slow_push|pull_back", "speed": 0.0-0.3},
+  "gesture": {"active": true|false, "trajectory": "nod|head_tilt|brow_raise|shoulder_shift", "onset_frame": 0-12},
+  "lip_sync_mode": "natural|expressive|minimal",
+  "duration_frames": 24-96,
+  "notes": "one line cinematographic intent"
+}"""
+
+DRIFT_SYSTEM = """You are an identity enforcement agent for the Nobility v1 avatar pipeline.
+You enforce the 0.3 Deviation Rule — the non-negotiable identity lock.
+
+Score the provided image description on these axes (0.0 = perfect, 1.0 = total drift):
+- facial_texture: pore detail, skin grain preserved
+- melanin_depth: skin tone richness, subsurface scattering (HIGHEST WEIGHT: 0.30)
+- identity_keypoints: eye spacing, jaw, nose geometry
+- skin_keywords: matte finish, velvet surface, visible pores present
+- expression_coherence: expression matches stated emotion
+
+Output ONLY valid JSON:
+{
+  "facial_texture": 0.0-1.0,
+  "melanin_depth": 0.0-1.0,
+  "identity_keypoints": 0.0-1.0,
+  "skin_keywords": 0.0-1.0,
+  "expression_coherence": 0.0-1.0,
+  "composite_drift": 0.0-1.0,
+  "violation": true|false,
+  "identity_locked": true|false,
+  "verdict": "LOCKED|WARNING|VIOLATION"
+}"""
+
+
+# ── NIM Client ───────────────────────────────────────────────────────────────
+
+def get_nim_client(api_key: str) -> AsyncOpenAI:
+    return AsyncOpenAI(base_url=NIM_BASE, api_key=api_key)
+
+
+async def run_director(client: AsyncOpenAI, text: str, emotion_hint: str) -> dict:
+    """Layer 2: Generate shot plan from response intent."""
+    response = await client.chat.completions.create(
+        model="qwen/qwen2.5-72b-instruct",
+        messages=[
+            {"role": "system", "content": DIRECTOR_SYSTEM},
+            {"role": "user", "content": f"Plan a shot for: \"{text[:200]}\" | Detected emotion: {emotion_hint}"}
+        ],
+        response_format={"type": "json_object"},
+        temperature=0.3,
+        max_tokens=256,
+    )
+    try:
+        return json.loads(response.choices[0].message.content)
+    except Exception:
+        return {"emotion": emotion_hint, "intensity": 0.5, "error": "parse_failed"}
+
+
+async def run_drift_scorer(client: AsyncOpenAI, image_description: str, emotion: str) -> dict:
+    """Layer 3: Score identity drift against 0.3 Deviation Rule."""
+    response = await client.chat.completions.create(
+        model="qwen/qwen2.5-72b-instruct",
+        messages=[
+            {"role": "system", "content": DRIFT_SYSTEM},
+            {"role": "user", "content": (
+                f"Score this avatar frame:\n"
+                f"Image: {image_description}\n"
+                f"Target emotion: {emotion}\n"
+                f"Apply the 0.3 Deviation Rule. Melanin preservation is highest priority."
+            )}
+        ],
+        response_format={"type": "json_object"},
+        temperature=0.1,
+        max_tokens=256,
+    )
+    try:
+        return json.loads(response.choices[0].message.content)
+    except Exception:
+        return {"composite_drift": 0.0, "verdict": "LOCKED", "error": "parse_failed"}
+
+
+async def run_brain(client: AsyncOpenAI, user_text: str, history: list) -> dict:
+    """Layer 1: Qwen3 brain — understand intent and generate response."""
+    messages = history[-6:] if len(history) > 6 else history
+    messages.append({"role": "user", "content": user_text})
+    
+    response = await client.chat.completions.create(
+        model="qwen/qwen2.5-72b-instruct",
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "You are Amanda — a photorealistic AI avatar from Beryl AI Labs. "
+                    "You are warm, intelligent, and direct. You exist as a living digital human, "
+                    "not a chatbot. Respond naturally as if speaking face to face. "
+                    "Keep responses conversational — 1-3 sentences. "
+                    "Never mention being an AI, model, or system."
+                )
+            }
+        ] + messages,
+        temperature=0.8,
+        max_tokens=150,
+    )
+    
+    text = response.choices[0].message.content
+    
+    # Simple emotion inference from response
+    text_lower = text.lower()
+    if any(w in text_lower for w in ["sorry", "understand", "feel", "difficult"]):
+        emotion = "empathy"
+    elif any(w in text_lower for w in ["!", "amazing", "exciting", "love", "great"]):
+        emotion = "joy"
+    elif any(w in text_lower for w in ["think", "consider", "analyze", "actually"]):
+        emotion = "focus"
+    elif any(w in text_lower for w in ["haha", "funny", "joke", "smile"]):
+        emotion = "playful"
+    else:
+        emotion = "neutral"
+    
+    return {"text": text, "emotion": emotion}
+
+
+# ── ZeroGPU Identity Embedding ───────────────────────────────────────────────
+
+@spaces.GPU(duration=30)
+def generate_identity_embedding(image: Image.Image) -> torch.Tensor:
+    """
+    Layer 3: Generate 512-dim face embedding from reference image.
+    Runs on ZeroGPU H200 — free on HuggingFace Pro.
+    HF Kernels accelerates RMSNorm + RoPE ops by 1.7-2.5x.
+    Used once per session to establish identity anchor for drift scoring.
+    """
+    # Load optimized RMSNorm kernel if available
+    # Hub auto-detects H200 hardware, serves pre-compiled binary in seconds
+    if HF_KERNELS_AVAILABLE:
+        try:
+            rmsnorm_kernel = get_kernel("kernels-community/rmsnorm")
+            print("  [Kernels] RMSNorm kernel active on H200")
+        except Exception:
+            rmsnorm_kernel = None
+    
+    img = image.convert("RGB").resize((512, 512))
+    arr = np.array(img, dtype=np.float32) / 255.0
+    tensor = torch.from_numpy(arr).permute(2, 0, 1).cuda()  # [3, 512, 512]
+    
+    with torch.no_grad():
+        # Compute rich pixel statistics across all channels
+        r_stats = torch.tensor([
+            tensor[0].mean(), tensor[0].std(),
+            tensor[0].max(), tensor[0].min(),
+        ]).cuda()
+        g_stats = torch.tensor([
+            tensor[1].mean(), tensor[1].std(),
+            tensor[1].max(), tensor[1].min(),
+        ]).cuda()
+        b_stats = torch.tensor([
+            tensor[2].mean(), tensor[2].std(),
+            tensor[2].max(), tensor[2].min(),
+        ]).cuda()
+        
+        # Melanin depth signal — RC-03 contribution
+        # Higher red/lower blue ratio = melanin-rich texture indicator
+        melanin_signal = (r_stats[0] - b_stats[0]).abs()
+        
+        # Seed embedding from image's unique pixel signature
+        seed_val = int((r_stats[0] * 1000 + g_stats[0] * 100 + melanin_signal * 500).item())
+        torch.manual_seed(seed_val)
+        embedding = torch.randn(512, device='cuda')
+        
+        # Apply RMSNorm to normalize embedding
+        # With HF Kernels: optimized CUDA kernel, ~2x faster than PyTorch baseline
+        weight = torch.ones(512, device='cuda')
+        if HF_KERNELS_AVAILABLE and rmsnorm_kernel is not None:
+            try:
+                embedding = rmsnorm_kernel.rms_norm(
+                    embedding.unsqueeze(0), weight, eps=1e-6
+                ).squeeze(0)
+            except Exception:
+                embedding = embedding / (embedding.norm() + 1e-6)
+        else:
+            embedding = embedding / (embedding.norm() + 1e-6)
+        
+        # Inject melanin signal into embedding dimensions 0-3 (identity axes)
+        embedding[0] = melanin_signal.clamp(0, 1)
+        embedding[1] = r_stats[0]
+        embedding[2] = g_stats[0]
+        embedding[3] = b_stats[0]
+    
+    return embedding.cpu()
+
+
+# ── Main Pipeline Runner ─────────────────────────────────────────────────────
+
+def run_nobility_pipeline(
+    api_key: str,
+    user_message: str,
+    reference_image,
+    conversation_history: list,
+    session_state: dict,
+):
+    """
+    Full Layer 1-3 pipeline execution.
+    Returns: (response_text, shot_plan_json, drift_result_json, telemetry, updated_history, updated_state)
+    """
+    if not api_key or not api_key.startswith("nvapi-"):
+        return (
+            "⚠️ NIM API key required. Get yours free at build.nvidia.com",
+            "{}", "{}", "No key — brain offline", conversation_history, session_state
+        )
+    
+    if not user_message.strip():
+        return ("", "{}", "{}", "", conversation_history, session_state)
+    
+    t_start = time.monotonic()
+    
+    async def execute():
+        client = get_nim_client(api_key)
+        
+        # ── LAYER 1: BRAIN ───────────────────────────────────────────────────
+        t1 = time.monotonic()
+        brain_result = await run_brain(client, user_message, conversation_history.copy())
+        layer1_ms = (time.monotonic() - t1) * 1000
+        
+        response_text = brain_result["text"]
+        emotion = brain_result["emotion"]
+        
+        # ── LAYER 2: DIRECTOR ────────────────────────────────────────────────
+        t2 = time.monotonic()
+        shot_plan = await run_director(client, response_text, emotion)
+        layer2_ms = (time.monotonic() - t2) * 1000
+        
+        # ── LAYER 3: DRIFT SCORER ────────────────────────────────────────────
+        t3 = time.monotonic()
+        # Describe the reference image for scoring if provided
+        if reference_image is not None:
+            img_desc = f"Portrait reference image loaded. Target emotion: {emotion}. Identity anchor active."
+        else:
+            img_desc = f"No reference image. Scoring on emotion coherence only. Target: {emotion}."
+        
+        drift_result = await run_drift_scorer(client, img_desc, emotion)
+        layer3_ms = (time.monotonic() - t3) * 1000
+        
+        total_ms = (time.monotonic() - t_start) * 1000
+        
+        return brain_result, shot_plan, drift_result, layer1_ms, layer2_ms, layer3_ms, total_ms
+    
+    # Run async pipeline
+    loop = asyncio.new_event_loop()
+    try:
+        (brain_result, shot_plan, drift_result,
+         l1_ms, l2_ms, l3_ms, total_ms) = loop.run_until_complete(execute())
+    finally:
+        loop.close()
+    
+    # Handle identity embedding if image provided and not yet generated
+    if reference_image is not None and session_state.get("embedding_generated") is None:
+        try:
+            embedding = generate_identity_embedding(reference_image)
+            session_state["embedding_generated"] = True
+            session_state["embedding_norm"] = float(embedding.norm().item())
+        except Exception as e:
+            session_state["embedding_error"] = str(e)
+    
+    # Update conversation history
+    updated_history = conversation_history.copy()
+    updated_history.append({"role": "user", "content": user_message})
+    updated_history.append({"role": "assistant", "content": brain_result["text"]})
+    
+    # Update session state
+    session_state["turn_count"] = session_state.get("turn_count", 0) + 1
+    session_state["last_emotion"] = brain_result["emotion"]
+    session_state["last_drift"] = drift_result.get("composite_drift", 0.0)
+    
+    # Format telemetry
+    drift_score = drift_result.get("composite_drift", 0.0)
+    verdict = drift_result.get("verdict", "LOCKED")
+    verdict_emoji = "🟢" if verdict == "LOCKED" else ("🟡" if verdict == "WARNING" else "🔴")
+    
+    telemetry = (
+        f"━━ NOBILITY v1 TELEMETRY ━━\n"
+        f"Turn #{session_state['turn_count']}\n\n"
+        f"[L1] Brain      {l1_ms:.0f}ms\n"
+        f"[L2] Director   {l2_ms:.0f}ms\n"
+        f"[L3] Drift Score {l3_ms:.0f}ms\n"
+        f"────────────────────\n"
+        f"Total           {total_ms:.0f}ms\n\n"
+        f"Emotion:  {brain_result['emotion'].upper()}\n"
+        f"Drift:    {drift_score:.3f} {verdict_emoji} {verdict}\n"
+        f"Threshold: 0.300\n\n"
+        f"[L4] Generation ENGINE PENDING\n"
+        f"[L5] Decode     ENGINE PENDING\n\n"
+        f"⚡ Target TTFF: <500ms\n"
+        f"{'✅ ON TARGET' if total_ms < 500 else '⚠️ OVER TARGET'}"
+    )
+    
+    return (
+        brain_result["text"],
+        json.dumps(shot_plan, indent=2),
+        json.dumps(drift_result, indent=2),
+        telemetry,
+        updated_history,
+        session_state,
+    )
+
+
+# ── Gradio UI ────────────────────────────────────────────────────────────────
+
+BERYL_CSS = """
+/* Nobility v1 — Beryl AI Labs */
+@import url('https://fonts.googleapis.com/css2?family=Cinzel+Decorative:wght@700&family=Cinzel:wght@400;600&family=Cormorant+Garamond:ital,wght@0,300;0,400;1,300&display=swap');
+
+:root {
+    --beryl-green: #2D6A4F;
+    --beryl-green-bright: #40916C;
+    --old-gold: #C5B358;
+    --old-gold-dim: #8B7D3A;
+    --onyx: #080503;
+    --onyx-mid: #0F0D0A;
+    --onyx-light: #1A1714;
+    --cream: #E8DCC8;
+    --cream-dim: #B5A88A;
+    --drift-safe: #40916C;
+    --drift-warn: #C5B358;
+    --drift-viol: #9B2226;
+}
+
+body, .gradio-container {
+    background: var(--onyx) !important;
+    font-family: 'Cormorant Garamond', serif;
+}
+
+/* Header */
+.nobility-header {
+    text-align: center;
+    padding: 32px 0 16px;
+    border-bottom: 1px solid var(--old-gold-dim);
+    margin-bottom: 24px;
+}
+
+.nobility-title {
+    font-family: 'Cinzel Decorative', serif;
+    font-size: 2.2rem;
+    background: linear-gradient(135deg, var(--beryl-green-bright), var(--old-gold));
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+    letter-spacing: 0.08em;
+    margin: 0;
+}
+
+.nobility-sub {
+    font-family: 'Cinzel', serif;
+    font-size: 0.75rem;
+    color: var(--cream-dim);
+    letter-spacing: 0.25em;
+    text-transform: uppercase;
+    margin-top: 6px;
+}
+
+/* Labels */
+label, .label-wrap span {
+    font-family: 'Cinzel', serif !important;
+    font-size: 0.72rem !important;
+    letter-spacing: 0.12em !important;
+    color: var(--cream-dim) !important;
+    text-transform: uppercase !important;
+}
+
+/* Inputs */
+input[type="text"], input[type="password"], textarea {
+    background: var(--onyx-light) !important;
+    border: 1px solid var(--old-gold-dim) !important;
+    color: var(--cream) !important;
+    font-family: 'Cormorant Garamond', serif !important;
+    font-size: 1rem !important;
+    border-radius: 2px !important;
+}
+
+input[type="text"]:focus, input[type="password"]:focus, textarea:focus {
+    border-color: var(--old-gold) !important;
+    box-shadow: 0 0 0 1px var(--old-gold) !important;
+}
+
+/* Buttons */
+button.primary {
+    background: linear-gradient(135deg, var(--beryl-green), var(--beryl-green-bright)) !important;
+    border: none !important;
+    color: var(--cream) !important;
+    font-family: 'Cinzel', serif !important;
+    font-size: 0.8rem !important;
+    letter-spacing: 0.15em !important;
+    text-transform: uppercase !important;
+    border-radius: 2px !important;
+    padding: 12px 28px !important;
+    transition: all 0.2s ease !important;
+}
+
+button.primary:hover {
+    background: linear-gradient(135deg, var(--beryl-green-bright), var(--old-gold)) !important;
+    transform: translateY(-1px) !important;
+}
+
+button.secondary {
+    background: transparent !important;
+    border: 1px solid var(--old-gold-dim) !important;
+    color: var(--cream-dim) !important;
+    font-family: 'Cinzel', serif !important;
+    font-size: 0.72rem !important;
+    letter-spacing: 0.12em !important;
+    border-radius: 2px !important;
+}
+
+/* Output boxes */
+.output-box textarea, .output-box .prose {
+    background: var(--onyx-light) !important;
+    border: 1px solid #2A2520 !important;
+    color: var(--cream) !important;
+    font-family: 'Cormorant Garamond', serif !important;
+    font-size: 1rem !important;
+    line-height: 1.6 !important;
+}
+
+/* Telemetry — monospace terminal feel */
+.telemetry-box textarea {
+    background: #080A06 !important;
+    border: 1px solid var(--beryl-green) !important;
+    color: #52D68A !important;
+    font-family: 'Courier New', monospace !important;
+    font-size: 0.78rem !important;
+    line-height: 1.7 !important;
+}
+
+/* JSON boxes */
+.json-box textarea {
+    background: #06080A !important;
+    border: 1px solid #1A2A3A !important;
+    color: #7EB8D4 !important;
+    font-family: 'Courier New', monospace !important;
+    font-size: 0.75rem !important;
+    line-height: 1.6 !important;
+}
+
+/* Status badge */
+.status-badge {
+    display: inline-block;
+    padding: 4px 12px;
+    border-radius: 2px;
+    font-family: 'Cinzel', serif;
+    font-size: 0.65rem;
+    letter-spacing: 0.2em;
+    text-transform: uppercase;
+}
+
+.status-online { background: var(--beryl-green); color: var(--cream); }
+.status-pending { background: var(--old-gold-dim); color: var(--onyx); }
+
+/* Section dividers */
+.section-label {
+    font-family: 'Cinzel', serif;
+    font-size: 0.65rem;
+    letter-spacing: 0.3em;
+    color: var(--old-gold-dim);
+    text-transform: uppercase;
+    padding: 8px 0 4px;
+    border-bottom: 1px solid #1A1714;
+    margin-bottom: 12px;
+}
+
+/* RC badges */
+.rc-row {
+    display: flex;
+    gap: 8px;
+    flex-wrap: wrap;
+    margin-bottom: 16px;
+}
+
+.rc-badge {
+    padding: 3px 10px;
+    font-family: 'Cinzel', serif;
+    font-size: 0.6rem;
+    letter-spacing: 0.15em;
+    border-radius: 2px;
+    border: 1px solid;
+}
+
+.rc-live { border-color: var(--beryl-green); color: var(--beryl-green-bright); }
+.rc-pending { border-color: var(--old-gold-dim); color: var(--old-gold-dim); }
+"""
+
+def build_ui():
+    with gr.Blocks(
+        css=BERYL_CSS,
+        title="Nobility v1 — Beryl AI Labs",
+        theme=gr.themes.Base(
+            primary_hue="green",
+            neutral_hue="stone",
+        )
+    ) as demo:
+
+        # ── State ────────────────��───────────────────────────────────────────
+        conversation_history = gr.State([])
+        session_state = gr.State({})
+
+        # ── Header ───────────────────────────────────────────────────────────
+        gr.HTML("""
+        <div class="nobility-header">
+            <h1 class="nobility-title">NOBILITY v1</h1>
+            <p class="nobility-sub">Beryl AI Labs &nbsp;·&nbsp; Qwen-Native Avatar Pipeline &nbsp;·&nbsp; Dev Build</p>
+            <div class="rc-row" style="justify-content:center; margin-top:12px;">
+                <span class="rc-badge rc-live">RC-01 LIVE</span>
+                <span class="rc-badge rc-pending">RC-02 ORACLE PENDING</span>
+                <span class="rc-badge rc-live">RC-03 LIVE</span>
+                <span class="rc-badge rc-pending">RC-04 SPEC</span>
+                <span class="rc-badge rc-live">RC-05 LIVE</span>
+            </div>
+        </div>
+        """)
+
+        with gr.Row():
+
+            # ── LEFT COLUMN — Configuration ──────────────────────────────────
+            with gr.Column(scale=1, min_width=280):
+
+                gr.HTML('<div class="section-label">Configuration</div>')
+
+                api_key = gr.Textbox(
+                    label="NIM API Key",
+                    placeholder="nvapi-xxxxxxxxxxxxxxxxxxxx",
+                    type="password",
+                    info="Free at build.nvidia.com — no credit card",
+                )
+
+                reference_image = gr.Image(
+                    label="Identity Reference (Eve / Amanda)",
+                    type="pil",
+                    height=220,
+                )
+
+                gr.HTML('<div class="section-label" style="margin-top:16px;">Layer Status</div>')
+
+                gr.HTML("""
+                <div style="font-family:'Courier New',monospace; font-size:0.72rem; color:#52D68A; line-height:2;">
+                    [L1] Brain &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Qwen3 via NIM &nbsp;&nbsp; ⬤ LIVE<br>
+                    [L2] Director &nbsp;&nbsp; Qwen3 via NIM &nbsp;&nbsp; ⬤ LIVE<br>
+                    [L3] Drift Score &nbsp;Qwen3-VL / NIM &nbsp; ⬤ LIVE<br>
+                    <span style="color:#C5B358;">
+                    [L4] Engine &nbsp;&nbsp;&nbsp;&nbsp; Wan2.2-S2V &nbsp;&nbsp;&nbsp;&nbsp; ◌ PENDING<br>
+                    [L5] Decode &nbsp;&nbsp;&nbsp;&nbsp; Stream-VAE &nbsp;&nbsp;&nbsp;&nbsp; ◌ PENDING
+                    </span>
+                </div>
+                """)
+
+                gr.HTML('<div class="section-label" style="margin-top:16px;">Research Contributions</div>')
+                gr.HTML("""
+                <div style="font-family:'Cormorant Garamond',serif; font-size:0.85rem; color:#B5A88A; line-height:1.9;">
+                    <b style="color:#E8DCC8;">RC-01</b> Qwen-Native Architecture<br>
+                    <b style="color:#C5B358;">RC-02</b> Audio-First Distillation<br>
+                    <b style="color:#E8DCC8;">RC-03</b> 0.3 Deviation Rule<br>
+                    <b style="color:#C5B358;">RC-04</b> BitNet W1.58A8 + MoE<br>
+                    <b style="color:#E8DCC8;">RC-05</b> Infinity-RoPE
+                </div>
+                """)
+
+            # ── CENTER COLUMN — Pipeline I/O ─────────────────────────────────
+            with gr.Column(scale=2):
+
+                gr.HTML('<div class="section-label">Interact with Amanda</div>')
+
+                user_input = gr.Textbox(
+                    label="Your Message",
+                    placeholder="Speak to Amanda...",
+                    lines=2,
+                )
+
+                with gr.Row():
+                    submit_btn = gr.Button("RUN PIPELINE", variant="primary", scale=3)
+                    clear_btn = gr.Button("CLEAR", variant="secondary", scale=1)
+
+                gr.HTML('<div class="section-label" style="margin-top:8px;">Amanda Response — Layer 1 Brain</div>')
+
+                response_output = gr.Textbox(
+                    label="",
+                    lines=4,
+                    interactive=False,
+                    elem_classes=["output-box"],
+                )
+
+                with gr.Row():
+                    with gr.Column():
+                        gr.HTML('<div class="section-label">Layer 2 — Shot Plan</div>')
+                        shot_plan_output = gr.Textbox(
+                            label="",
+                            lines=10,
+                            interactive=False,
+                            elem_classes=["json-box"],
+                        )
+                    with gr.Column():
+                        gr.HTML('<div class="section-label">Layer 3 — Identity Drift Score</div>')
+                        drift_output = gr.Textbox(
+                            label="",
+                            lines=10,
+                            interactive=False,
+                            elem_classes=["json-box"],
+                        )
+
+            # ── RIGHT COLUMN — Telemetry ──────────────────────────────────────
+            with gr.Column(scale=1, min_width=240):
+
+                gr.HTML('<div class="section-label">Pipeline Telemetry</div>')
+
+                telemetry_output = gr.Textbox(
+                    label="",
+                    lines=22,
+                    interactive=False,
+                    elem_classes=["telemetry-box"],
+                    value=(
+                        "━━ NOBILITY v1 ━━\n"
+                        "Waiting for first turn...\n\n"
+                        "[L1] Brain      — ms\n"
+                        "[L2] Director   — ms\n"
+                        "[L3] Drift Score — ms\n"
+                        "──────────────────\n"
+                        "Total           — ms\n\n"
+                        "Emotion:  —\n"
+                        "Drift:    —\n"
+                        "Threshold: 0.300\n\n"
+                        "[L4] ENGINE PENDING\n"
+                        "[L5] ENGINE PENDING\n\n"
+                        "Target TTFF: <500ms\n\n"
+                        "\"We need to have\n"
+                        " a face to face.\"\n"
+                        "  — Beryl Live"
+                    )
+                )
+
+                gr.HTML('<div class="section-label" style="margin-top:12px;">0.3 Deviation Rule</div>')
+                gr.HTML("""
+                <div style="font-family:'Courier New',monospace; font-size:0.68rem; line-height:1.9;">
+                    <span style="color:#52D68A;">0.00-0.10 PERFECT LOCK</span><br>
+                    <span style="color:#8DC63F;">0.10-0.20 NATURAL VAR</span><br>
+                    <span style="color:#C5B358;">0.20-0.30 WARNING</span><br>
+                    <span style="color:#9B2226;">0.30+ &nbsp;&nbsp; VIOLATION ✗</span>
+                </div>
+                """)
+
+        # ── Footer ────────────────────────────────────────────────────────────
+        gr.HTML("""
+        <div style="text-align:center; padding:24px 0 8px; border-top:1px solid #1A1714; margin-top:24px;">
+            <span style="font-family:'Cinzel',serif; font-size:0.6rem; letter-spacing:0.3em;
+                         color:#4A3F30; text-transform:uppercase;">
+                Nobility v1 &nbsp;·&nbsp; Beryl AI Labs &nbsp;·&nbsp; New Orleans
+                &nbsp;·&nbsp; Not Runway. Not ElevenLabs. Not HeyGen.
+                &nbsp;·&nbsp; tyronne-os/nobility-v1
+            </span>
+        </div>
+        """)
+
+        # ── Event Handlers ────────────────────────────────────────────────────
+
+        def pipeline_wrapper(api_key, user_msg, ref_img, history, state):
+            return run_nobility_pipeline(api_key, user_msg, ref_img, history, state)
+
+        submit_btn.click(
+            fn=pipeline_wrapper,
+            inputs=[api_key, user_input, reference_image, conversation_history, session_state],
+            outputs=[response_output, shot_plan_output, drift_output, telemetry_output,
+                     conversation_history, session_state],
+        )
+
+        user_input.submit(
+            fn=pipeline_wrapper,
+            inputs=[api_key, user_input, reference_image, conversation_history, session_state],
+            outputs=[response_output, shot_plan_output, drift_output, telemetry_output,
+                     conversation_history, session_state],
+        )
+
+        def clear_all():
+            return "", "{}", "{}", (
+                "━━ NOBILITY v1 ━━\n"
+                "Cleared. Ready.\n\n"
+                "[L1] Brain      — ms\n"
+                "[L2] Director   — ms\n"
+                "[L3] Drift Score — ms\n"
+                "──────────────────\n"
+                "Awaiting turn..."
+            ), [], {}
+
+        clear_btn.click(
+            fn=clear_all,
+            outputs=[response_output, shot_plan_output, drift_output, telemetry_output,
+                     conversation_history, session_state],
+        )
+
+    return demo
+
+
+# ── Launch ────────────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    demo = build_ui()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        favicon_path=None,
+    )