app.py

import os
import re
import io
import json
import base64
import time
import uuid
import datetime
import contextlib
import urllib.request
import html as _html
from pathlib import Path

# --- Preload CUDA runtime libs before importing llama_cpp ---
# The cu124 llama-cpp-python wheel's libllama.so needs libcudart.so.12 /
# libcublas at import time. On ZeroGPU those aren't on the default loader
# path, so we dlopen the pip-provided nvidia libs (cudart first) globally.
import ctypes
import glob
import site


def _preload_cuda():
    bases = set(site.getsitepackages())
    try:
        bases.add(site.getusersitepackages())
    except Exception:
        pass
    libs = []
    for base in bases:
        libs += glob.glob(os.path.join(base, "nvidia", "*", "lib", "*.so*"))
    priority = {"cuda_runtime": 0, "cublas": 1}

    def _key(p):
        for name, rank in priority.items():
            if name in p:
                return rank
        return 2

    for so in sorted(set(libs), key=_key):
        try:
            ctypes.CDLL(so, mode=ctypes.RTLD_GLOBAL)
        except OSError:
            pass


_preload_cuda()

import gradio as gr
import spaces

# Gradio 5+ sanitizes gr.HTML by default, stripping <iframe> elements.
# Detect and pass sanitize_html=False so the srcdoc preview renders.
_GR_MAJOR = int(gr.__version__.split(".")[0])
_HTML_RAW = {"sanitize_html": False} if _GR_MAJOR >= 5 else {}
from huggingface_hub import hf_hub_download
from llama_cpp import Llama

# ---- model (GGUF pulled from the Hub at startup, runs on ZeroGPU) ----
GGUF_REPO = os.environ.get("GGUF_REPO", "").strip()
GGUF_FILE = os.environ.get("GGUF_FILE", "").strip()
if not GGUF_REPO or not GGUF_FILE:
    raise RuntimeError("Set GGUF_REPO and GGUF_FILE as Space secrets (private GGUF repo + file path).")
N_CTX = int(os.environ.get("N_CTX", "16384"))  # keep cold-start init light

print("Downloading GGUF from the Hub ...", flush=True)
MODEL_PATH = hf_hub_download(GGUF_REPO, GGUF_FILE)
print("GGUF ready at", MODEL_PATH, flush=True)

_LLM = None

# q8_0 KV cache (GGML type 8) + flash attention: ~half the KV memory and
# faster decode. Quantized KV requires flash_attn=True in llama.cpp.
try:
    import llama_cpp as _lcpp
    _Q8 = int(getattr(_lcpp, "GGML_TYPE_Q8_0", 8))
except Exception:
    _Q8 = 8
KV_Q8 = os.environ.get("KV_Q8", "0") != "0"  # off by default: keep cold start fast/simple


def _get_llm():
    global _LLM
    if _LLM is None:
        kw = dict(
            model_path=MODEL_PATH,
            n_gpu_layers=-1,
            n_ctx=N_CTX,
            verbose=False,
        )
        if KV_Q8:  # optional: q8 KV + flash attn (lighter KV, but heavier cold init)
            kw["flash_attn"] = True
            kw["type_k"] = _Q8
            kw["type_v"] = _Q8
        try:
            _LLM = Llama(**kw)
        except Exception as e:
            print(f"LLM init failed ({e}); retrying plain", flush=True)
            _LLM = Llama(model_path=MODEL_PATH, n_gpu_layers=-1, n_ctx=N_CTX, verbose=False)
    return _LLM


_THINK = re.compile(r"<think>(.*?)</think>", re.DOTALL)
_CODE_BLOCK = re.compile(r"```([\w+-]*)\s*\n(.*?)```", re.DOTALL)
_TOOL_BLOCK = re.compile(r"```tool\s*\n(.*?)```", re.DOTALL | re.IGNORECASE)
_PY_BLOCK = re.compile(r"```(?:python|py)\s*\n(.*?)```", re.DOTALL | re.IGNORECASE)
# ```file:path/to/x.html\n...content...``` — explicit multi-file deliverable.
_FILE_BLOCK = re.compile(r"```file:([^\n`]+)\n(.*?)```", re.DOTALL)
# An *artifact* block is anything the model emits as a deliverable file: an
# explicit `file:` block, or its natural html/css/js/svg code block. These are
# auto-captured into the virtual filesystem (a write_file tool call) instead of
# being pasted in the chat.
_ARTIFACT_BLOCK = re.compile(
    r"```(?:file:[^\n`]+|html|htm|css|js|javascript|xml|svg)[^\n]*\n.*?```",
    re.DOTALL | re.IGNORECASE,
)
_ARTIFACT_OPEN = re.compile(
    r"```(?:file:[^\n`]+|html|htm|css|js|javascript|xml|svg)\b", re.IGNORECASE
)
# The FIRST sign that deliverable code/markup has begun — a fence opener OR a
# raw HTML tag. Everything from here on is the artifact (possibly messy due to
# token-cap continuation), so we cut it from the chat entirely.
_ARTIFACT_START = re.compile(
    r"```file:[^\n`]+"
    r"|```(?:html|htm|css|js|javascript|xml|svg)\b"
    r"|<!doctype\s+html"
    r"|<html[\s>]"
    r"|<script[\s>]"
    r"|<style[\s>]",
    re.IGNORECASE,
)
# Extract a whole HTML document by TAGS (not fences): first opener to the LAST
# </html> (greedy), so a continuation-split artifact is still captured whole.
_HTML_DOC = re.compile(r"(?:<!doctype\s+html|<html[\s>]).*</html\s*>", re.IGNORECASE | re.DOTALL)
_HTML_OPEN = re.compile(r"<!doctype\s+html|<html[\s>]", re.IGNORECASE)
# Native SICS-1 / the model tool-call format the model emits on its own.
_NATIVE_TOOL = re.compile(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", re.DOTALL)


def _artifact_name(head):
    """Filename for an artifact fence opener line like ```file:foo.css or ```html."""
    m = re.match(r"```file:([^\n`]+)", head)
    if m:
        return m.group(1).strip()
    lang = re.match(r"```([a-z]+)", head.lower())
    return {"css": "styles.css", "js": "script.js", "javascript": "script.js"}.get(
        lang.group(1) if lang else "", "index.html"
    )


def _split(text):
    """Return (clean_answer, thinking). Robust to the common the model-thinking case

    where the chat template injects the opening <think> so the model only emits

    the CLOSING </think> (no opening tag in the stream)."""
    text = text or ""
    if "</think>" in text:
        # everything up to the first </think> is reasoning, even with no <think>
        i = text.index("</think>")
        thinking = text[:i].replace("<think>", "").strip()
        answer = text[i + len("</think>"):]
    elif "<think>" in text:
        # opening tag present but not yet closed -> still thinking, no answer
        i = text.index("<think>")
        answer = text[:i]
        thinking = text[i + len("<think>"):].strip()
    else:
        answer, thinking = text, ""
    answer = _TOOL_BLOCK.sub("", answer)               # hide tool-call JSON
    answer = answer.replace("<think>", "").replace("</think>", "")  # stray tags
    return answer.strip(), thinking


def _extract_doc(answer):
    """Assemble a single self-contained HTML document from the answer's

    HTML/CSS/JS code blocks, to render in the preview iframe."""
    htmls, csss, jss = [], [], []
    for lang, body in _CODE_BLOCK.findall(answer):
        l = (lang or "").lower().strip()
        b = body.strip()
        if not b or l == "tool":
            continue
        low = b.lower()
        if l in ("html", "htm") or "<!doctype" in low or "<html" in low or "<body" in low:
            htmls.append(b)
        elif l == "css":
            csss.append(b)
        elif l in ("js", "javascript"):
            jss.append(b)
        elif l == "" and "<" in b and ">" in b:
            htmls.append(b)
    doc = htmls[0] if htmls else ""
    if not doc and (csss or jss):
        doc = "<!DOCTYPE html><html><head><meta charset='utf-8'></head><body></body></html>"
    if not doc:
        return ""
    if "<html" not in doc.lower() and "<!doctype" not in doc.lower():
        doc = (
            "<!DOCTYPE html><html><head><meta charset='utf-8'></head><body>\n"
            + doc
            + "\n</body></html>"
        )
    if csss and "<style" not in doc.lower():
        style = "<style>\n" + "\n".join(csss) + "\n</style>"
        doc = doc.replace("</head>", style + "</head>", 1) if "</head>" in doc else style + doc
    if jss and "<script" not in doc.lower():
        script = "<script>\n" + "\n".join(jss) + "\n</script>"
        doc = doc.replace("</body>", script + "</body>", 1) if "</body>" in doc else doc + script
    return doc


def _extract_html_doc(text):
    """Pull a whole HTML document out of raw text by TAG boundaries, ignoring

    fences/prose. Handles a continuation-split or unterminated document."""
    m = _HTML_DOC.search(text or "")
    if m:
        return m.group(0).strip()
    o = _HTML_OPEN.search(text or "")  # unterminated (still being written)
    if o:
        doc = text[o.start():].strip()
        if "</html" not in doc.lower():
            doc += "\n</html>"
        return doc
    return ""


def _best_doc(text):
    """The most complete renderable doc: prefer whichever of the fenced-block

    assembly or the tag-based extraction is longer (more complete)."""
    a = _extract_doc(text or "")
    b = _extract_html_doc(text or "")
    return b if len(b) > len(a) else a


def _strip_artifacts(text):
    """Keep the chat clean and LEAK-PROOF: as soon as deliverable code/markup

    begins, cut everything from there and leave a pointer. Whatever messy mix of

    fences, raw tags, or continuation noise follows can never reach the chat."""
    if not text:
        return text
    m = _ARTIFACT_START.search(text)
    if not m:
        return text
    head = text[m.start(): m.start() + 80]
    fm = re.match(r"```file:([^\n`]+)", head)
    name = fm.group(1).strip() if fm else "index.html"
    pointer = f"📄 `{name}` → see **Preview** / **Code**"
    return (text[: m.start()].rstrip() + "\n\n" + pointer).strip()


def _capture_artifacts(text, files):
    """Route the model's deliverable into the write_file tool's virtual FS.

    Returns [(path, bytes), ...] for the tools trace."""
    captured = []
    for path, content in _FILE_BLOCK.findall(text or ""):
        path, content = path.strip(), content.strip("\n")
        if path:
            files[path] = content
            captured.append((path, len(content)))
    if not any(p.lower().endswith((".html", ".htm")) for p, _ in captured):
        doc = _best_doc(text or "")
        if doc:
            files["index.html"] = doc
            captured.append(("index.html", len(doc)))
    return captured


def _doc_from_files(files):
    """Newest .html file the model wrote this turn (the renderable artifact)."""
    html = [p for p in files if p.lower().endswith((".html", ".htm"))]
    if not html:
        return "", ""
    p = html[-1]
    return files[p], p


def _current_doc(files, answer):
    """Prefer a file the model WROTE; fall back to scraping the raw text."""
    doc, name = _doc_from_files(files)
    if doc:
        return doc, name
    return _best_doc(answer), "index.html"


# ---------------------------------------------------------------------------
# Tools — real, in-process. The whole ReAct loop runs inside ONE @spaces.GPU
# window (below), so tool calls execute on CPU while the GPU stays attached;
# multi-step turns add no extra cold starts.
# ---------------------------------------------------------------------------
def _t_web_search(query="", **_):
    try:
        from ddgs import DDGS
    except Exception:
        from duckduckgo_search import DDGS  # older name
    rows = list(DDGS().text(str(query), max_results=5))
    if not rows:
        return "(no results)"
    return "\n".join(
        f"- {r.get('title','')}: {(r.get('body') or '')[:200]} ({r.get('href','')})"
        for r in rows
    )


def _t_open_url(url="", **_):
    req = urllib.request.Request(str(url), headers={"User-Agent": "Mozilla/5.0"})
    with urllib.request.urlopen(req, timeout=15) as r:
        raw = r.read(300000).decode("utf-8", "ignore")
    text = re.sub(r"<script.*?</script>|<style.*?</style>", " ", raw, flags=re.S | re.I)
    text = re.sub(r"<[^>]+>", " ", text)
    text = re.sub(r"\s+", " ", text).strip()
    return text[:4000] or "(empty page)"


def _t_python(code="", **_):
    buf = io.StringIO()
    g = {"__name__": "__main__"}
    try:
        with contextlib.redirect_stdout(buf):
            exec(str(code), g)
    except Exception as e:  # noqa: BLE001
        return f"{buf.getvalue()}\n[error] {type(e).__name__}: {e}"[:4000]
    return (buf.getvalue() or "(ran, no stdout)")[:4000]


def _t_write_file(files, path="", content="", **_):
    path = (str(path) or "index.html").strip().lstrip("/")
    files[path] = str(content)
    return f"wrote {path} ({len(files[path])} bytes)" + (
        " — rendering in Preview" if path.lower().endswith((".html", ".htm")) else ""
    )


def _t_read_file(files, path="", **_):
    path = str(path).strip().lstrip("/")
    if path in files:
        return files[path][:4000]
    return f"[no such file: {path}] — files: {', '.join(sorted(files)) or '(none)'}"


def _t_list_files(files, **_):
    if not files:
        return "(no files yet)"
    return "\n".join(f"{p} ({len(files[p])} bytes)" for p in sorted(files))


# tools that take the turn-local virtual filesystem as first arg
FILE_TOOLS = {
    "write_file": _t_write_file,
    "read_file": _t_read_file,
    "list_files": _t_list_files,
}
# stateless tools
TOOLS = {"web_search": _t_web_search, "open_url": _t_open_url, "python": _t_python}
MAX_TOOL_STEPS = 6
MAX_CONTINUE = 3  # auto-resume rounds when a step hits the token cap mid-output

TOOLS_GUIDE = (
    "You can call real tools that run live in this chat. Keep the chat itself short — "
    "do NOT paste whole files or large output as plain text.\n"
    "\n"
    "FILES (the deliverable) — for any web app, page, game, or UI, just write the code in a "
    "normal ```html block (and ```css / ```js if you split it). It is automatically SAVED as "
    "a file, rendered live in the Preview panel, and its source shown in the Code panel — you "
    "do NOT need to repeat it in the message. For several named files use ```file:path blocks, "
    "e.g. ```file:game.js. In the chat write only a one-line note about what you built.\n"
    "Inspect saved files with a tool block: ```tool\n{\"name\": \"list_files\", \"args\": {}}\n``` "
    "or {\"name\": \"read_file\", \"args\": {\"path\": \"index.html\"}}\n"
    "\n"
    "PYTHON (compute) — write a normal ```python block with print(); it is EXECUTED and you get "
    "stdout back as a TOOL RESULT. Use it for math, data, and checks.\n"
    "\n"
    "WEB — to search or fetch, emit ONE tool block and stop:\n"
    "```tool\n{\"name\": \"web_search\", \"args\": {\"query\": \"...\"}}\n```\n"
    "(or {\"name\": \"open_url\", \"args\": {\"url\": \"...\"}}). You then receive a TOOL RESULT "
    "and continue.\n"
    "\n"
    "Use one tool per step, only when needed. When finished, give a brief final answer."
)


def _parse_call(text, seen):
    """Detect an *action* tool call that needs a result fed back: explicit

    ```tool JSON, native <tool_call> JSON, or a bare ```python block.

    Deliverable file blocks (html/css/js/file:) are NOT handled here — they are

    captured directly into the filesystem by _capture_artifacts. Returns a call

    dict or None; `seen` dedupes python blocks already run this turn."""
    text = text or ""
    blocks = list(_TOOL_BLOCK.findall(text)) + list(_NATIVE_TOOL.findall(text))
    for block in blocks:
        try:
            call = json.loads(block.strip())
        except Exception:
            continue
        if isinstance(call, dict) and "name" in call:
            # SICS-1/the model use "arguments"; we use "args"
            call["args"] = call.get("args") or call.get("arguments") or {}
            call.pop("arguments", None)
            return call
    pys = _PY_BLOCK.findall(text)
    if pys:
        code = pys[-1].strip()
        h = ("py", hash(code))
        if h not in seen:
            return {"name": "python", "args": {"code": code}, "_h": h}
    return None


def _exec_tool(call, files):
    name = call.get("name")
    args = call.get("args") or {}
    if not isinstance(args, dict):
        args = {"query": args} if name == "web_search" else {"code": str(args)}
    try:
        if name in FILE_TOOLS:
            return str(FILE_TOOLS[name](files, **args))[:4000]
        fn = TOOLS.get(name)
        if not fn:
            return f"[unknown tool: {name}]"
        return str(fn(**args))[:4000]
    except Exception as e:  # noqa: BLE001
        return f"[tool error] {type(e).__name__}: {e}"


# ---------------------------------------------------------------------------
# Preview: a browser-chrome frame around the live artifact iframe.
# ---------------------------------------------------------------------------
def _placeholder(title, sub, building=False):
    cls = "pv-empty pv-building" if building else "pv-empty"
    return (
        f'<div class="{cls}"><div class="pv-empty__glyph">⚗️</div>'
        f'<div class="pv-empty__title">{title}</div>'
        f'<div class="pv-empty__sub">{sub}</div></div>'
    )


_EMPTY_PREVIEW = _placeholder(
    "Живой превью",
    "SICS-1 рассуждает, строит и рендерит самодостаточный артефакт прямо здесь.",
)
_BUILDING_PREVIEW = _placeholder(
    "Собираю артефакт",
    "Формирую самодостаточную страницу из ответа модели.",
    building=True,
)
_NO_REASONING = "_В этом шаге нет рассуждения `<think>`._"
_NO_TOOLS = "_Инструменты в этом шаге не использовались._"


def _iframe(doc, name="artifact.html"):
    if not doc or not doc.strip():
        return _EMPTY_PREVIEW
    esc = _html.escape(doc, quote=True)
    pill = _html.escape(name or "artifact.html", quote=True)
    # data: URL so the chrome buttons can be plain <a> links — HF's CSP blocks
    # inline onclick handlers, so JS-driven buttons silently do nothing.
    data_url = "data:text/html;base64," + base64.b64encode(doc.encode("utf-8")).decode("ascii")
    # allow-same-origin: real origin so artifacts using localStorage/fetch work;
    # allow-top-navigation-by-user-activation: in-artifact links navigate on click.
    sandbox = ("allow-scripts allow-same-origin allow-modals allow-forms "
               "allow-popups allow-popups-to-escape-sandbox "
               "allow-top-navigation-by-user-activation")
    h = "clamp(440px,62vh,760px)"
    return (
        f'<div style="overflow:hidden;border-radius:14px;height:{h};">'
        '<div class="bchrome" style="height:100%;display:flex;flex-direction:column;">'
        '<div class="bchrome__bar">'
        '<span class="bchrome__dots"><i></i><i></i><i></i></span>'
        f'<span class="bchrome__pill">{pill}</span>'
        '<span class="bchrome__actions">'
        f'<a href="{data_url}" target="pvframe" title="Reload">&#8635;</a>'
        f'<a href="{data_url}" target="_blank" rel="noopener" title="Open in new tab">&#8599;</a>'
        '</span></div>'
        f'<iframe name="pvframe" srcdoc="{esc}" sandbox="{sandbox}"'
        f' style="display:block;width:100%;flex:1;min-height:0;border:0;background:#fff;"></iframe>'
        '</div>'
        '</div>'
    )


# ---------------------------------------------------------------------------
# Skills: reusable instruction snippets injected into the system prompt.
# ---------------------------------------------------------------------------
SKILLS = {
    "Single-file HTML artifact": (
        "When the user wants a web app, page, game, or visual UI, WRITE ONE complete, "
        "self-contained HTML file (inline CSS and JS) to disk with a ```file:index.html block "
        "— never paste the file in the chat. No external files. Avoid CDNs unless asked."
    ),
    "Tailwind via Play CDN": (
        "Style with Tailwind using the Play CDN (<script src=\"https://cdn.tailwindcss.com\">). "
        "Prefer utility classes; keep custom CSS minimal."
    ),
    "Canvas game loop": (
        "For games, use a <canvas> with a requestAnimationFrame loop, keyboard and touch "
        "controls, a score, and a restart. Target smooth 60fps."
    ),
    "Inline data-viz": (
        "For charts or dashboards, draw with inline SVG or Canvas (no chart libraries). "
        "Label axes, add a legend, and animate transitions."
    ),
    "Refined dark UI": (
        "Default to a refined dark interface: tinted near-black surfaces, one accent color, "
        "soft shadows, generous spacing, no pure black or white."
    ),
    "Mobile-first": (
        "Mobile-first and responsive: fluid layout, large tap targets, works from 320px up."
    ),
    "Accessible by default": (
        "Semantic HTML, alt text, ARIA where needed, visible focus rings, sufficient contrast."
    ),
    "Tasteful micro-interactions": (
        "Add restrained micro-interactions: ease-out transitions on hover and state changes, "
        "subtle entrance animations. Never animate layout in a way that janks."
    ),
}
DEFAULT_SKILLS = ["Single-file HTML artifact", "Tasteful micro-interactions"]

DEFAULT_SYS = (
    "You are SICS-1-35B, a sharp reasoning and building model — the engine behind "
    "«Фабрика гипотез». Reason inside <think> ... </think>, then give a focused, "
    "complete answer. Prefer runnable, self-contained solutions over fragments."
)


# Reasoning effort is a SOFT hint injected into the system prompt (not a hard
# token budget). The the model-format model respects it and self-regulates the
# length of its <think> chain.
EFFORT_HINTS = {
    "low": (
        "Reasoning effort: low. Think very briefly inside <think>...</think> "
        "(a couple of lines at most), then answer directly. Favor speed."
    ),
    "medium": (
        "Reasoning effort: medium. Think inside <think>...</think> with a short, "
        "focused chain, then answer."
    ),
    "high": (
        "Reasoning effort: high. Think thoroughly inside <think>...</think> — "
        "consider edge cases and alternatives — then give a careful answer."
    ),
}
DEFAULT_EFFORT = "low"


def _compose_system(base, selected, custom, use_tools, effort=DEFAULT_EFFORT):
    base = (base or "").strip()
    parts = [base] if base else []
    hint = EFFORT_HINTS.get((effort or "").lower())
    if hint:
        parts.append(hint)
    if use_tools:
        parts.append(TOOLS_GUIDE)
    lines = []
    for label in selected or []:
        instr = SKILLS.get(label)
        if instr:
            lines.append(f"- {label}: {instr}")
    for raw in (custom or "").splitlines():
        raw = raw.strip().lstrip("-").strip()
        if raw:
            lines.append(f"- {raw}")
    if lines:
        parts.append("Active skills (apply every one that is relevant):\n" + "\n".join(lines))
    return "\n\n".join(parts)


def _tools_md(trace):
    if not trace:
        return _NO_TOOLS
    out = []
    for i, (call, result) in enumerate(trace, 1):
        name = call.get("name")
        args = call.get("args", {}) or {}
        if name == "write_file":  # don't dump the whole file into the trace
            nbytes = call.get("_bytes", len(str(args.get("content", ""))))
            disp = json.dumps({"path": args.get("path", ""), "bytes": nbytes})
        else:
            disp = json.dumps(args, ensure_ascii=False)
            if len(disp) > 280:
                disp = disp[:280] + "…"
        out.append(f"**{i}. `{name}`** `{disp}`\n\n```\n{result[:1200]}\n```")
    return "\n\n".join(out)


def _g(o, key, default=None):
    """Get a field from a value that may be a dict OR a typed object."""
    if isinstance(o, dict):
        return o.get(key, default)
    return getattr(o, key, default)


def _chunk_fields(chunk):
    """Extract (content_delta, finish_reason) from a streaming chat chunk.

    llama-cpp-python returns either dict chunks or typed objects (Choice) across

    builds, so access both ways."""
    choices = _g(chunk, "choices") or []
    if not choices:
        return "", None
    ch = choices[0]
    finish = _g(ch, "finish_reason")
    delta = _g(ch, "delta") or {}
    content = _g(delta, "content", "") or ""
    return content, finish


def _count_tokens(llm, text):
    """Best-effort token count for the live ↑/↓ counter."""
    try:
        return len(llm.tokenize(text.encode("utf-8"), add_bos=False, special=True))
    except Exception:
        return max(1, len(text) // 4)


# ---------------------------------------------------------------------------
# Agent turn: full ReAct loop inside a single GPU window.
# ---------------------------------------------------------------------------
def _content_text(c):
    """Flatten a chat message's content to plain text. On follow-up turns Gradio

    returns content as a list of segment dicts ([{'type':'text','text':...}]),

    not a string — coerce it so the LLM and tokenizer always get a str."""
    if isinstance(c, str):
        return c
    if isinstance(c, list):
        parts = []
        for seg in c:
            if isinstance(seg, dict):
                parts.append(seg.get("text") or "")
            elif isinstance(seg, str):
                parts.append(seg)
        return "\n".join(p for p in parts if p)
    return str(c or "")


@spaces.GPU(duration=60)
def _agent_stream(message, history, system_prompt, temperature, max_tokens, use_tools):
    llm = _get_llm()
    msgs = []
    if system_prompt and system_prompt.strip():
        msgs.append({"role": "system", "content": system_prompt.strip()})
    for m in history:
        role = m.get("role")
        content = _content_text(m.get("content"))
        if role in ("user", "assistant") and content.strip():
            msgs.append({"role": role, "content": content})
    msgs.append({"role": "user", "content": _content_text(message)})

    trace = []
    seen = set()
    files = {}  # turn-local virtual filesystem the model writes into
    last_answer = ""
    last_thinking = ""
    transcript = ""  # raw, across steps
    in_tok = 0   # tokens sent INTO the model (↑), accumulated over steps
    out_tok = 0  # tokens generated (↓), accumulated over the turn

    for step in range(MAX_TOOL_STEPS if use_tools else 1):
        out = ""
        cont = 0
        # Generate the step; if the model hits the token cap mid-artifact
        # (finish_reason == "length"), auto-continue from where it stopped
        # and concatenate — so big builds don't get truncated.
        while True:
            if not out:
                gen_msgs = msgs
            else:
                mid_code = bool(_ARTIFACT_START.search(out))
                cont_instr = (
                    "Continue the file EXACTLY where you stopped. Output ONLY the raw "
                    "remaining file content — no prose, no explanations, no markdown "
                    "fences, no backticks. Do not repeat anything already written; "
                    "resume from the next character."
                ) if mid_code else (
                    "Continue EXACTLY where you stopped. Do not repeat or restate any "
                    "earlier text and do not restart — emit only the next characters."
                )
                gen_msgs = msgs + [
                    {"role": "assistant", "content": out},
                    {"role": "user", "content": cont_instr},
                ]
            in_tok += _count_tokens(llm, "\n".join(m.get("content", "") for m in gen_msgs))
            finish = None
            try:
                for chunk in llm.create_chat_completion(
                    messages=gen_msgs,
                    max_tokens=int(max_tokens),
                    temperature=float(temperature),
                    stream=True,
                ):
                    delta, fr = _chunk_fields(chunk)
                    if fr:
                        finish = fr
                    if not delta:
                        continue
                    out += delta
                    out_tok += 1
                    ans, think = _split(transcript + out)
                    disp = _strip_artifacts(ans)
                    doc, name = _current_doc(files, ans)
                    yield disp or "…", transcript + out, think, _tools_md(trace), doc, name, in_tok, out_tok
            except Exception as stream_err:  # streaming broken in this llama build -> non-stream fallback
                try:
                    resp = llm.create_chat_completion(
                        messages=gen_msgs, max_tokens=int(max_tokens),
                        temperature=float(temperature), stream=False,
                    )
                    ch0 = (_g(resp, "choices") or [None])[0]
                    msg = _g(ch0, "message") or {}
                    full = _g(msg, "content", "") or ""
                    finish = _g(ch0, "finish_reason")
                    out += full
                    out_tok += _count_tokens(llm, full)
                    ans, think = _split(transcript + out)
                    yield _strip_artifacts(ans) or "…", transcript + out, think, _tools_md(trace), \
                        *_current_doc(files, ans), in_tok, out_tok
                except Exception as gen_err:
                    err = f"⚠️ generation error: {type(gen_err).__name__}: {gen_err}"
                    yield err, transcript + "\n" + err, last_thinking, _tools_md(trace), "", "index.html", in_tok, out_tok
                    return
            if finish == "length" and cont < MAX_CONTINUE:
                cont += 1
                continue
            break

        transcript += out + "\n\n"
        last_answer, last_thinking = _split(transcript)
        msgs.append({"role": "assistant", "content": out})

        # Route this step's deliverable code blocks through write_file.
        if use_tools:
            for path, nbytes in _capture_artifacts(out, files):
                trace.append((
                    {"name": "write_file", "args": {"path": path}, "_bytes": nbytes},
                    f"wrote {path} ({nbytes} bytes) — rendered in Preview",
                ))

        disp_answer = _strip_artifacts(last_answer)
        call = _parse_call(out, seen) if use_tools else None
        doc, name = _current_doc(files, last_answer)
        if not call:
            break
        if "_h" in call:
            seen.add(call["_h"])
        yield (disp_answer or "…"), transcript, last_thinking, _tools_md(
            trace + [(call, "running…")]
        ), doc, name, in_tok, out_tok
        result = _exec_tool(call, files)
        trace.append((call, result))
        msgs.append({"role": "user", "content": f"TOOL RESULT ({call.get('name')}):\n{result}"})
        doc, name = _current_doc(files, last_answer)
        yield (disp_answer or "…"), transcript, last_thinking, _tools_md(trace), doc, name, in_tok, out_tok

    doc, name = _current_doc(files, last_answer)
    yield (_strip_artifacts(last_answer) or "…"), transcript, last_thinking, _tools_md(trace), doc, name, in_tok, out_tok


def _gen_status(in_tok, out_tok):
    """Live status strip shown while generating: a pulsing wing + token meters."""
    return (
        "<div class='gen-status'>"
        "<span class='gen-wing'>⚗️</span>"
        f"<span class='gen-tok'><b class='up'>↑</b> {in_tok:,} in</span>"
        f"<span class='gen-tok'><b class='dn'>↓</b> {out_tok:,} out</span>"
        "</div>"
    )


def _chat_message(answer, thinking, tools, streaming=False, in_tok=0, out_tok=0):
    """Compose the chat bubble: a live status strip while generating, then

    collapsible <details> for reasoning and tools, then the clean answer.

    Reasoning auto-opens while thinking (no answer yet)."""
    answer = (answer or "").strip()
    parts = []
    if streaming:
        parts.append(_gen_status(in_tok, out_tok))
    think = (thinking or "").strip()
    if think:
        opened = " open" if (streaming and not answer) else ""
        parts.append(
            f"<details class='turn-fold'{opened}>"
            f"<summary>🧠 Reasoning</summary>\n\n{think}\n\n</details>"
        )
    trace = (tools or "").strip()
    if trace and trace != _NO_TOOLS:
        n = trace.count("\n\n**") + (1 if trace.startswith("**") else 0)
        label = f"🛠 Tools · {n}" if n else "🛠 Tools"
        parts.append(
            f"<details class='turn-fold'>"
            f"<summary>{label}</summary>\n\n{trace}\n\n</details>"
        )
    if answer:
        parts.append(answer)
    return "\n\n".join(parts) if parts else "…"


def respond(message, history, base_sys, skills, custom_skills, use_tools,

            effort, temperature, max_tokens, meta):
    meta = meta or []
    if not message or not message.strip():
        yield history or [], "", "", _NO_REASONING, "", _EMPTY_PREVIEW, _NO_TOOLS, meta
        return
    system_prompt = _compose_system(base_sys, skills, custom_skills, use_tools, effort)
    history = (history or []) + [
        {"role": "user", "content": message},
        {"role": "assistant", "content": ""},
    ]
    prior = history[:-2]
    answer = raw = thinking = tools = doc = ""
    doc_name = "artifact.html"
    in_tok = out_tok = 0
    try:
        for answer, raw, thinking, tools, doc, doc_name, in_tok, out_tok in _agent_stream(
            message, prior, system_prompt, temperature, max_tokens, use_tools
        ):
            history[-1]["content"] = _chat_message(
                answer, thinking, tools, streaming=True, in_tok=in_tok, out_tok=out_tok
            )
            prev = _BUILDING_PREVIEW if doc else _EMPTY_PREVIEW
            yield history, "", raw, (thinking or _NO_REASONING), doc, prev, (tools or _NO_TOOLS), meta
    except Exception:
        import traceback
        tb = traceback.format_exc()
        history[-1]["content"] = f"⚠️ **generation crashed**\n\n```\n{tb[-1800:]}\n```"
        yield history, "", tb, (thinking or _NO_REASONING), doc, _EMPTY_PREVIEW, (tools or _NO_TOOLS), meta
        return
    history[-1]["content"] = _chat_message(answer, thinking, tools, streaming=False)
    meta = meta + [{
        "turn_id": uuid.uuid4().hex, "user": message, "answer": answer,
        "reasoning": thinking, "code": doc, "tools": tools,
    }]
    yield history, "", raw, (thinking or _NO_REASONING), doc, _iframe(doc, doc_name), (tools or _NO_TOOLS), meta


# ---------------------------------------------------------------------------
# Look & feel
# ---------------------------------------------------------------------------
HERO = """

<div class="hero">

  <div class="hero__badge">⚗️</div>

  <div class="hero__body">

    <div class="hero__eyebrow">Фабрика гипотез · AI co-scientist</div>

    <div class="hero__title">SICS-1<span>-35B</span></div>

    <div class="hero__sub">Из анализа хвостов флотации — ранжированные, обоснованные и проверяемые гипотезы для извлечения Ni и Cu.</div>

    <div class="hero__chips">

      <span class="chip">35B MoE · reasoning</span>

      <span class="chip">GGUF · Q4_K_M</span>

      <span class="chip chip--accent">ZeroGPU · llama.cpp</span>

      <span class="chip chip--copper">Ni · Cu · хвосты флотации</span>

    </div>

  </div>

</div>

"""

CSS = """

:root{

  /* deep slate / graphite base — industrial, metallurgy-adjacent */

  --bg: oklch(0.17 0.018 248);

  --surface: oklch(0.21 0.020 248);

  --surface-2: oklch(0.26 0.022 248);

  --line: oklch(0.36 0.022 248);

  --line-soft: oklch(0.36 0.022 248 / 0.55);

  --text: oklch(0.97 0.008 240);

  --muted: oklch(0.73 0.020 245);

  /* teal (blue→green) primary + copper secondary — Cu/Ni recovery */

  --accent: oklch(0.80 0.130 190);

  --accent-2: oklch(0.76 0.135 55);

  --accent-soft: oklch(0.80 0.130 190 / 0.14);

  --accent-ring: oklch(0.80 0.130 190 / 0.32);

  --copper-soft: oklch(0.76 0.135 55 / 0.15);

  --radius: 14px;

  --radius-sm: 10px;

  --pill: 999px;

  --ease: cubic-bezier(.2,.7,.2,1);

  --shadow-1: 0 1px 2px oklch(0.10 0.02 248 / 0.40);

  --shadow-2: 0 10px 26px -14px oklch(0.06 0.02 248 / 0.60);

  --shadow-pop: 0 18px 40px -24px oklch(0.05 0.02 248 / 0.72);

}

.gradio-container{max-width:1320px !important;width:100% !important;margin:0 auto !important;}

/* force the app to actually fill the width (Gradio sometimes leaves it shrunk-left) */

.gradio-container > .main,

.gradio-container .main > .wrap,

.gradio-container .main .contain,

.gradio-container .main .contain > .row{width:100% !important;}

.gradio-container .main .contain > .row{flex-wrap:nowrap;}

footer{display:none !important;}



/* crisp, consistent focus + selection + thin scrollbars */

.gradio-container :focus-visible{outline:2px solid var(--accent) !important;outline-offset:2px;border-radius:6px;}

::selection{background:var(--accent-soft);color:var(--text);}

*{scrollbar-width:thin;scrollbar-color:var(--line) transparent;}

::-webkit-scrollbar{width:10px;height:10px;}

::-webkit-scrollbar-thumb{background:var(--line);border-radius:var(--pill);border:2px solid transparent;background-clip:content-box;}

::-webkit-scrollbar-thumb:hover{background:var(--muted);}

@media (prefers-reduced-motion: reduce){

  *,*::before,*::after{animation-duration:.001ms !important;transition-duration:.001ms !important;}

}



.hero{display:flex;gap:18px;align-items:center;padding:20px 22px;border:1px solid var(--line);

  border-radius:18px;position:relative;overflow:hidden;background:

    radial-gradient(120% 160% at 0% 0%, var(--accent-soft), transparent 55%),

    radial-gradient(120% 160% at 100% 0%, var(--copper-soft), transparent 60%),

    var(--surface);box-shadow:var(--shadow-2);}

/* thin teal→copper seam along the top edge */

.hero::before{content:"";position:absolute;inset:0 0 auto 0;height:2px;

  background:linear-gradient(90deg, var(--accent), oklch(0.72 0.10 150), var(--accent-2));opacity:.85;}

.hero__badge{display:grid;place-items:center;width:56px;height:56px;flex:none;border-radius:15px;

  font-size:28px;background:var(--accent-soft);border:1px solid var(--accent-ring);

  box-shadow:0 1px 0 oklch(1 0 0 / 0.06) inset, 0 8px 22px -14px var(--accent-ring);}

.hero__eyebrow{font-size:11.5px;font-weight:700;letter-spacing:0.16em;text-transform:uppercase;

  color:var(--accent);margin-bottom:5px;}

.hero__title{font-size:27px;font-weight:800;letter-spacing:-0.015em;color:var(--text);line-height:1.05;}

.hero__title span{color:var(--muted);font-weight:600;}

.hero__sub{color:var(--muted);font-size:14px;margin-top:5px;max-width:62ch;line-height:1.5;}

.hero__chips{display:flex;gap:8px;flex-wrap:wrap;margin-top:13px;}

.chip{font-size:12px;color:var(--muted);background:var(--surface-2);border:1px solid var(--line);

  padding:4px 11px;border-radius:999px;line-height:1.5;font-variant-numeric:tabular-nums;}

.chip--accent{color:var(--text);border-color:var(--accent-ring);background:var(--accent-soft);}

.chip--copper{color:var(--text);border-color:oklch(0.76 0.135 55 / 0.5);background:var(--copper-soft);}

.chip--link{color:var(--accent);text-decoration:none;}

.chip--link:hover{background:var(--accent-soft);}



.bchrome{border:1px solid var(--line);border-radius:14px;overflow:hidden;background:var(--surface);

  box-shadow:0 18px 40px -24px oklch(0 0 0 / 0.7);}

.bchrome__bar{display:flex;align-items:center;gap:10px;padding:9px 12px;background:var(--surface-2);

  border-bottom:1px solid var(--line);}

.bchrome__dots{display:inline-flex;gap:6px;}

.bchrome__dots i{width:11px;height:11px;border-radius:50%;background:var(--line);display:block;}

.bchrome__dots i:nth-child(1){background:oklch(0.66 0.17 25);}

.bchrome__dots i:nth-child(2){background:oklch(0.78 0.15 85);}

.bchrome__dots i:nth-child(3){background:oklch(0.74 0.16 150);}

.bchrome__pill{flex:1;text-align:center;font-size:12px;color:var(--muted);background:var(--bg);

  border:1px solid var(--line);border-radius:8px;padding:3px 10px;max-width:340px;margin:0 auto;

  font-family:ui-monospace,monospace;}

.bchrome__actions{display:inline-flex;gap:4px;}

.bchrome__actions a{all:unset;cursor:pointer;color:var(--muted);font-size:15px;width:26px;height:26px;

  display:grid;place-items:center;border-radius:7px;text-decoration:none;}

.bchrome__actions a:hover{color:var(--text);background:var(--bg);}

.bchrome iframe{display:block;width:100%;height:clamp(440px,62vh,760px);border:0;background:#fff;}



.pv-empty{display:flex;flex-direction:column;align-items:center;justify-content:center;text-align:center;

  gap:8px;min-height:clamp(440px,62vh,760px);border:1px dashed var(--line);border-radius:14px;

  background:

    radial-gradient(60% 60% at 50% 38%, var(--accent-soft), transparent 70%),

    repeating-linear-gradient(45deg, transparent 0 13px, oklch(1 0 0 / 0.012) 13px 14px),

    var(--surface);}

.pv-empty__glyph{font-size:40px;opacity:0.9;}

.pv-empty__title{font-size:16px;font-weight:700;color:var(--text);}

.pv-empty__sub{font-size:13px;color:var(--muted);max-width:36ch;}

.pv-building .pv-empty__glyph{animation:floaty 1.6s ease-in-out infinite;}

@keyframes floaty{0%,100%{transform:translateY(0)}50%{transform:translateY(-7px)}}



/* ---- pill-group controls: skills (multi) + effort segmented (single) ---- */

.skills .wrap, .seg .wrap{gap:8px !important;}

.skills label, .seg label{border:1px solid var(--line) !important;background:var(--surface-2) !important;

  border-radius:var(--pill) !important;padding:7px 14px !important;color:var(--muted) !important;

  cursor:pointer;transition:border-color .18s var(--ease),background .18s var(--ease),color .18s var(--ease),transform .12s var(--ease);}

.skills label:hover, .seg label:hover{border-color:var(--accent) !important;color:var(--text) !important;}

.skills label:active, .seg label:active{transform:translateY(1px);}

.skills label:has(input:checked), .seg label:has(input:checked){

  border-color:var(--accent) !important;background:var(--accent-soft) !important;color:var(--text) !important;}

.seg input[type="radio"]{position:absolute;opacity:0;width:0;height:0;}



/* ---- tabs -> a segmented pill control instead of the default underline ---- */

.tab-nav, div.tab-nav{border:none !important;gap:4px;padding:4px;background:var(--surface-2);

  border:1px solid var(--line) !important;border-radius:12px;flex-wrap:wrap;}

.tab-nav button{border:none !important;border-radius:9px !important;color:var(--muted) !important;

  font-weight:600;font-size:13px;padding:7px 14px !important;transition:color .18s var(--ease),background .18s var(--ease);}

.tab-nav button:hover{color:var(--text) !important;background:var(--bg);}

.tab-nav button.selected{color:var(--text) !important;background:var(--accent-soft) !important;

  box-shadow:inset 0 0 0 1px var(--accent);}



/* ---- examples -> clickable chips instead of a clunky table ---- */

#examples{border:none !important;background:transparent !important;}

#examples .label-wrap, #examples > .label, #examples thead{display:none !important;}

#examples table, #examples tbody, #examples .table-wrap{border:none !important;background:transparent !important;

  display:block !important;box-shadow:none !important;}

#examples tbody{display:flex !important;flex-wrap:wrap;gap:8px;}

#examples tr{display:block !important;border:none !important;background:transparent !important;}

#examples td, #examples .gallery-item{border:1px solid var(--line) !important;background:var(--surface-2) !important;

  border-radius:var(--pill) !important;padding:7px 14px !important;color:var(--muted) !important;font-size:13px !important;

  cursor:pointer;transition:border-color .18s var(--ease),background .18s var(--ease),color .18s var(--ease);

  white-space:normal;max-width:260px;line-height:1.35;}

#examples td:hover, #examples .gallery-item:hover{border-color:var(--accent) !important;color:var(--text) !important;

  background:var(--accent-soft) !important;}



/* ---- chat surface + bubbles ---- */

.chatbot, .bubble-wrap{background:transparent !important;border:none !important;}

.message, .message-row .message{border-radius:14px !important;border:1px solid var(--line) !important;

  box-shadow:none !important;line-height:1.55;}

.user .message, .user-row .message, .message.user{background:var(--accent-soft) !important;border-color:transparent !important;}

.bot .message, .bot-row .message, .message.bot{background:var(--surface-2) !important;}



/* ---- live generation status: pulsing wing + token meters ---- */

.gen-status{display:inline-flex;align-items:center;gap:14px;padding:6px 14px;margin:0 0 10px;

  border:1px solid var(--line);border-radius:var(--pill);background:var(--surface-2);

  font-size:12.5px;color:var(--muted);font-variant-numeric:tabular-nums;}

.gen-wing{display:inline-block;font-size:16px;animation:wingpulse 1.15s var(--ease) infinite;

  filter:drop-shadow(0 0 7px var(--accent-ring));}

@keyframes wingpulse{

  0%,100%{opacity:1;transform:scale(1) translateY(0);}

  50%{opacity:.45;transform:scale(1.14) translateY(-1px);}

}

.gen-tok{display:inline-flex;align-items:center;gap:5px;}

.gen-tok b{font-style:normal;font-weight:800;font-size:13px;}

.gen-tok .up{color:var(--accent-2);}

.gen-tok .dn{color:var(--accent);}



/* ---- collapsible reasoning / tools inside a chat bubble ---- */

.turn-fold{border:1px solid var(--line);border-radius:var(--radius-sm);background:var(--bg);

  margin:0 0 8px;overflow:hidden;}

.turn-fold > summary{cursor:pointer;list-style:none;padding:8px 12px;font-size:12.5px;font-weight:600;

  color:var(--muted);display:flex;align-items:center;gap:6px;user-select:none;

  transition:color .15s var(--ease),background .15s var(--ease);}

.turn-fold > summary::-webkit-details-marker{display:none;}

.turn-fold > summary::after{content:"⌄";margin-left:auto;transition:transform .2s var(--ease);font-size:14px;}

.turn-fold[open] > summary{color:var(--text);border-bottom:1px solid var(--line);background:var(--surface-2);}

.turn-fold[open] > summary::after{transform:rotate(180deg);}

.turn-fold > summary:hover{color:var(--text);}

.turn-fold > *:not(summary){padding:4px 12px 10px;font-size:13px;}

.turn-fold pre{background:var(--bg) !important;border:1px solid var(--line);border-radius:8px;}



/* ---- command bar (the main input) ---- */

#cmdbar textarea, #cmdbar input{font-size:15px !important;padding:13px 16px !important;border-radius:var(--radius) !important;

  background:var(--surface) !important;border:1px solid var(--line) !important;color:var(--text) !important;

  transition:border-color .18s var(--ease),box-shadow .18s var(--ease);}

#cmdbar textarea:focus, #cmdbar input:focus{border-color:var(--accent) !important;

  box-shadow:0 0 0 3px var(--accent-soft) !important;outline:none !important;}



/* ---- buttons: soft depth + hover lift ---- */

.gradio-container button.primary{box-shadow:var(--shadow-1);font-weight:650;

  transition:transform .12s var(--ease),box-shadow .18s var(--ease),filter .18s var(--ease);

  background-image:linear-gradient(180deg, oklch(1 0 0 / 0.06), transparent) !important;}

.gradio-container button.primary:hover{transform:translateY(-1px);box-shadow:var(--shadow-2);filter:brightness(1.05);}

.gradio-container button.primary:active{transform:translateY(0);box-shadow:var(--shadow-1);}

.gradio-container button.secondary{background:var(--surface-2) !important;border:1px solid var(--line) !important;

  color:var(--muted) !important;transition:border-color .18s var(--ease),color .18s var(--ease),transform .12s var(--ease);}

.gradio-container button.secondary:hover{border-color:var(--accent) !important;color:var(--text) !important;}

.gradio-container button.secondary:active{transform:translateY(1px);}



/* ---- accordions + blocks: quieter borders, rounder ---- */

.gradio-container .block, .gradio-container .form{border-radius:var(--radius) !important;}

.gradio-container .label-wrap > span, .gradio-container span[data-testid="block-info"]{color:var(--muted) !important;}

.accordion, .gradio-accordion{border:1px solid var(--line) !important;border-radius:var(--radius) !important;

  background:var(--surface) !important;}



/* ---- sliders ---- */

input[type="range"]{accent-color:var(--accent);}



/* ---- subtle entrance for the whole app ---- */

.gradio-container > .main, .gradio-container .contain{animation:appfade .4s var(--ease) both;}

@keyframes appfade{from{opacity:0;transform:translateY(6px)}to{opacity:1;transform:none}}

"""

THEME = gr.themes.Base(
    primary_hue=gr.themes.colors.teal,
    secondary_hue=gr.themes.colors.amber,
    neutral_hue=gr.themes.colors.slate,
    # System font stacks only — HF Spaces CSP blocks external font CDNs.
    font=["ui-sans-serif", "system-ui", "-apple-system", "Segoe UI", "Roboto", "sans-serif"],
    font_mono=["ui-monospace", "SFMono-Regular", "Menlo", "Consolas", "monospace"],
).set(
    body_background_fill="oklch(0.17 0.018 248)",
    body_background_fill_dark="oklch(0.17 0.018 248)",
    background_fill_primary="oklch(0.21 0.020 248)",
    background_fill_primary_dark="oklch(0.21 0.020 248)",
    background_fill_secondary="oklch(0.26 0.022 248)",
    block_background_fill="oklch(0.21 0.020 248)",
    block_border_color="oklch(0.36 0.022 248)",
    border_color_primary="oklch(0.36 0.022 248)",
    body_text_color="oklch(0.97 0.008 240)",
    body_text_color_subdued="oklch(0.73 0.020 245)",
    block_label_text_color="oklch(0.73 0.020 245)",
    button_primary_background_fill="oklch(0.80 0.130 190)",
    button_primary_background_fill_hover="oklch(0.84 0.130 190)",
    button_primary_text_color="oklch(0.18 0.020 248)",
    input_background_fill="oklch(0.20 0.020 248)",
    block_radius="14px",
)

# Domain-flavored one-liners: each yields a reasoning answer or a live artifact.
EXAMPLES = [
    "Сформулируй 3 проверяемые гипотезы, как поднять извлечение меди из хвостов флотации.",
    "Построй интерактивный дашборд извлечения Ni и Cu из хвостов флотации.",
    "Визуализируй кривую кинетики пенной флотации на canvas.",
    "Сделай калькулятор извлечения металла: содержания в питании и хвостах → % извлечения.",
    "Тепловая карта содержания Ni по пробам — нарисуй инлайн SVG с легендой.",
    "Объясни механизм пенной флотации простыми словами, как хорошему другу.",
]

with gr.Blocks(title="SICS-1-35B · Фабрика гипотез", fill_height=True, fill_width=True) as demo:
    gr.HTML(HERO)
    meta_state = gr.State([])
    with gr.Row(equal_height=False):
        with gr.Column(scale=5):
            chatbot = gr.Chatbot(
                height=460, show_label=False, sanitize_html=False,
                placeholder="Спросите SICS-1 — гипотеза, расчёт, артефакт или поиск источников.",
            )
            msg = gr.Textbox(
                placeholder='напр. «построй дашборд извлечения Ni/Cu из хвостов»  ·  Enter — отправить',
                show_label=False, autofocus=True, lines=1, elem_id="cmdbar",
            )
            with gr.Row():
                send = gr.Button("Отправить", variant="primary", scale=3)
                clear = gr.Button("Очистить", scale=1)
            gr.Examples(examples=EXAMPLES, inputs=msg, label="Примеры", elem_id="examples")
        with gr.Column(scale=6):
            with gr.Tabs():
                with gr.Tab("Превью"):
                    preview = gr.HTML(_EMPTY_PREVIEW, **_HTML_RAW)
                with gr.Tab("Инструменты"):
                    tools_box = gr.Markdown(_NO_TOOLS)
                with gr.Tab("Код"):
                    code_box = gr.Code(label=None, language="html")
                with gr.Tab("Рассуждение"):
                    think_box = gr.Markdown(_NO_REASONING)
                with gr.Tab("Raw"):
                    raw_box = gr.Textbox(show_label=False, lines=18, max_lines=18)
            with gr.Accordion("Навыки и инструменты", open=True):
                use_tools = gr.Checkbox(
                    value=True,
                    label="Инструменты  ·  web · fetch · python",
                )
                effort = gr.Radio(
                    choices=["low", "medium", "high"], value=DEFAULT_EFFORT,
                    label="Глубина рассуждения",
                    info="low — короче и быстрее · high — глубже и медленнее",
                    elem_classes=["seg"],
                )
                skills = gr.CheckboxGroup(
                    choices=list(SKILLS.keys()), value=DEFAULT_SKILLS,
                    show_label=False, elem_classes=["skills"],
                )
                custom_skills = gr.Textbox(
                    label="Свои навыки (по одному в строке)",
                    placeholder="Всегда добавляй горячие клавиши\nПиши лаконичный, комментированный код",
                    lines=2,
                )
            with gr.Accordion("Настройки", open=False):
                system_prompt = gr.Textbox(value=DEFAULT_SYS, label="Базовый системный промпт", lines=3)
                temperature = gr.Slider(0.0, 1.5, value=0.6, step=0.05, label="Температура")
                max_tokens = gr.Slider(256, 8192, value=6144, step=128, label="Макс. токенов / шаг")

    inputs = [msg, chatbot, system_prompt, skills, custom_skills, use_tools,
              effort, temperature, max_tokens, meta_state]
    outputs = [chatbot, msg, raw_box, think_box, code_box, preview, tools_box, meta_state]
    send.click(respond, inputs, outputs)
    msg.submit(respond, inputs, outputs)
    clear.click(
        lambda: ([], "", "", _NO_REASONING, "", _EMPTY_PREVIEW, _NO_TOOLS, []), None, outputs
    )

demo.queue().launch(theme=THEME, css=CSS, ssr_mode=False, show_error=True)