hermes-agent/tools/flux3_video_tool.py

"""Native BFL FLUX 3 video generation tools, backed by the Nous tool gateway.

These are service-gated native tools in the ``image_generate`` mold: schemas
and descriptions are pinned here as build-time facts, the handlers speak the
gateway's own REST contract, and ``check_fn`` hides the whole toolset unless
the user is signed in to Nous Portal with paid service access. No runtime
discovery, and no server-supplied schema is ever consulted — that is the point
of the design.

The wire is two calls against the gateway's managed mount, and it names the
vendor but not the vendor's API:

- ``POST {base}/generations`` with ``{mode, prompt, ...}`` -> ``{id, status,
  guidance}``
- ``GET {base}/generations/<id>`` -> the job state plus ``guidance``

``guidance`` is the gateway's live policy channel: exact waits, what to do
next, and how to deliver a finished clip ship from the server so they cannot
drift from what it actually enforces. Handlers surface it verbatim as the
tool's result text, and surface ``error.message`` the same way on a refusal.

Media inputs: handlers know their own media fields explicitly. A local file
path is resolved through :func:`tools.image_source.resolve_image_source`
(sandbox confinement, credential guard) and delivered via the Nous upload
protocol (presign, direct PUT to storage, ``nous-upload:<token>`` reference).
URLs pass through untouched.
"""

from __future__ import annotations

import asyncio
import json
import logging
import re
from typing import Optional

from tools.registry import registry
from tools.managed_tool_gateway import (
    build_managed_media_uploader,
    managed_gateway_auth_headers,
    managed_vendor_endpoints,
    read_nous_access_token,
)

logger = logging.getLogger(__name__)

_TOOLSET = "bfl"
_VENDOR = "bfl"

# Submit sits behind the gateway's upstream call plus upload-reference
# resolution, and the gateway bounds a poll server-side. One generous read
# timeout covers both without ever approaching the agent's per-tool budget.
_TRANSPORT_READ_TIMEOUT_SECONDS = 180.0
_TRANSPORT_CONNECT_TIMEOUT_SECONDS = 10.0

_SIGN_IN_MESSAGE = (
    "BFL video generation needs a Nous Portal sign-in with an active paid plan. "
    "Ask the user to run `hermes model` and sign in to Nous, then retry."
)

# ---------------------------------------------------------------------------
# Local-path detection (moved from the retired MCP media-argument walker)
# ---------------------------------------------------------------------------

# Drive-absolute Windows paths: ``C:\Users\...`` / ``C:/Users/...``.
_WINDOWS_DRIVE_PATH = re.compile(r"^[A-Za-z]:[\\/]")

# A whole string of base64 alphabet (optionally line-wrapped, optionally
# padded). Base64's alphabet includes "/", so an inline JPEG payload always
# starts with "/9j/" — which reads as an absolute path unless caught first.
_BASE64_PAYLOAD = re.compile(r"^[A-Za-z0-9+/\r\n]+={0,2}[\r\n]*$")
# Real filesystem paths are short; base64 of even a thumbnail runs to
# thousands of characters. Anything this long and alphabet-pure is a payload.
_MIN_BASE64_PAYLOAD_LENGTH = 256


def _looks_like_local_path(value: str) -> bool:
    """True for things we should read off disk rather than forward as-is.

    Deliberately narrow: only explicitly rooted paths and ``file://`` qualify.
    Bare names are ambiguous with opaque ids, URLs pass through, and base64
    payloads (checked first — a JPEG's base64 starts ``/9j/``) fall through
    untouched.
    """
    if len(value) >= _MIN_BASE64_PAYLOAD_LENGTH and _BASE64_PAYLOAD.match(value):
        return False
    if value.startswith("file://"):
        return True
    if value == "~" or value.startswith(("~/", "~\\")):
        return True
    if value.startswith(("/", "./", "../", ".\\", "..\\")):
        return True
    if _WINDOWS_DRIVE_PATH.match(value) or value.startswith("\\\\"):
        return True
    return False


def _display_path(path: str) -> str:
    """A value safe to embed in an error message shown to the model."""
    return path if len(path) <= 200 else f"{path[:200]}… ({len(path)} characters)"


# ---------------------------------------------------------------------------
# Gateway transport
# ---------------------------------------------------------------------------

def _endpoints() -> Optional[dict]:
    """Absolute URLs for the managed BFL routes, or None when unreachable."""
    return managed_vendor_endpoints(_VENDOR)


async def _call_gateway(method: str, url: str, json_body: Optional[dict] = None) -> str:
    """One REST round trip, rendered as this tool's result.

    The gateway's ``guidance`` (on success) and ``error.message`` (on a
    refusal) are both written for a model to act on, so they are surfaced
    verbatim. A refusal is a normal outcome the model can respond to — being
    throttled is not a broken tool — so only genuinely unreadable responses
    become ``error``.
    """
    import httpx

    headers = managed_gateway_auth_headers(url)
    if not headers:
        return json.dumps({"error": _SIGN_IN_MESSAGE})
    headers["Content-Type"] = "application/json"

    timeout = httpx.Timeout(_TRANSPORT_CONNECT_TIMEOUT_SECONDS, read=_TRANSPORT_READ_TIMEOUT_SECONDS)
    try:
        async with httpx.AsyncClient(timeout=timeout) as client:
            response = await client.request(method, url, headers=headers, json=json_body)
    except Exception as exc:
        return json.dumps({
            "error": f"Could not reach the video-generation gateway: {type(exc).__name__}: {exc}",
        })

    if response.status_code == 401:
        return json.dumps({"error": _SIGN_IN_MESSAGE, "needs_reauth": True})

    try:
        payload = response.json()
    except Exception:
        payload = None

    if not isinstance(payload, dict):
        return json.dumps({
            "error": f"The video-generation gateway answered HTTP {response.status_code} with an unreadable body.",
        })

    if response.status_code >= 400:
        error = payload.get("error") if isinstance(payload.get("error"), dict) else {}
        message = error.get("message") or f"the gateway refused the request (HTTP {response.status_code})"
        out: dict = {"error": str(message)}
        if isinstance(error.get("details"), dict):
            out["details"] = error["details"]
        return json.dumps(out, ensure_ascii=False)

    guidance = payload.pop("guidance", None)
    return json.dumps(
        {"result": guidance or "Request accepted.", "details": payload},
        ensure_ascii=False,
    )


# ---------------------------------------------------------------------------
# Media delivery
# ---------------------------------------------------------------------------

# Every media field the gateway understands, and the media kind each accepts.
# `input_image` and `input_images` are interchangeable server-side, so both
# must be sanitized whatever the mode.
_MEDIA_FIELDS = {
    "input_image": ("image",),
    "input_images": ("image",),
    "input_video": ("video",),
}
# The vendor takes at most ten keyframes. Checked here as well as server-side
# so an over-long list is refused before it spends the caller's upload quota.
_MAX_IMAGES = 10


def _warm_nous_token() -> None:
    """Refresh the Nous token once, before any parallel upload needs it.

    ``read_nous_access_token`` takes no lock and, when a refresh fails, falls
    back to returning the stale cached token. Uploading in parallel therefore
    had every request discover the token was expiring at the same instant and
    fire its own refresh; the rotating refresh token means the first wins and
    the rest quietly send the stale bearer, which the gateway answers with 401.
    One warm-up call first puts a fresh token in the cache, so the fan-out all
    reads it instead of racing for it.
    """
    try:
        read_nous_access_token()
    except Exception as exc:  # pragma: no cover — the real read retries below
        logger.debug("Nous token warm-up failed before parallel uploads: %s", exc)


async def _prepare_media(args: dict, task_id: Optional[str]) -> dict:
    """Replace local paths with upload references in every media field.

    Deliberately covers all media fields rather than the one this mode
    expects. The gateway accepts `input_image` and `input_images`
    interchangeably, so a value left unsanitized in the "wrong" field still
    reaches the vendor — as a raw local path, which both fails the generation
    and discloses the user's directory layout to a third party.
    """
    prepared = dict(args or {})
    _warm_nous_token()
    for field, permitted in _MEDIA_FIELDS.items():
        value = prepared.get(field)
        if value is None:
            continue
        if isinstance(value, list):
            if len(value) > _MAX_IMAGES:
                raise ValueError(f"{field} takes at most {_MAX_IMAGES} images; you passed {len(value)}.")
            # Uploaded together rather than one after another: each entry is a
            # presign round trip plus a full-file PUT, and ten keyframes in
            # sequence took long enough that a submit looked hung.
            prepared[field] = list(
                await asyncio.gather(*(_deliver_media(entry, permitted, task_id) for entry in value))
            )
        else:
            prepared[field] = await _deliver_media(value, permitted, task_id)
    return prepared


def _without_media(args: dict) -> dict:
    """Drop media fields entirely — for the mode that takes none.

    The gateway ignores them for text-to-video, so stripping here matches its
    behaviour while avoiding an upload the caller never needed.
    """
    return {k: v for k, v in dict(args or {}).items() if k not in _MEDIA_FIELDS}


async def _deliver_media(value, permitted: tuple, task_id: Optional[str]):
    """Replace a local path with a ``nous-upload:`` reference; pass URLs through.

    Raises ``ValueError`` with a model-readable sentence when the file cannot
    be read or uploaded — the caller turns that into the tool's error payload.
    """
    if not isinstance(value, str) or not _looks_like_local_path(value):
        return value

    from tools.image_source import ImageResolutionError, ResolveContext, resolve_image_source

    try:
        resolved = await resolve_image_source(value, ResolveContext(task_id=task_id), permitted=permitted)
    except ImageResolutionError as exc:
        raise ValueError(f"Could not read {_display_path(value)}: {exc}") from exc

    endpoints = _endpoints()
    uploader = None
    if endpoints is not None:
        uploader = build_managed_media_uploader(endpoints["base_url"], endpoints["upload_path"])
    if uploader is None:
        raise ValueError("Local file uploads are not available in this build; pass a URL instead.")

    try:
        return await uploader(resolved.data, resolved.mime)
    except Exception as exc:
        raise ValueError(f"Could not upload {_display_path(value)}: {exc}") from exc


# ---------------------------------------------------------------------------
# Saving the finished clip
# ---------------------------------------------------------------------------

# Generous: a 50MB clip over a slow link, still well inside the agent's budget.
_DOWNLOAD_READ_TIMEOUT_SECONDS = 300.0
_DOWNLOAD_CONNECT_TIMEOUT_SECONDS = 15.0
# A rejection page is a few hundred bytes of XML; a clip is megabytes. Anything
# smaller than this is not the video, whatever the HTTP status said.
_MIN_PLAUSIBLE_VIDEO_BYTES = 64 * 1024
# Enough collision suffixes to be useful, few enough to fail fast if something
# is generating files in a loop.
_MAX_FILENAME_ATTEMPTS = 50


async def _save_if_ready(raw: str, save_to) -> str:
    """Download a finished clip and swap the signed URL for a local path.

    The URL is handled here rather than by the model on purpose. It is long and
    percent-encoded, and re-keying it into a shell command dropped characters
    often enough to be the main failure mode of this tool — a corrupted
    signature is rejected, but `curl` still writes the rejection body to the
    output file and exits 0, so it read as success. Passing the exact string
    from this response removes the transcription step entirely.

    It also keeps a bearer credential out of the transcript: the signed URL
    grants the clip to anyone holding it for the next 15-60 minutes, and
    returning it put it in the model's context and the saved conversation. The
    gateway already scrubs presigned URLs for *input* media for exactly this
    reason; this makes the output side match.
    """
    try:
        payload = json.loads(raw)
    except Exception:
        return raw
    if not isinstance(payload, dict):
        return raw

    details = payload.get("details")
    if not isinstance(details, dict) or details.get("status") != "Ready":
        return raw

    result = details.get("result")
    url = result.get("sample") if isinstance(result, dict) else None
    if not isinstance(url, str) or not url.strip():
        return raw

    # Dropped whether or not the save succeeds. A retry re-polls, which mints a
    # fresh URL, so nothing is lost by not handing this one to the model.
    result.pop("sample", None)

    try:
        target, size = await _download_video(url.strip(), save_to)
    except Exception as exc:
        payload["result"] = (
            f"The clip finished but saving it failed: {type(exc).__name__}: {exc}. "
            "Poll this job again to retry the download; the job itself is unaffected."
        )
        return json.dumps(payload, ensure_ascii=False)

    details["saved_path"] = str(target)
    details["saved_bytes"] = size
    payload["result"] = f"Saved to {target}. " + str(payload.get("result") or "")
    return json.dumps(payload, ensure_ascii=False)


async def _download_video(url: str, save_to) -> tuple:
    """Stream the clip to disk, returning (path, bytes).

    SSRF-guarded, for the same reason the upload PUT is: this URL comes from
    the vendor by way of the gateway, and it is fetched from the user's own
    machine. Real result URLs are public CDN objects, which the guard allows.
    """
    import httpx

    from tools.url_safety import create_ssrf_safe_async_client

    target = _resolve_destination(save_to, _filename_from_url(url))
    # Written under a .part name and renamed only once it is complete and
    # plausible, so a failed download can never leave something that looks like
    # a playable file behind.
    partial = target.with_name(target.name + ".part")
    timeout = httpx.Timeout(_DOWNLOAD_CONNECT_TIMEOUT_SECONDS, read=_DOWNLOAD_READ_TIMEOUT_SECONDS)

    try:
        async with create_ssrf_safe_async_client(timeout=timeout, follow_redirects=True) as client:
            async with client.stream("GET", url) as response:
                response.raise_for_status()
                with partial.open("wb") as handle:
                    async for chunk in response.aiter_bytes():
                        handle.write(chunk)

        size = partial.stat().st_size
        if size < _MIN_PLAUSIBLE_VIDEO_BYTES:
            raise ValueError(f"the download returned only {size} bytes, which is not a video")
        partial.replace(target)
    except BaseException:
        partial.unlink(missing_ok=True)
        raise

    return target, size


def _filename_from_url(url: str) -> str:
    from pathlib import PurePosixPath
    from urllib.parse import unquote, urlsplit

    name = PurePosixPath(unquote(urlsplit(url).path)).name
    # The path segment is vendor-controlled, so keep only a plain filename.
    name = re.sub(r"[^A-Za-z0-9._-]", "_", name).lstrip(".")[:120]
    return name or "flux3-video.mp4"


def _resolve_destination(save_to, filename: str):
    """Where to write, honouring an explicit request and never overwriting."""
    from pathlib import Path

    if isinstance(save_to, str) and save_to.strip():
        requested = Path(save_to.strip()).expanduser()
        if requested.is_dir() or save_to.rstrip().endswith(("/", "\\")):
            directory, name = requested, filename
        else:
            directory, name = requested.parent, requested.name
    else:
        downloads = Path.home() / "Downloads"
        directory, name = (downloads if downloads.is_dir() else Path.cwd()), filename

    directory.mkdir(parents=True, exist_ok=True)
    return _free_path(directory / name)


def _free_path(candidate):
    """`name.mp4` -> `name-2.mp4` -> `name-3.mp4` … so nothing is clobbered."""
    if not candidate.exists():
        return candidate
    for suffix in range(2, _MAX_FILENAME_ATTEMPTS + 2):
        sibling = candidate.with_name(f"{candidate.stem}-{suffix}{candidate.suffix}")
        if not sibling.exists():
            return sibling
    raise ValueError(f"could not find a free filename next to {candidate}")


# ---------------------------------------------------------------------------
# Handlers
# ---------------------------------------------------------------------------

def _error(message: str) -> str:
    return json.dumps({"error": message}, ensure_ascii=False)


def _submit_args(mode: str, args: dict) -> dict:
    """The wire body: the model's arguments, minus Nones, plus our mode.

    Arguments pass through as the model gave them; the gateway owns validation
    and the translation onto the vendor's own fields.
    """
    body = {k: v for k, v in dict(args or {}).items() if v is not None}
    body["mode"] = mode
    return body


async def _submit(mode: str, args: dict) -> str:
    endpoints = _endpoints()
    if endpoints is None:
        return _error("BFL video generation is not available in this build.")
    return await _call_gateway("POST", f"{endpoints['base_url']}/generations", _submit_args(mode, args))


async def _handle_text_to_video(args: dict, **kwargs) -> str:
    return await _submit("text_to_video", _without_media(args))


async def _handle_image_to_video(args: dict, **kwargs) -> str:
    try:
        prepared = await _prepare_media(args, kwargs.get("task_id"))
    except ValueError as exc:
        return _error(str(exc))
    return await _submit("image_to_video", prepared)


async def _handle_keyframes_to_video(args: dict, **kwargs) -> str:
    images = (args or {}).get("input_images")
    if not isinstance(images, list) or not images:
        return _error("input_images must be a non-empty list of 1-10 images (local paths or URLs).")
    try:
        prepared = await _prepare_media(args, kwargs.get("task_id"))
    except ValueError as exc:
        return _error(str(exc))
    return await _submit("keyframes_to_video", prepared)


async def _handle_video_continuation(args: dict, **kwargs) -> str:
    try:
        prepared = await _prepare_media(args, kwargs.get("task_id"))
    except ValueError as exc:
        return _error(str(exc))
    return await _submit("video_continuation", prepared)


async def _handle_get_result(args: dict, **kwargs) -> str:
    job_id = (args or {}).get("id")
    if not isinstance(job_id, str) or not job_id.strip():
        return _error("id is required: the job id returned by the generate tool.")
    endpoints = _endpoints()
    if endpoints is None:
        return _error("BFL video generation is not available in this build.")
    from urllib.parse import quote

    raw = await _call_gateway("GET", f"{endpoints['base_url']}/generations/{quote(job_id.strip(), safe='')}")
    return await _save_if_ready(raw, (args or {}).get("save_to"))


async def _handle_prompting_guide(args: dict, **kwargs) -> str:
    return FLUX3_PROMPTING_GUIDE


# ---------------------------------------------------------------------------
# Gating
# ---------------------------------------------------------------------------

def check_bfl_requirements() -> bool:
    try:
        if _endpoints() is None:
            return False
        from hermes_cli.nous_account import get_nous_portal_account_info

        info = get_nous_portal_account_info()
        return bool(getattr(info, "logged_in", False) and getattr(info, "paid_service_access", False))
    except Exception:
        return False


# ---------------------------------------------------------------------------
# Pinned schemas
# ---------------------------------------------------------------------------

_ASPECT_RATIOS = ["auto", "21:9", "16:9", "4:3", "1:1", "3:4", "9:16", "9:21"]
_RESOLUTIONS = ["720p"]

_GUIDE_POINTER = "Read bfl_flux3_prompting_guide before your first generation. "
_MEDIA_SENTENCE = (
    "Media fields accept a local file path (uploaded automatically to Nous-managed temporary "
    "storage and deleted when the generation finishes) or a URL. "
)
_OVERRIDE_SENTENCE = "All guidance is defaults: explicit user instructions override it."


def _shared_submit_properties() -> dict:
    return {
        "prompt": {
            "type": "string",
            "minLength": 1,
            "description": (
                "Generation brief in plain prose (it is interpreted and expanded by a reasoning "
                "harness). Order it subject, distinguishing visual specifics, action, camera, "
                "lighting, environment, audio, style. Audio is generated by default — say "
                '"no music" if unwanted.'
            ),
        },
        "aspect_ratio": {
            "type": "string",
            "enum": list(_ASPECT_RATIOS),
            "default": "auto",
            "description": 'Output aspect ratio. "auto" lets the model choose.',
        },
        "duration": {
            "oneOf": [
                {"type": "integer", "minimum": 5, "maximum": 20},
                {"type": "string", "const": "auto"},
            ],
            "default": "auto",
            "description": 'Clip duration in whole seconds (5-20), or "auto".',
        },
        "resolution": {
            "type": "string",
            "enum": list(_RESOLUTIONS),
            "default": "720p",
            "description": "Output resolution bin.",
        },
        "generate_audio": {
            "type": "boolean",
            "default": True,
            "description": "Generate synchronized audio.",
        },
        "grounding": {
            "type": "boolean",
            "default": True,
            "description": "Allow a short research pass before generation.",
        },
        "seed": {
            "type": "integer",
            "minimum": 0,
            "maximum": 4294967295,
            "description": "Optional reproducibility seed.",
        },
        "version": {
            "type": "string",
            "description": 'Model version pin. Defaults to "latest".',
        },
    }


TEXT_TO_VIDEO_SCHEMA = {
    "name": "bfl_flux3_text_to_video",
    "description": (
        _GUIDE_POINTER
        + "FLUX 3 text-to-video: generates a clip (with audio) from the prompt alone. Nothing but "
        "the prompt anchors the subject here, so research anything with a real, checkable "
        "appearance before writing it — whatever you leave unspecified is filled in for you. "
        "Generation takes several minutes: this returns a job id immediately; poll "
        "bfl_flux3_get_result. "
        + _OVERRIDE_SENTENCE
    ),
    "parameters": {
        "type": "object",
        "properties": _shared_submit_properties(),
        "required": ["prompt"],
        "additionalProperties": False,
    },
}

IMAGE_TO_VIDEO_SCHEMA = {
    "name": "bfl_flux3_image_to_video",
    "description": (
        _GUIDE_POINTER
        + "FLUX 3 image-to-video: animates one image as the literal opening frame — those pixels "
        "are frame 0 and the clip moves from there. "
        + _MEDIA_SENTENCE
        + "Returns a job id; poll bfl_flux3_get_result. "
        + _OVERRIDE_SENTENCE
    ),
    "parameters": {
        "type": "object",
        "properties": {
            **_shared_submit_properties(),
            "input_image": {
                "type": "string",
                "minLength": 1,
                "description": "Exactly one opening-frame image: a local file path or a URL (PNG/JPEG/WebP, up to 10MB).",
            },
        },
        "required": ["prompt", "input_image"],
        "additionalProperties": False,
    },
}

KEYFRAMES_TO_VIDEO_SCHEMA = {
    "name": "bfl_flux3_keyframes_to_video",
    "description": (
        _GUIDE_POINTER
        + "FLUX 3 keyframe video: a storyboard of 1-10 images pinned at chosen frame positions "
        "(24fps). Name the subject and describe the motion that carries it between the pins, and "
        "keep the subject consistent across every pinned image. "
        + _MEDIA_SENTENCE
        + "Returns a job id; poll bfl_flux3_get_result. "
        + _OVERRIDE_SENTENCE
    ),
    "parameters": {
        "type": "object",
        "properties": {
            **_shared_submit_properties(),
            "input_images": {
                "type": "array",
                "items": {"type": "string", "minLength": 1},
                "minItems": 1,
                "maxItems": 10,
                "description": "1-10 keyframe images: local file paths or URLs (PNG/JPEG/WebP, up to 10MB each).",
            },
            "keyframe_indices": {
                "type": "array",
                "items": {"type": "integer", "minimum": 0, "maximum": 480},
                "minItems": 1,
                "maxItems": 10,
                "description": (
                    "One unique non-negative frame index per image (24fps). Each must be at most "
                    'duration×24, so set an explicit duration rather than "auto" whenever you pin '
                    'indices — "auto" resolves to 5, 10, 15 or 20 seconds and an index past the '
                    "length it picks is rejected."
                ),
            },
        },
        "required": ["prompt", "input_images", "keyframe_indices"],
        "additionalProperties": False,
    },
}

VIDEO_CONTINUATION_SCHEMA = {
    "name": "bfl_flux3_video_continuation",
    "description": (
        _GUIDE_POINTER
        + "FLUX 3 video continuation: the new generation picks up from the input clip's final "
        'frames. Open the prompt with "Continue this video from its final frames:", re-establish '
        "the subject and the moment it ended on, then describe what happens next. input_video "
        "must be an mp4 of at most 50MB and 15 seconds, and the generated segment tops out at 15s "
        "too — chain a second continuation for a longer sequence. duration is the "
        "new segment only. "
        + _MEDIA_SENTENCE
        + "Returns a job id; poll bfl_flux3_get_result. "
        + _OVERRIDE_SENTENCE
    ),
    "parameters": {
        "type": "object",
        "properties": {
            **_shared_submit_properties(),
            "input_video": {
                "type": "string",
                "minLength": 1,
                "description": "The clip to continue: a local file path or a URL. mp4 only, at most 50MB and 15 seconds.",
            },
        },
        "required": ["prompt", "input_video"],
        "additionalProperties": False,
    },
}

GET_RESULT_SCHEMA = {
    "name": "bfl_flux3_get_result",
    "description": (
        "Poll a FLUX 3 video job by the job id a generate tool returned. Generation takes minutes "
        "and a long Generating phase is normal. Every response states the job's status, how long "
        "to wait before polling again, and what you may do next — follow it rather than guessing. "
        "On Ready the clip is downloaded for you and the response gives its local path; your only "
        "remaining step is to deliver that file as the response describes."
    ),
    "parameters": {
        "type": "object",
        "properties": {
            "id": {
                "type": "string",
                "minLength": 1,
                "description": "Job id from a previous bfl_flux3_* generate call.",
            },
            "save_to": {
                "type": "string",
                "description": (
                    "Where to save the finished clip: a directory or a full file path. Set this "
                    "only when the user asked for a particular location; the default is "
                    "~/Downloads. An existing file is never overwritten."
                ),
            },
        },
        "required": ["id"],
        "additionalProperties": False,
    },
}

PROMPTING_GUIDE_SCHEMA = {
    "name": "bfl_flux3_prompting_guide",
    "description": (
        "Read this before your first FLUX 3 generation. The prompting and grounding guide: how "
        "to research a subject so it renders as itself, how to assemble a prompt, which generate "
        "tool fits, and how to save and deliver the finished clip. Takes no arguments and spends "
        "no generation budget."
    ),
    "parameters": {"type": "object", "properties": {}, "additionalProperties": False},
}


# ---------------------------------------------------------------------------
# Pinned prompting guide (methodology only — policy numbers such as waits and
# limits arrive live in the gateway's tool responses, so they cannot drift)
# ---------------------------------------------------------------------------

FLUX3_PROMPTING_GUIDE = """# FLUX 3 video generation — how to get the best results

Everything here is guidance, not policy: the user's explicit instructions
always win. If they say skip the research, save somewhere specific, or deliver
differently — do it their way without arguing. Only the server's own validation
and limits are non-negotiable.

Start from the user's own wording. The prompt is rewritten by a reasoning
harness before anything is generated, so restyling it yourself just stacks a
second rewrite on top and gives their intent another chance to drift. There are
two reasons to add anything: grounding facts for a subject with a real,
checkable appearance, and the behaviours below, which they had no way to know
about. Absent those, send what they wrote.

## Grounding

Grounding is your job, and it is the highest-leverage step in the workflow.
What you specify is preserved; what you leave out is filled in for you. So
when a subject has a real, checkable appearance, research it first and put
what you found into the prompt — that is what makes the result yours rather
than an approximation of it.

Ground whenever someone who knows the subject could watch the clip and say
"that's not what that is": a named person or place, a landmark, a particular
vehicle or machine, anything technical, anything culturally specific, or a
period setting. Skip it for generics ("a dog on a beach") — there is no fact
to get wrong.

To ground: search for VISUAL references, where three good photographs beat any
amount of prose. If you can analyze images, analyze what you find. Then put
only what a camera could see into the prompt: silhouette and proportion,
materials and finish, specific colours, distinctive details, era-correct
context.

## How this model behaves

The prompt is read by a reasoning harness rather than a tag encoder, so keyword
tricks and word order do nothing. Write plain prose at whatever length the
brief deserves; a single line is a valid prompt.

Audio is generated by default, whether or not you mention it, so leaving it out
gets you invented sound rather than silence. Name the ambient sound, the music
and any speech separately — each lands as its own layer — and say "no music"
when you do not want it.

A quoted line becomes speech only if a speaker is visible on camera. Without
one it tends to render as burned-in text instead, so quote the line, describe
the speaker, and add "no on-screen text, no subtitles".

Multi-shot sequences work inside a single generation: "SHOT ONE ... HARD CUT.
SHOT TWO ..." produces real cuts, and "one continuous unbroken shot" gets an
uncut take. Consecutive shots have to contrast in scale, location or colour or
the cut will not read as one — near-identical coverage blends back into a
continuous take.

## Choosing a tool

- No input media -> bfl_flux3_text_to_video.
- Animate one image as the literal opening frame ->
  bfl_flux3_image_to_video (those pixels are frame 0, and the clip moves from
  there). Name the subject and its distinguishing specifics here as you would
  anywhere else: only frame 0 is pinned, every frame after it is generated,
  and what you leave out is the model's call rather than yours.
- Several images pinned at chosen moments -> bfl_flux3_keyframes_to_video
  (name the subject and describe the motion that carries it between the pins;
  keep it consistent across every pin, since a mismatch becomes a visible
  morph mid-clip). Pin exact indices and you want an explicit duration too —
  every index must fall within duration×24.
- Keep going from where a clip ends, or chain segments ->
  bfl_flux3_video_continuation. Open with "Continue this video from its final
  frames:", re-establish the subject and the moment the clip ended on, then
  describe what happens next; duration is the new segment only, so plan
  segments of 15 seconds or less to feed outputs back in.

## Media inputs

Pass local file paths directly — never hand-encode file contents into an argument.
URLs also work. Limits per file:
images 10MB (PNG/JPEG/WebP), video one mp4 of at most 50MB and 15 seconds.
Do not pre-shrink files to fit imagined caps; oversized pixel dimensions are
auto-downscaled and output tops out at 720p.

## Workflow

Submit returns a job id immediately — the video does not exist yet. Poll
bfl_flux3_get_result with that id; generation takes several minutes and a long
Generating phase is normal, not a stall. Nothing reaches disk before the job is
Ready, so checking folders mid-run tells you nothing. Every response
states the job status, how long to wait before polling again, and what you may
do next — follow it literally rather than guessing an interval. A job survives
client restarts: re-poll the same id rather than resubmitting, which would only
spend your budgets on duplicate work.

## Save and deliver

bfl_flux3_get_result saves the clip itself and returns saved_path. The download
is not yours to do and no URL is handed to you to fetch. Pass save_to only when
the user named a location; otherwise it lands in ~/Downloads, and an existing
file is never overwritten. If saving fails the response says so — poll the same
job again to retry, which is safe and spends no generation budget.

Then deliver that file so the clip plays inline. Which markup plays inline is the
host's decision, so check your system prompt or platform instructions and use
exactly the form they give; the common ones are a MEDIA: tag alone on its own
line and a markdown embed. Two things break it. Write the real absolute path,
with ~ expanded. And keep the markup plain: wrapping it in bold, backticks or a
code fence, or rewriting it as a [link](path), turns an inline player into
literal text or a click-target. That is the most common way this step fails,
and it reads as success because the filename is on screen. Where the
instructions call for no delivery markup, or the host has no such mechanism,
follow them and state the absolute path in plain text.

Report what you did rather than what the job says it did: the echoed prompt
field describes intent and often overstates what the render preserved.
"""


# ---------------------------------------------------------------------------
# Registration (auto-discovered: top-level registry.register calls)
# ---------------------------------------------------------------------------

registry.register(
    name="bfl_flux3_text_to_video",
    toolset=_TOOLSET,
    schema=TEXT_TO_VIDEO_SCHEMA,
    handler=_handle_text_to_video,
    check_fn=check_bfl_requirements,
    requires_env=[],
    is_async=True,
    emoji="🎬",
)

registry.register(
    name="bfl_flux3_image_to_video",
    toolset=_TOOLSET,
    schema=IMAGE_TO_VIDEO_SCHEMA,
    handler=_handle_image_to_video,
    check_fn=check_bfl_requirements,
    requires_env=[],
    is_async=True,
    emoji="🎬",
)

registry.register(
    name="bfl_flux3_keyframes_to_video",
    toolset=_TOOLSET,
    schema=KEYFRAMES_TO_VIDEO_SCHEMA,
    handler=_handle_keyframes_to_video,
    check_fn=check_bfl_requirements,
    requires_env=[],
    is_async=True,
    emoji="🎬",
)

registry.register(
    name="bfl_flux3_video_continuation",
    toolset=_TOOLSET,
    schema=VIDEO_CONTINUATION_SCHEMA,
    handler=_handle_video_continuation,
    check_fn=check_bfl_requirements,
    requires_env=[],
    is_async=True,
    emoji="🎬",
)

registry.register(
    name="bfl_flux3_get_result",
    toolset=_TOOLSET,
    schema=GET_RESULT_SCHEMA,
    handler=_handle_get_result,
    check_fn=check_bfl_requirements,
    requires_env=[],
    is_async=True,
    emoji="🎬",
)

registry.register(
    name="bfl_flux3_prompting_guide",
    toolset=_TOOLSET,
    schema=PROMPTING_GUIDE_SCHEMA,
    handler=_handle_prompting_guide,
    check_fn=check_bfl_requirements,
    requires_env=[],
    is_async=True,
    emoji="📖",
)