gradio UI

app.py

@@ -0,0 +1,348 @@
+"""WitGym Gradio demo for Hugging Face Spaces."""
+import html
+import os
+import threading
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Spaces: default to HF API so we don't load 9B weights on the Space container
+if os.getenv("SPACE_ID"):
+    os.environ.setdefault("LLM_BACKEND", "hf_api")
+
+import gradio as gr
+from loguru import logger
+
+from witgym.conversation import ConversationManager
+from witgym.engine import WitGymEngine, get_shared_resources
+from witgym.debug_render import format_transcript_html, thinking_turn_html
+from witgym import config
+
+INDEX_PATH = os.getenv("WITGYM_INDEX_PATH", config.INDEX_PATH)
+_FAVICON = Path(__file__).parent / "assets" / "favicon.png"
+
+LOGO_SVG = """<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" fill="none" width="56" height="56">
+  <circle cx="32" cy="32" r="30" fill="#f5f0e6" stroke="#c4b8a8" stroke-width="2"/>
+  <path d="M18 28c0-6 4-10 14-10s14 4 14 10" stroke="#5c4a3a" stroke-width="2.5" stroke-linecap="round"/>
+  <ellipse cx="24" cy="30" rx="5" ry="6" fill="#2d6a4f"/>
+  <ellipse cx="40" cy="30" rx="5" ry="6" fill="#2d6a4f"/>
+  <path d="M26 42c2 3 10 3 12 0" stroke="#5c4a3a" stroke-width="2" stroke-linecap="round"/>
+  <path d="M32 14v4M22 18l3 2M42 18l-3 2" stroke="#b08968" stroke-width="1.5" stroke-linecap="round"/>
+</svg>"""
+
+STARTERS = [
+    "I've been ignoring a voicemail for so long that opening it now feels like a legal risk.",
+    "I'm very low-maintenance. I just have high standards.",
+    "I don't micromanage. I just like to stay closely involved in outcomes.",
+    "My intern corrected my presentation in front of the client.",
+    "I sent a complaint about my manager to my manager.",
+    "I've been so invested in this project I forgot to eat today.",
+    "I give excellent feedback. People just don't always know how to receive it.",
+    "I went to bed early last night.",
+]
+
+TRANSCRIPT_HEIGHT = 560
+
+APP_CSS = """
+#witgym-main { max-width: 1200px; margin: 0 auto; width: 100%; }
+
+.witgym-header {
+    display: flex; flex-direction: column; align-items: center;
+    text-align: center; gap: 0.35rem; margin-bottom: 1rem;
+}
+.witgym-logo { width: 56px; height: 56px; flex-shrink: 0; }
+.witgym-title { font-size: 2rem; font-weight: 700; margin: 0; color: #3d3429; }
+.witgym-tagline { color: #6b6560; margin: 0; font-size: 0.95rem; }
+
+.wg-transcript {
+    font-family: ui-monospace, 'SF Mono', Menlo, Consolas, monospace;
+    font-size: 15px; line-height: 1.55; color: #1a1a1a;
+}
+.wg-empty { color: #777; font-style: italic; padding: 1.5rem 0; text-align: center; }
+.wg-turn { margin-bottom: 1.5rem; }
+.wg-turn--thinking { cursor: wait; }
+.wg-user { color: #16a34a; font-weight: 700; margin-bottom: 0.75rem; font-size: 16px; }
+.wg-thinking {
+    display: flex; align-items: center; gap: 0.5rem;
+    color: #6b6258; font-style: italic; font-size: 0.95rem;
+    padding: 0.65rem 0.75rem; margin-top: 0.25rem;
+    background: #f3efe8; border-radius: 6px; border: 1px solid #e0d8cc;
+}
+.wg-thinking-icon { flex-shrink: 0; animation: wg-spin 0.85s linear infinite; }
+@keyframes wg-spin { to { transform: rotate(360deg); } }
+@media (prefers-reduced-motion: reduce) {
+    .wg-thinking-icon { animation: none; }
+}
+.wg-label { font-weight: 700; margin-right: 0.35rem; }
+.wg-coach-divider {
+    display: flex; align-items: center; gap: 0.75rem;
+    margin: 1rem 0 0.85rem; color: #8a7f72; font-size: 0.72rem;
+}
+.wg-coach-divider-line {
+    flex: 1; height: 1px; background: #d8d0c4;
+}
+.wg-coach-divider-label {
+    white-space: nowrap; font-style: italic; color: #6b6258; font-size: 0.78rem;
+}
+.wg-coach-reply {
+    margin-top: 1.1rem; padding: 0.9rem 1rem;
+    background: #f7f3ec; border: 1px solid #d4cbb8; border-left: 3px solid #2d6a4f;
+    border-radius: 8px;
+}
+.wg-coach-reply-header {
+    font-size: 0.7rem; font-weight: 700; letter-spacing: 0.1em;
+    text-transform: uppercase; color: #2d6a4f; margin-bottom: 0.45rem;
+}
+.wg-coach-reply-body {
+    font-family: Georgia, 'Times New Roman', serif;
+    font-size: 1.05rem; line-height: 1.5; color: #2a2520;
+}
+.wg-coach-reply--compact { margin-top: 0.75rem; }
+.wg-rule { border-top: 1px solid #e8e2d8; margin: 0.75rem 0; }
+.wg-dim { color: #888; }
+.wg-dim-italic { color: #888; font-style: italic; }
+.wg-cyan { color: #0891b2; }
+.wg-bold { font-weight: 600; }
+.wg-panel { border-radius: 6px; padding: 0.65rem 0.85rem; margin: 0.5rem 0;
+    border: 1px solid; font-size: 14px; }
+.wg-panel-title { font-weight: 700; margin-bottom: 0.35rem; font-size: 13px; }
+.wg-panel-yellow { background: #fffbeb; border-color: #fbbf24; color: #92400e; }
+.wg-panel-yellow .wg-panel-title { color: #b45309; }
+.wg-panel-blue { background: #eff6ff; border-color: #60a5fa; color: #1e3a5f; }
+.wg-panel-blue .wg-panel-title { color: #2563eb; }
+.wg-panel-green { background: #f0fdf4; border-color: #4ade80; color: #14532d; }
+.wg-panel-green .wg-panel-title { color: #16a34a; }
+.wg-panel-dim { background: #f5f5f4; border-color: #d6d3d1; color: #78716c; opacity: 0.85; }
+.wg-meta { border-collapse: collapse; width: 100%; }
+.wg-meta td { padding: 0.15rem 0.5rem 0.15rem 0; vertical-align: top; }
+
+.wg-starter-btn button {
+    width: 100%; text-align: left; white-space: normal;
+    height: auto !important; min-height: 2.25rem; line-height: 1.35;
+}
+"""
+
+_shared = None
+_warmup_done = threading.Event()
+_warmup_error: str | None = None
+
+
+def _ensure_index():
+    if not Path(INDEX_PATH).exists():
+        raise FileNotFoundError(
+            f"Index not found at {INDEX_PATH}. Run `witgym-index` or bundle data/index.json in the Space."
+        )
+
+
+def _get_shared():
+    global _shared
+    if _shared is None:
+        _ensure_index()
+        _shared = get_shared_resources(index_path=INDEX_PATH)
+    return _shared
+
+
+def _loading_html(message: str) -> str:
+    return f'<div class="wg-transcript"><div class="wg-empty">{html.escape(message)}</div></div>'
+
+
+def _bg_warmup():
+    """Preload embedder/index/model in background — keeps submit queue free."""
+    global _warmup_error
+    try:
+        if config.LLM_BACKEND == "hf_api":
+            logger.info("Background warmup: HF API mode — embedder + index only")
+        else:
+            logger.info("Background warmup: loading local model + embedder + index")
+        _get_shared()
+    except Exception as e:
+        logger.exception("Background warmup failed")
+        _warmup_error = str(e)
+    finally:
+        _warmup_done.set()
+
+
+def _on_page_load():
+    """Fast page-load handler — never blocks on model load."""
+    if _warmup_error:
+        return _loading_html(f"Startup error: {_warmup_error}")
+    return format_transcript_html([])
+
+
+threading.Thread(target=_bg_warmup, daemon=True).start()
+
+
+def _new_session():
+    return {"conversation": ConversationManager(), "traces": []}
+
+
+def _header_html() -> str:
+    return f"""
+    <div class="witgym-header">
+      <div class="witgym-logo">{LOGO_SVG}</div>
+      <p class="witgym-title">WitGym</p>
+      <p class="witgym-tagline">Coach your comedy instincts — one situation at a time.</p>
+    </div>
+    """
+
+
+def _clear_input():
+    return gr.update(value="", interactive=True)
+
+
+def _disable_input():
+    return gr.update(value="", interactive=False)
+
+
+def fill_starter(text: str) -> str:
+    return text
+
+
+def practice(user_input: str, session, progress=gr.Progress()):
+    if not isinstance(session, dict):
+        session = _new_session()
+
+    user_input = (user_input or "").strip()
+    if not user_input:
+        yield format_transcript_html(session["traces"]), _clear_input(), session
+        return
+
+    logger.info(f"Practice: {user_input[:80]!r}")
+    yield (
+        format_transcript_html(session["traces"], append_html=thinking_turn_html(user_input)),
+        _disable_input(),
+        session,
+    )
+
+    progress(0.05, desc="Warming up coach…")
+    engine = WitGymEngine(resources=_get_shared(), conversation=session["conversation"])
+    progress(0.25, desc="Reading the room…")
+    try:
+        result = engine.respond(user_input)
+        progress(0.95, desc="Polishing the line…")
+    except Exception as e:
+        logger.exception("Engine error")
+        err_turn = (
+            f'<div class="wg-turn"><div class="wg-user"><span class="wg-label">You</span> '
+            f'{html.escape(user_input)}</div>'
+            f'<div style="color:#dc2626">Error: {html.escape(str(e))}</div></div>'
+        )
+        yield format_transcript_html(session["traces"], append_html=err_turn), _clear_input(), session
+        return
+
+    session["traces"].append((user_input, result))
+    session["traces"] = session["traces"][-5:]
+
+    yield format_transcript_html(session["traces"]), _clear_input(), session
+
+
+def clear_session():
+    return (
+        format_transcript_html([]),
+        _clear_input(),
+        _new_session(),
+    )
+
+
+def _theme():
+    return (
+        gr.themes.Soft(
+            primary_hue=gr.themes.colors.stone,
+            secondary_hue=gr.themes.colors.amber,
+            neutral_hue=gr.themes.colors.stone,
+        )
+        .set(
+            body_background_fill="#fffff8",
+            block_background_fill="#faf9f6",
+            button_primary_background_fill="#3d3429",
+            button_primary_background_fill_hover="#4a4034",
+            button_primary_text_color="#faf9f6",
+        )
+    )
+
+
+def build_ui():
+    with gr.Blocks(title="WitGym") as demo:
+        gr.HTML(_header_html())
+
+        session_state = gr.State(_new_session())
+
+        with gr.Column(elem_id="witgym-main"):
+            with gr.Row():
+                with gr.Column(scale=3):
+                    transcript = gr.HTML(
+                        value=format_transcript_html([]),
+                        max_height=TRANSCRIPT_HEIGHT,
+                        autoscroll=True,
+                        show_label=False,
+                        padding=True,
+                    )
+                    user_input = gr.Textbox(
+                        show_label=False,
+                        placeholder="Describe a situation… (Enter to send)",
+                        lines=1,
+                        max_lines=1,
+                    )
+                    with gr.Row():
+                        submit_btn = gr.Button(
+                            "Start Practicing Humour",
+                            variant="primary",
+                            scale=4,
+                        )
+                        clear_btn = gr.Button(
+                            "Clear conversation",
+                            size="sm",
+                            variant="secondary",
+                            scale=1,
+                        )
+                with gr.Column(scale=1):
+                    gr.Markdown("**Try a starter**")
+                    for text in STARTERS:
+                        starter_btn = gr.Button(
+                            text,
+                            size="sm",
+                            variant="secondary",
+                            elem_classes=["wg-starter-btn"],
+                        )
+                        starter_btn.click(
+                            fn=fill_starter,
+                            inputs=[gr.State(text)],
+                            outputs=user_input,
+                            queue=False,
+                        )
+
+        submit_btn.click(
+            fn=practice,
+            inputs=[user_input, session_state],
+            outputs=[transcript, user_input, session_state],
+            show_progress="full",
+            show_progress_on=submit_btn,
+        )
+        user_input.submit(
+            fn=practice,
+            inputs=[user_input, session_state],
+            outputs=[transcript, user_input, session_state],
+            show_progress="full",
+            show_progress_on=submit_btn,
+        )
+        clear_btn.click(
+            fn=clear_session,
+            outputs=[transcript, user_input, session_state],
+        )
+
+        demo.load(fn=_on_page_load, outputs=transcript, show_progress="hidden")
+
+    return demo
+
+
+demo = build_ui()
+demo.queue(default_concurrency_limit=1)
+demo.favicon_path = _FAVICON
+
+if __name__ == "__main__":
+    launch_kwargs = dict(favicon_path=_FAVICON, theme=_theme(), css=APP_CSS)
+    if os.getenv("SPACE_ID"):
+        launch_kwargs["server_name"] = "0.0.0.0"
+    demo.launch(**launch_kwargs)

pyproject.toml

@@ -16,6 +16,7 @@ dependencies = [
     "huggingface-hub>=0.22.0",
     "datasets>=2.19.0",
     "sentencepiece>=0.2.0",
+    "gradio>=5.0.0",
 ]
 
 [project.scripts]

requirements.txt

@@ -0,0 +1,13 @@
+torch>=2.2.0
+transformers>=4.40.0
+accelerate>=0.29.0
+pydantic>=2.0.0
+loguru>=0.7.0
+rich>=13.0.0
+sentence-transformers>=3.0.0
+numpy>=1.26.0
+python-dotenv>=1.0.0
+huggingface-hub>=0.22.0
+datasets>=2.19.0
+sentencepiece>=0.2.0
+gradio>=5.0.0

uv.lock

@@ -5,9 +5,12 @@ resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
     "python_full_version >= '3.14' and sys_platform == 'emscripten'",
     "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version < '3.14' and sys_platform == 'win32'",
-    "python_full_version < '3.14' and sys_platform == 'emscripten'",
-    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
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 source = { registry = "https://pypi.org/simple" }
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+version = "0.49.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c4/1f/fa18009dea8469069cca78a4e877a008ab78f08b064bfc9ab891579077ff/uvicorn-0.49.0.tar.gz", hash = "sha256:ebf4271aa580d9de97f93192d4595176df6e91f9aae919ca73e4fc07df1e66a3", size = 91284, upload-time = "2026-06-03T22:01:30.448Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/fa/e1388bbcf24ef3274f45c0c1c7b501fd14971037c1b6ee23610553307497/uvicorn-0.49.0-py3-none-any.whl", hash = "sha256:ba3d14c3ee7e41c6c654c46c9eb489d33213cdd30aa1696eab1374337c13f68f", size = 71376, upload-time = "2026-06-03T22:01:29.037Z" },
+]
+
 [[package]]
 name = "win32-setctime"
 version = "1.2.0"
@@ -2208,6 +2646,7 @@ source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
     { name = "datasets" },
+    { name = "gradio" },
     { name = "huggingface-hub" },
     { name = "loguru" },
     { name = "numpy" },
@@ -2224,6 +2663,7 @@ dependencies = [
 requires-dist = [
     { name = "accelerate", specifier = ">=0.29.0" },
     { name = "datasets", specifier = ">=2.19.0" },
+    { name = "gradio", specifier = ">=5.0.0" },
     { name = "huggingface-hub", specifier = ">=0.22.0" },
     { name = "loguru", specifier = ">=0.7.0" },
     { name = "numpy", specifier = ">=1.26.0" },

witgym/config.py

@@ -2,8 +2,10 @@
 import os
 import torch
 
-# Model
-MODEL_ID = "Qwen/Qwen3.5-9B"
+# LLM — set LLM_MODEL_ID in env / HF Space secrets to swap models (local + API)
+LLM_MODEL_ID = os.getenv("LLM_MODEL_ID", "Qwen/Qwen3.5-9B")
+MODEL_ID = LLM_MODEL_ID  # backward compat for imports
+
 EMBED_MODEL_ID = "BAAI/bge-small-en-v1.5"
 
 # Device — MPS for Apple Silicon, fallback to cuda/cpu
@@ -53,5 +55,12 @@ OVERLAP_NGRAM_SIZE = 6  # contiguous words shared with retrieved dialogue
 CLICHE_LOGIT_PENALTY = -5.0
 CLICHE_PENALTY_TOKENS = 6   # Penalise first N tokens of the obvious response
 
-# HuggingFace auth
+# HuggingFace auth + inference backend
 HF_TOKEN = os.getenv("HF_TOKEN", "")
+# "local" = Transformers on device; "hf_api" = Inference Providers (Spaces default)
+LLM_BACKEND = os.getenv("LLM_BACKEND", "local")
+# Qwen3.5-9B thinking-mode toggle requires Together; "auto" often 400s on extra_body
+HF_INFERENCE_PROVIDER = os.getenv(
+    "HF_INFERENCE_PROVIDER",
+    "together" if "Qwen3.5" in LLM_MODEL_ID else "auto",
+)

witgym/conversation.py

@@ -49,10 +49,13 @@ class ConversationManager:
             lines.append(f"Turn -{len(recent) - i + 1}: archetype={arch}, tension={tension}")
         return "\n".join(lines)
 
-    def needs_compression(self, tokenizer) -> bool:
+    def needs_compression(self, tokenizer=None) -> bool:
         """Check if token count of full history exceeds 80% of context window."""
         full_text = self.get_context_string()
-        token_count = len(tokenizer.encode(full_text))
+        if tokenizer is not None:
+            token_count = len(tokenizer.encode(full_text))
+        else:
+            token_count = len(full_text) // 4  # chars-per-token estimate (hf_api path)
         threshold = int(config.CONTEXT_WINDOW * config.COMPRESSION_THRESHOLD)
         if token_count > threshold:
             logger.info(f"Context compression triggered: {token_count} tokens > {threshold}")

witgym/debug_render.py

@@ -0,0 +1,145 @@
+"""Format WitGymResponse traces for Gradio — CLI-style HTML transcript."""
+import html
+from typing import List, Tuple, Any
+from witgym.schemas import WitGymResponse
+
+
+def _esc(text: str) -> str:
+    return html.escape(text or "")
+
+
+_THINKING_ICON = (
+    '<svg class="wg-thinking-icon" viewBox="0 0 24 24" width="18" height="18" '
+    'aria-hidden="true" fill="none" xmlns="http://www.w3.org/2000/svg">'
+    '<circle cx="12" cy="12" r="9" stroke="#2d6a4f" stroke-width="2" opacity="0.2"/>'
+    '<path d="M12 3a9 9 0 0 1 9 9" stroke="#2d6a4f" stroke-width="2.5" '
+    'stroke-linecap="round"/></svg>'
+)
+
+
+def thinking_turn_html(user_input: str) -> str:
+    """Immediate feedback while the engine runs — shown before final coaching notes."""
+    return (
+        '<div class="wg-turn wg-turn--thinking">'
+        f'<div class="wg-user"><span class="wg-label">You</span> {_esc(user_input)}</div>'
+        '<div class="wg-thinking">'
+        f"{_THINKING_ICON}"
+        "<span>Your humor coach is thinking…</span>"
+        "</div>"
+        "</div>"
+    )
+
+
+def format_trace_html(result: WitGymResponse, user_input: str) -> str:
+    """Render one turn as styled HTML matching CLI debug panels."""
+    parts = [
+        '<div class="wg-turn">',
+        f'<div class="wg-user"><span class="wg-label">You</span> {_esc(user_input)}</div>',
+    ]
+
+    if result.route == "smalltalk":
+        parts.append(
+            '<div class="wg-coach-reply wg-coach-reply--compact">'
+            '<div class="wg-coach-reply-header">Your humor coach</div>'
+            f'<div class="wg-coach-reply-body">{_esc(result.selected)}</div>'
+            "</div>"
+        )
+        parts.append("</div>")
+        return "\n".join(parts)
+
+    meta = result.metadata
+    parts.append(
+        '<div class="wg-coach-divider">'
+        '<span class="wg-coach-divider-line"></span>'
+        '<span class="wg-coach-divider-label">Comedy Hall of Fame — coaching notes</span>'
+        '<span class="wg-coach-divider-line"></span>'
+        "</div>"
+        '<div class="wg-panel wg-panel-yellow">'
+        '<div class="wg-panel-title">Pass 1 — Extracted Metadata</div>'
+        '<table class="wg-meta">'
+        f'<tr><td class="wg-dim">Archetype</td><td class="wg-cyan">{_esc(meta.archetype.value)}</td></tr>'
+        f'<tr><td class="wg-dim">Tension</td><td class="wg-cyan">{_esc(meta.tension_type.value)}</td></tr>'
+        f'<tr><td class="wg-dim">Distance</td><td class="wg-cyan">{_esc(meta.violation_distance.value)}</td></tr>'
+        f'<tr><td class="wg-dim">Twist potential</td><td class="wg-cyan">{meta.twist_potential}/10</td></tr>'
+        f'<tr><td class="wg-dim">Surface</td><td class="wg-cyan">{_esc(meta.surface)}</td></tr>'
+        f'<tr><td class="wg-dim">Subtext</td><td class="wg-cyan">{_esc(meta.subtext)}</td></tr>'
+        f'<tr><td class="wg-dim">Power dynamic</td><td class="wg-cyan">{_esc(meta.power_dynamic)}</td></tr>'
+        f'<tr><td class="wg-dim">Connector</td><td class="wg-cyan">{_esc(meta.connector or "none")}</td></tr>'
+        f'<tr><td class="wg-dim">Boring response (suppressed)</td>'
+        f'<td class="wg-dim-italic">{_esc(meta.obvious_response)}</td></tr>'
+        "</table></div>"
+    )
+
+    for i, scene in enumerate(result.retrieved_scenes, 1):
+        parts.append(
+            f'<div class="wg-panel wg-panel-blue">'
+            f'<div class="wg-panel-title">Retrieved Scene {i}</div>'
+            f'<div><span class="wg-bold">{_esc(scene.show)}</span> — {_esc(scene.character)}</div>'
+            f'<div><span class="wg-dim">Setup:</span> {_esc(scene.setup)}</div>'
+            f'<div><span class="wg-dim">Response:</span> {_esc(scene.response)}</div>'
+            f'<div><span class="wg-dim">Why it works:</span> {_esc(scene.why_it_works)}</div>'
+            f'<div><span class="wg-dim">Archetype:</span> {_esc(scene.archetype.value)}</div>'
+            f"</div>"
+        )
+
+    for c in result.candidates:
+        selected = c.text == result.selected
+        cls = "wg-panel-green" if selected else "wg-panel-dim"
+        title = "✓ SELECTED" if selected else c.persona.upper()
+        parts.append(
+            f'<div class="wg-panel {cls}">'
+            f'<div class="wg-panel-title">{_esc(title)}</div>'
+            f"<div>{_esc(c.text)}</div></div>"
+        )
+
+    parts.append(
+        '<div class="wg-coach-reply">'
+        '<div class="wg-coach-reply-header">Your humor coach</div>'
+        f'<div class="wg-coach-reply-body">{_esc(result.selected)}</div>'
+        "</div>"
+    )
+    parts.append('<div class="wg-rule"></div></div>')
+    return "\n".join(parts)
+
+
+def format_transcript_html(
+    traces: List[Tuple[str, Any]],
+    max_turns: int = 5,
+    append_html: str = "",
+) -> str:
+    """Full scrollable transcript — oldest first, newest at bottom."""
+    if not traces and not append_html:
+        inner = (
+            '<div class="wg-empty">Type your situation in the box below, then press '
+            '<strong>Start Practicing Humour</strong> (or Enter).</div>'
+        )
+        body = inner
+    else:
+        blocks = []
+        if traces:
+            recent = traces[-max_turns:]
+            blocks = [
+                format_trace_html(
+                    r if isinstance(r, WitGymResponse) else WitGymResponse.model_validate(r),
+                    user_input,
+                )
+                for user_input, r in recent
+            ]
+        body = f'{"".join(blocks)}{append_html}'
+
+    return f'<div class="wg-transcript">{body}</div>'
+
+
+# Legacy markdown helpers (CLI-adjacent tooling)
+def format_trace(result: WitGymResponse, user_input: str) -> str:
+    """Plain-markdown fallback."""
+    lines = [f"**You:** {user_input}", f"**WitGym:** {result.selected}", ""]
+    if result.route == "smalltalk":
+        return "\n".join(lines)
+    meta = result.metadata
+    lines.append(f"_archetype={meta.archetype.value}, tension={meta.tension_type.value}_")
+    return "\n".join(lines)
+
+
+def format_logs(traces: List[Tuple[str, Any]], max_turns: int = 5) -> str:
+    return format_transcript_html(traces, max_turns)

witgym/engine.py

@@ -1,17 +1,21 @@
 """WitGym engine — pure function core. No I/O, no UI.
 
-This is the single file that both CLI (main.py) and future Gradio (app.py) call.
+This is the single file that both CLI (main.py) and Gradio (app.py) call.
 """
 import re
+from typing import Optional
 from loguru import logger
 from sentence_transformers import SentenceTransformer
 from witgym import config
 from witgym.model import load_model
-from witgym.schemas import WitGymResponse
+from witgym.schemas import WitGymResponse, fallback_metadata
 from witgym.extractor import extract_comedy_metadata
 from witgym.retriever import load_index, retrieve_scenes
 from witgym.generator import generate_candidates, rank_candidates, compress_winner
 from witgym.conversation import ConversationManager
+from witgym.router import classify_intent, SMALLTALK_REPLY
+
+_shared_resources = None
 
 
 def _cap_two_sentences(text: str) -> str:
@@ -23,20 +27,73 @@ def _cap_two_sentences(text: str) -> str:
     return " ".join(sentences[:2]).strip()
 
 
-class WitGymEngine:
-    """Stateful engine. Load once, call respond() in a loop."""
+class SharedResources:
+    """Heavy resources loaded once per process (model/tokenizer, embedder, index)."""
 
     def __init__(self, index_path: str = config.INDEX_PATH):
-        logger.info("Initialising WitGymEngine...")
+        logger.info("Loading shared WitGym resources...")
         self.model, self.tokenizer = load_model()
         logger.info(f"Loading embedding model: {config.EMBED_MODEL_ID}")
         self.embed_model = SentenceTransformer(config.EMBED_MODEL_ID, device=config.DEVICE)
         self.index = load_index(index_path)
-        self.conversation = ConversationManager()
+        logger.success("Shared resources ready.")
+
+
+def get_shared_resources(index_path: str = config.INDEX_PATH) -> SharedResources:
+    """Process-wide singleton for Spaces / Gradio multi-session use."""
+    global _shared_resources
+    if _shared_resources is None:
+        _shared_resources = SharedResources(index_path=index_path)
+    return _shared_resources
+
+
+class WitGymEngine:
+    """Stateful engine. Load once, call respond() in a loop."""
+
+    def __init__(
+        self,
+        index_path: str = config.INDEX_PATH,
+        resources: Optional[SharedResources] = None,
+        conversation: Optional[ConversationManager] = None,
+    ):
+        if resources is None:
+            logger.info("Initialising WitGymEngine...")
+            self._resources = SharedResources(index_path=index_path)
+        else:
+            self._resources = resources
+        self.conversation = conversation or ConversationManager()
         logger.success("WitGymEngine ready.")
 
+    @property
+    def model(self):
+        return self._resources.model
+
+    @property
+    def tokenizer(self):
+        return self._resources.tokenizer
+
+    @property
+    def embed_model(self):
+        return self._resources.embed_model
+
+    @property
+    def index(self):
+        return self._resources.index
+
     def respond(self, user_input: str) -> WitGymResponse:
         """Full two-pass pipeline. Returns WitGymResponse with all intermediate data."""
+        if classify_intent(user_input) == "smalltalk":
+            logger.info("Small-talk route — skipping humour pipeline")
+            metadata = fallback_metadata(user_input)
+            self.conversation.add_turn(user_input, SMALLTALK_REPLY, metadata)
+            return WitGymResponse(
+                metadata=metadata,
+                retrieved_scenes=[],
+                candidates=[],
+                selected=SMALLTALK_REPLY,
+                route="smalltalk",
+            )
+
         # Check compression before adding new turn
         if self.conversation.needs_compression(self.tokenizer):
             self.conversation.compress(self.model, self.tokenizer)
@@ -56,6 +113,7 @@ class WitGymEngine:
                 retrieved_scenes=[],
                 candidates=[],
                 selected=selected,
+                route="humour",
             )
 
         # RAG — Retrieve analogous situations (not similar text)
@@ -102,4 +160,5 @@ class WitGymEngine:
             retrieved_scenes=scenes,
             candidates=candidates,
             selected=selected,
+            route="humour",
         )

witgym/extractor.py

@@ -35,6 +35,7 @@ Archetype selection guidance (avoid overusing self_delusion):
 - self_delusion: specifically a self-image story ("I'm fine / I'm great / I'm the best") contradicted by behavior/evidence in the same moment
 If unsure between self_delusion vs social_fail/anxiety_escalation, prefer the more specific one (social_fail/anxiety_escalation) unless there is an explicit self-image contradiction.
 For twist_potential: score high if the input has self-delusion, status gap, or absurd logic. Score low if it is a neutral factual statement with no tension.
+If the input is ONLY a greeting, social opener, or typo-greeting with no described situation, set twist_potential to 1 and subtext to "greeting only — no situational content". Do not invent comedy tension.
 For connector: look for a single word or short phrase that carries an expected meaning in context AND a second meaning that reframes the situation. Most inputs will have null. Return null unless a genuine dual-reading exists (e.g. "manage" can mean control people or barely cope; "balance" can mean financial or emotional equilibrium).
 Return ONLY the JSON. Nothing else."""
 

witgym/generator.py

@@ -172,14 +172,17 @@ def generate_candidates(
         if personas_to_run is None or name in personas_to_run
     }
 
-    cliche_processor = ClichePenaltyProcessor(metadata.obvious_response, tokenizer)
-    processors = LogitsProcessorList([cliche_processor])
-    if config.ENABLE_BAD_WORD_GUARD:
-        bad_words_ids = [
-            tokenizer.encode(phrase, add_special_tokens=False)
-            for phrase in config.BAD_WORD_PHRASES
-        ]
-        processors.append(NoBadWordsLogitsProcessor(bad_words_ids, eos_token_id=tokenizer.eos_token_id))
+    processors = None
+    cliche_processor = None
+    if config.LLM_BACKEND == "local" and tokenizer is not None:
+        cliche_processor = ClichePenaltyProcessor(metadata.obvious_response, tokenizer)
+        processors = LogitsProcessorList([cliche_processor])
+        if config.ENABLE_BAD_WORD_GUARD:
+            bad_words_ids = [
+                tokenizer.encode(phrase, add_special_tokens=False)
+                for phrase in config.BAD_WORD_PHRASES
+            ]
+            processors.append(NoBadWordsLogitsProcessor(bad_words_ids, eos_token_id=tokenizer.eos_token_id))
 
     candidates = []
     ngram_n = config.OVERLAP_NGRAM_SIZE
@@ -205,6 +208,9 @@ def generate_candidates(
 
         raw = generate_text(prompt, model, tokenizer, config_type="generate", logits_processors=processors)
         last_raw, last_persona = raw, persona_name
+        if any(phrase in raw for phrase in config.BAD_WORD_PHRASES):
+            logger.warning(f"[{persona_name}] leaked routing label — dropping candidate")
+            continue
         if config.ENABLE_OVERLAP_GUARD and _copies_retrieved_phrase(raw, scenes, ngram_n):
             logger.warning(f"[{persona_name}] copies precedent phrasing — dropping candidate")
             continue
@@ -228,12 +234,11 @@ def generate_candidates(
             w.lower() for w in _re.findall(r"\b\w+\b", raw)
             if w.lower() not in _STOP_WORDS and len(w) > 3
         ]
-        if words:
+        if words and cliche_processor is not None and tokenizer is not None:
             top_words = [w for w, _ in Counter(words).most_common(8)]
-            # Add these word token IDs to the penalty set for next candidates
             for word in top_words:
                 extra_ids = tokenizer.encode(word, add_special_tokens=False)
-                cliche_processor.penalty_ids.update(extra_ids[:2])  # first 2 tokens of each word
+                cliche_processor.penalty_ids.update(extra_ids[:2])
 
     if not candidates and last_raw:
         logger.warning(f"All candidates dropped — keeping last line from {last_persona}")

witgym/model.py

@@ -14,22 +14,46 @@ from witgym import config
 
 _model = None
 _tokenizer = None
+_inference_client = None
+
+
+def _get_inference_client():
+    global _inference_client
+    if _inference_client is None:
+        from huggingface_hub import InferenceClient
+
+        _inference_client = InferenceClient(
+            model=config.LLM_MODEL_ID,
+            token=config.HF_TOKEN or None,
+            provider=config.HF_INFERENCE_PROVIDER,
+        )
+    return _inference_client
 
 
 def load_model():
-    """Load Qwen3.5-4B onto MPS with bfloat16. Cached after first call."""
+    """Load local weights + tokenizer, or (None, None) when LLM_BACKEND=hf_api."""
     global _model, _tokenizer
-    if _model is not None:
+
+    if config.LLM_BACKEND == "hf_api":
+        logger.info(
+            f"HF API backend ({config.HF_INFERENCE_PROVIDER}) — "
+            f"remote inference for {config.LLM_MODEL_ID}, no local weights or tokenizer"
+        )
+        return None, None
+
+    if _model is not None and _tokenizer is not None:
         return _model, _tokenizer
 
-    logger.info(f"Loading model {config.MODEL_ID} on {config.DEVICE} ({config.DTYPE})")
+    logger.info(f"Loading tokenizer for {config.LLM_MODEL_ID}")
     _tokenizer = AutoTokenizer.from_pretrained(
-        config.MODEL_ID,
+        config.LLM_MODEL_ID,
         token=config.HF_TOKEN,
         trust_remote_code=True,
     )
+
+    logger.info(f"Loading model {config.LLM_MODEL_ID} on {config.DEVICE} ({config.DTYPE})")
     _model = AutoModelForCausalLM.from_pretrained(
-        config.MODEL_ID,
+        config.LLM_MODEL_ID,
         dtype=config.DTYPE,
         device_map=None,          # Never device_map="auto" on MPS
         token=config.HF_TOKEN,
@@ -91,6 +115,61 @@ def _strip_thinking(text: str) -> str:
     return text.strip()
 
 
+def _extract_hf_message_text(message) -> str:
+    """Read assistant text from HF chat response.
+
+    Qwen3.5 on Together defaults to thinking mode: output lands in `reasoning`
+  with `content` null unless enable_thinking=False is set via chat_template_kwargs.
+    """
+    content = (message.content or "").strip()
+    if content:
+        return _strip_thinking(content)
+    reasoning = (getattr(message, "reasoning", None) or "").strip()
+    if reasoning:
+        stripped = _strip_thinking(reasoning)
+        if stripped:
+            return stripped
+    return ""
+
+
+def _hf_api_extra_body() -> dict:
+    # Together / Qwen3.5: disable thinking so JSON and wit lines land in `content`
+    # https://www.together.ai/models/qwen3-5-9b
+    return {"chat_template_kwargs": {"enable_thinking": False}}
+
+
+def _generate_via_hf_api(prompt: str, config_type: str) -> str:
+    """Route generation through Hugging Face Inference Providers."""
+    client = _get_inference_client()
+    messages = [{"role": "user", "content": prompt}]
+
+    if config_type == "extract":
+        kwargs = {"max_tokens": config.EXTRACT_MAX_NEW_TOKENS, "temperature": 0.2}
+    elif config_type == "generate":
+        kwargs = {
+            "max_tokens": config.GENERATE_MAX_NEW_TOKENS,
+            "temperature": config.GENERATE_TEMP,
+            "top_p": 0.95,
+        }
+    elif config_type == "rank":
+        kwargs = {"max_tokens": 10, "temperature": 0.1}
+    else:
+        raise ValueError(f"Unknown config_type: {config_type}")
+
+    extra = _hf_api_extra_body()
+    try:
+        output = client.chat_completion(messages, **kwargs, extra_body=extra)
+    except Exception as e:
+        # Some providers reject chat_template_kwargs — retry without it
+        logger.warning(f"HF API extra_body rejected ({e}); retrying without thinking toggle")
+        output = client.chat_completion(messages, **kwargs)
+
+    raw = _extract_hf_message_text(output.choices[0].message)
+    if not raw:
+        logger.warning(f"HF API returned empty text (config_type={config_type})")
+    return raw
+
+
 class ClichePenaltyProcessor(LogitsProcessor):
     """Soft penalty on the opening tokens of the obvious/boring response.
 
@@ -118,6 +197,9 @@ def generate_text(
     logits_processors: LogitsProcessorList = None,
 ) -> str:
     """Unified generation. config_type: 'extract' | 'generate' | 'rank'."""
+    if config.LLM_BACKEND == "hf_api":
+        return _generate_via_hf_api(prompt, config_type)
+
     messages = [{"role": "user", "content": prompt}]
     text = _apply_chat_template_no_think(tokenizer, messages)
     inputs = tokenizer(text, return_tensors="pt").to(config.DEVICE)

witgym/router.py

@@ -0,0 +1,86 @@
+"""Heuristic router: small talk vs humour practice (no extra LLM call)."""
+import re
+from typing import Literal
+
+Route = Literal["smalltalk", "humour"]
+
+_SMALLTALK_EXACT = {
+    "hi", "hello", "hey", "helo", "hiya", "yo", "sup", "howdy",
+    "thanks", "thank you", "thx", "ok", "okay", "bye", "goodbye",
+}
+
+_CORE_GREETINGS = ("hi", "hey", "hello", "howdy", "sup", "yo")
+
+
+def _levenshtein(a: str, b: str) -> int:
+    if a == b:
+        return 0
+    if not a:
+        return len(b)
+    if not b:
+        return len(a)
+    prev = list(range(len(b) + 1))
+    for i, ca in enumerate(a, 1):
+        curr = [i]
+        for j, cb in enumerate(b, 1):
+            curr.append(min(prev[j] + 1, curr[j - 1] + 1, prev[j - 1] + (ca != cb)))
+        prev = curr
+    return prev[-1]
+
+
+def _is_greeting_token(token: str) -> bool:
+    if len(token) > 6:
+        return False
+    return any(_levenshtein(token, g) <= 1 for g in _CORE_GREETINGS)
+
+_SMALLTALK_PREFIXES = (
+    "hi ", "hello ", "hey ", "what's up", "whats up", "how are you",
+    "who are you", "what are you", "what is witgym", "what is this",
+    "how does this work", "help", "how do i use",
+)
+
+_HUMOUR_SIGNALS = (
+    "roast", "comeback", "witty", "funny", "humor", "humour",
+    "punchline", "joke", "make it funny", "one-liner", "one liner",
+    "practice humour", "practice humor", "sharp line", "wit ",
+)
+
+
+def classify_intent(text: str) -> Route:
+    """Return 'smalltalk' for greetings/meta; 'humour' for scenarios or explicit wit asks."""
+    normalized = text.strip().lower()
+    normalized = re.sub(r"[^\w\s'?]", "", normalized)
+    normalized = re.sub(r"\s+", " ", normalized).strip()
+
+    if not normalized:
+        return "smalltalk"
+
+    if any(sig in normalized for sig in _HUMOUR_SIGNALS):
+        return "humour"
+
+    if len(normalized) > 80:
+        return "humour"
+
+    if normalized in _SMALLTALK_EXACT:
+        return "smalltalk"
+
+    if any(normalized.startswith(p) for p in _SMALLTALK_PREFIXES):
+        return "smalltalk"
+
+    if len(normalized.split()) <= 4 and normalized.endswith("?"):
+        if any(k in normalized for k in ("who", "what", "how", "help")):
+            return "smalltalk"
+
+    tokens = normalized.split()
+    if len(tokens) == 1 and _is_greeting_token(tokens[0]):
+        return "smalltalk"
+
+    return "humour"
+
+
+SMALLTALK_REPLY = (
+    "I'm WitGym — I help you practice sharper one-liners. "
+    "Paste a real situation (work awkwardness, social fail, misplaced confidence) "
+    "and I'll generate a punchy line while showing how I reasoned through it in the logs. "
+    "No need to ask for jokes explicitly — just describe the moment."
+)

witgym/schemas.py

@@ -63,6 +63,7 @@ class WitGymResponse(BaseModel):
     retrieved_scenes: List[TranscriptScene]
     candidates: List[CandidateResponse]
     selected: str
+    route: str = "humour"  # "smalltalk" | "humour"
 
 
 # Fallback metadata when extraction fails