Teknium
546 lines
20 KiB
Python
546 lines
20 KiB
Python
"""OpenAI image generation backend — ChatGPT/Codex OAuth variant.
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Same model catalog and tier semantics as the ``openai`` plugin (``gpt-image-2``
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at low/medium/high quality), but routed through the Codex Responses API
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``image_generation`` tool instead of ``images.generate``, so users already
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authenticated with Codex/ChatGPT need no separate ``OPENAI_API_KEY``.
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Tier precedence: ``OPENAI_IMAGE_MODEL`` env → ``image_gen.openai-codex.model``
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→ ``image_gen.model`` (when it's one of our tier IDs) → :data:`DEFAULT_MODEL`.
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Output is saved as PNG under ``$HERMES_HOME/cache/images/``; source images for
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editing are sent as Responses ``input_image`` content parts.
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Do NOT reintroduce an "account capability" classifier keyed on ``Tool choice
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'image_generation' not found in 'tools' parameter``: that HTTP 400 is a
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request-shape rejection emitted for every account (the Codex backend resolves
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tool_choice as a function-tool name), not an entitlement problem. It is fixed
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by omitting tool_choice (see ``_build_responses_payload``); any remaining HTTP
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error must surface verbatim so it stays diagnosable.
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"""
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from __future__ import annotations
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import base64
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import json
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import logging
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import os
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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from agent.image_gen_provider import (
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DEFAULT_ASPECT_RATIO,
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ImageGenProvider,
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resolve_aspect_ratio,
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save_b64_image,
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success_response,
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)
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from plugins.image_gen._common import (
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GPT_IMAGE_2_API_MODEL as API_MODEL,
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GPT_IMAGE_2_DEFAULT as DEFAULT_MODEL,
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GPT_IMAGE_2_TIERS,
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catalog_rows,
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collect_source_images,
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error_factory,
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prompt_required_error,
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resolve_static_model,
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size_for,
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)
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logger = logging.getLogger(__name__)
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_MAX_ERROR_BODY_CHARS = 500
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_MODELS: Dict[str, Dict[str, Any]] = dict(GPT_IMAGE_2_TIERS)
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# Codex Responses surface used for the request. The chat model only hosts the
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# ``image_generation`` tool call; the image work is done by ``API_MODEL``.
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_CODEX_CHAT_MODEL = "gpt-5.5"
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_CODEX_BASE_URL = "https://chatgpt.com/backend-api/codex"
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_CODEX_INSTRUCTIONS = (
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"You are an assistant that must fulfill image generation and image editing "
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"requests by using the image_generation tool when provided."
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)
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_MAX_REFERENCE_IMAGES = 16
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_MAX_INPUT_IMAGE_BYTES = 25 * 1024 * 1024
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# gpt-image-2's ``input_image`` accepts raster formats only. The shared sniffer
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# also recognizes SVG/TIFF/ICO, which the API rejects server-side — gate to this
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# allowlist so unsupported inputs fail locally instead of as an opaque HTTP 400.
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_ACCEPTED_INPUT_MIME = frozenset({"image/png", "image/jpeg", "image/gif", "image/webp"})
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# Progressive preview frames (partial_image_b64) are intermediate renders;
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# saving them as finals produced the "smear" failure mode. Defense in depth:
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# request 0 partials, never let a partial overwrite a final in the extractor,
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# and only deliver source=final from generate(). Live streams may still emit a
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# partial event even with 0 — fine as long as only a final ``result`` is saved.
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_PARTIAL_IMAGES_REQUESTED = 0
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# Content-agnostic retries when the stream yields no final result.
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_NONFINAL_RETRIES = 1
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_NO_AUTH = (
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"No Codex/ChatGPT OAuth credentials available. Run "
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"`hermes auth codex` (or `hermes setup` → Codex) to sign in."
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)
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def _summarize_error_body(body: str) -> str:
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"""Bounded error summary preferring parsed ``error.message``.
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A blind head-truncation of the raw body can cut the actual message off —
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Codex error payloads sometimes carry hundreds of bytes of leading metadata.
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"""
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text = body or ""
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try:
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payload = json.loads(text)
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error = payload.get("error") if isinstance(payload, dict) else None
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message = error.get("message") if isinstance(error, dict) else None
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if isinstance(message, str) and message.strip():
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return message.strip()[:_MAX_ERROR_BODY_CHARS]
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except (TypeError, ValueError):
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pass
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return text[:_MAX_ERROR_BODY_CHARS]
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def _resolve_model() -> Tuple[str, Dict[str, Any]]:
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"""Decide which tier to use and return ``(model_id, meta)``."""
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return resolve_static_model(
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_MODELS, DEFAULT_MODEL, env_var="OPENAI_IMAGE_MODEL", config_key="openai-codex"
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)
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def _read_codex_access_token() -> Optional[str]:
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"""Usable Codex OAuth token or None; the canonical reader in
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``agent.auxiliary_client`` owns expiry, pool selection and JWT decoding."""
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try:
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from agent.auxiliary_client import _read_codex_access_token as _reader
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token = _reader()
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if isinstance(token, str) and token.strip():
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return token.strip()
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return None
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except Exception as exc:
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logger.debug("Could not resolve Codex access token: %s", exc)
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return None
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def _sniff_image_mime(raw: bytes) -> Optional[str]:
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"""Raster MIME from magic bytes (shared sniffer), gated to :data:`_ACCEPTED_INPUT_MIME`."""
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from agent.image_routing import _sniff_mime_from_bytes
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mime = _sniff_mime_from_bytes(raw)
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return mime if mime in _ACCEPTED_INPUT_MIME else None
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def _encode_input_image(raw: bytes, too_big: str, unsupported: str) -> str:
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"""Size- and MIME-check raw image bytes, then return a canonical ``data:`` URL."""
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if len(raw) > _MAX_INPUT_IMAGE_BYTES:
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raise ValueError(too_big)
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mime = _sniff_image_mime(raw)
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if mime is None:
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raise ValueError(unsupported)
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return f"data:{mime};base64,{base64.b64encode(raw).decode('ascii')}"
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def _data_url_to_input_image_url(value: str) -> str:
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"""Validate and canonicalize a data:image URL for Responses input_image."""
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if "," not in value:
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raise ValueError("Image data URL is missing a comma separator")
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header, data = value.split(",", 1)
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header_lc = header.lower()
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if not header_lc.startswith("data:image/") or ";base64" not in header_lc:
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raise ValueError("Only base64 data:image URLs are supported as Codex image inputs")
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return _encode_input_image(
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base64.b64decode(data, validate=True),
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"Image data URL exceeds 25MB cap",
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"Image data URL does not contain supported image bytes",
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)
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def _local_image_to_data_url(value: str) -> str:
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"""Read a local image path and return a validated data:image URL."""
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try:
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from agent.file_safety import get_read_block_error
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blocked = get_read_block_error(value)
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if blocked:
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raise ValueError(blocked)
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except ValueError:
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raise
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except Exception as exc:
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logger.debug("Codex image input read guard unavailable: %s", exc)
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path = Path(os.path.expanduser(value)).resolve()
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if not path.is_file():
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raise ValueError(f"Image input path does not exist or is not a file: {value}")
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if path.stat().st_size <= 0:
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raise ValueError(f"Image input path is empty: {value}")
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return _encode_input_image(
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path.read_bytes(),
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f"Image input path exceeds 25MB cap: {value}",
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f"Image input path is not a supported image: {value}",
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)
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def _to_input_image_part(value: str) -> Dict[str, str]:
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"""Convert a URL/data URL/local path into a Responses input_image part."""
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candidate = (value or "").strip()
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if not candidate:
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raise ValueError("Blank image input")
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lowered = candidate.lower()
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if lowered.startswith(("http://", "https://")):
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image_url = candidate
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elif lowered.startswith("data:"):
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image_url = _data_url_to_input_image_url(candidate)
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else:
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image_url = _local_image_to_data_url(candidate)
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return {"type": "input_image", "image_url": image_url}
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def _normalize_input_images(
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image_url: Optional[str],
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reference_image_urls: Optional[List[str]],
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) -> List[Dict[str, str]]:
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"""Collect primary + reference images as ordered Responses content parts."""
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values = collect_source_images(image_url, reference_image_urls, limit=_MAX_REFERENCE_IMAGES)
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return [_to_input_image_part(value) for value in values]
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def _build_responses_payload(
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*,
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prompt: str,
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size: str,
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quality: str,
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input_images: Optional[List[Dict[str, str]]] = None,
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) -> Dict[str, Any]:
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"""Build the Codex Responses request body for an image_generation call.
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No ``tool_choice`` is sent: the Codex backend rejects every shape for
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forcing the hosted ``image_generation`` tool (it looks tool_choice up as a
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*function* name), so letting the host model decide — nudged by
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``instructions`` — is the only accepted shape.
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"""
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content: List[Dict[str, Any]] = [{"type": "input_text", "text": prompt}]
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if input_images:
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content.extend(input_images)
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return {
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"model": _CODEX_CHAT_MODEL,
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"store": False,
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"instructions": _CODEX_INSTRUCTIONS,
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"input": [{"type": "message", "role": "user", "content": content}],
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"tools": [{
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"type": "image_generation",
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"model": API_MODEL,
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"size": size,
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"quality": quality,
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"output_format": "png",
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"background": "opaque",
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"partial_images": _PARTIAL_IMAGES_REQUESTED,
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}],
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"stream": True,
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}
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def _extract_image_candidates(value: Any) -> Tuple[Optional[str], Optional[str]]:
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"""Return ``(final_result_b64, latest_partial_b64)`` from a payload tree.
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Tracked separately so a partial can never overwrite a genuine final, even
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when both coexist in the same event payload.
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"""
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result_b64: Optional[str] = None
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partial_b64: Optional[str] = None
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def walk(node: Any) -> None:
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nonlocal result_b64, partial_b64
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if isinstance(node, dict):
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if node.get("type") == "image_generation_call":
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result = node.get("result")
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if isinstance(result, str) and result:
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result_b64 = result
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partial = node.get("partial_image_b64")
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if isinstance(partial, str) and partial:
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partial_b64 = partial
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for child in node.values():
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walk(child)
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elif isinstance(node, list):
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for child in node:
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walk(child)
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walk(value)
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return result_b64, partial_b64
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def _extract_image_b64(value: Any) -> Optional[str]:
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"""Image b64 from a payload, preferring a final result over a partial."""
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result_b64, partial_b64 = _extract_image_candidates(value)
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return result_b64 or partial_b64
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def _png_pixel_size(raw: bytes) -> Optional[str]:
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"""Return ``"{w}x{h}"`` for a PNG payload, or None if not a PNG IHDR."""
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import struct
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if len(raw) < 24 or raw[:8] != b"\x89PNG\r\n\x1a\n" or raw[12:16] != b"IHDR":
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return None
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width, height = struct.unpack(">II", raw[16:24])
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return f"{width}x{height}"
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def _iter_sse_json(response: Any):
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"""Yield JSON payloads from an SSE response without OpenAI SDK parsing.
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The Codex backend can emit image-generation events newer than the pinned
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SDK understands; raw SSE parsing stays tolerant of those shape changes.
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"""
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event_name: Optional[str] = None
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data_lines: List[str] = []
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def flush():
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nonlocal event_name, data_lines
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if not data_lines:
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event_name = None
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return None
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raw = "\n".join(data_lines).strip()
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event = event_name
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event_name = None
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data_lines = []
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if not raw or raw == "[DONE]":
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return None
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payload = json.loads(raw)
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if isinstance(payload, dict) and event and "type" not in payload:
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payload["type"] = event
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return payload
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for line in response.iter_lines():
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if isinstance(line, bytes):
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line = line.decode("utf-8", errors="replace")
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line = str(line)
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if line == "":
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payload = flush()
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if payload is not None:
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yield payload
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continue
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if line.startswith(":"):
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continue
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if line.startswith("event:"):
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event_name = line[len("event:"):].strip()
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elif line.startswith("data:"):
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data_lines.append(line[len("data:"):].lstrip())
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payload = flush()
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if payload is not None:
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yield payload
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def _collect_image_b64(
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token: str,
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*,
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prompt: str,
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size: str,
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quality: str,
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input_images: Optional[List[Dict[str, str]]] = None,
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) -> Optional[Dict[str, str]]:
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"""Stream a Codex Responses image_generation call.
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Returns ``{"b64": ..., "source": "final"|"partial"}`` or ``None``. A
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progressive partial is retained only when no final result ever arrives;
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callers must not treat partial-only as success.
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"""
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import httpx
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from agent.codex_headers import codex_cloudflare_headers
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headers = codex_cloudflare_headers(token)
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headers.update({
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"Accept": "text/event-stream",
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"Authorization": f"Bearer {token}",
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"Content-Type": "application/json",
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})
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payload = _build_responses_payload(
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prompt=prompt, size=size, quality=quality, input_images=input_images,
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)
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timeout = httpx.Timeout(300.0, connect=30.0, read=300.0, write=30.0, pool=30.0)
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final_b64: Optional[str] = None
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partial_b64: Optional[str] = None
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with httpx.Client(timeout=timeout, headers=headers) as http:
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with http.stream("POST", f"{_CODEX_BASE_URL}/responses", json=payload) as response:
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try:
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response.raise_for_status()
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except httpx.HTTPStatusError as exc:
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exc.response.read()
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raise RuntimeError(
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f"Codex Responses API returned HTTP {exc.response.status_code}: "
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f"{_summarize_error_body(exc.response.text)}"
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) from exc
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for event in _iter_sse_json(response):
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result_b64, event_partial = _extract_image_candidates(event)
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if result_b64:
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final_b64 = result_b64
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if event_partial:
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partial_b64 = event_partial
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if final_b64:
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return {"b64": final_b64, "source": "final"}
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if partial_b64:
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return {"b64": partial_b64, "source": "partial"}
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return None
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class OpenAICodexImageGenProvider(ImageGenProvider):
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"""gpt-image-2 routed through ChatGPT/Codex OAuth instead of an API key."""
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@property
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def name(self) -> str:
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return "openai-codex"
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@property
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def display_name(self) -> str:
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return "OpenAI (Codex auth)"
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def is_available(self) -> bool:
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if not _read_codex_access_token():
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return False
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try:
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import httpx # noqa: F401
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except ImportError:
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return False
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return True
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def list_models(self) -> List[Dict[str, Any]]:
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return catalog_rows(_MODELS, price="varies")
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def default_model(self) -> Optional[str]:
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return DEFAULT_MODEL
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def get_setup_schema(self) -> Dict[str, Any]:
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return {
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"name": "OpenAI (Codex auth)",
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"badge": "free",
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"tag": "gpt-image-2 via ChatGPT/Codex OAuth — no API key required; supports text and image inputs",
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"env_vars": [],
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"post_setup_hint": (
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"Sign in with `hermes auth codex` (or `hermes setup` → Codex) "
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"if you haven't already. No API key needed."
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),
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}
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def capabilities(self) -> Dict[str, Any]:
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# Source/reference images travel as `input_image` content parts; keep
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# this honest so the dynamic schema encourages identity-preserving edits.
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return {"modalities": ["text", "image"], "max_reference_images": _MAX_REFERENCE_IMAGES}
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def generate(
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self,
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prompt: str,
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aspect_ratio: str = DEFAULT_ASPECT_RATIO,
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*,
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image_url: Optional[str] = None,
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reference_image_urls: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Dict[str, Any]:
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prompt = (prompt or "").strip()
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aspect = resolve_aspect_ratio(aspect_ratio)
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if not prompt:
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return prompt_required_error("openai-codex", aspect)
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token = _read_codex_access_token()
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if not token:
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return error_factory("openai-codex", aspect)(_NO_AUTH, "auth_required")
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try:
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import httpx # noqa: F401
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except ImportError:
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return error_factory("openai-codex", aspect)(
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"httpx Python package not installed (pip install httpx)", "missing_dependency"
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)
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tier_id, meta = _resolve_model()
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size = size_for(aspect)
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fail = error_factory("openai-codex", aspect, model=tier_id, prompt=prompt)
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attempts = _NONFINAL_RETRIES + 1
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try:
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input_images = _normalize_input_images(image_url, reference_image_urls)
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except Exception as exc:
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return fail(f"Invalid image input for Codex image editing: {exc}", "invalid_image_input")
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try:
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collected: Optional[Dict[str, str]] = None
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for attempt in range(attempts):
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collected = _collect_image_b64(
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token, prompt=prompt, size=size, quality=meta["quality"],
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input_images=input_images or None,
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)
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if collected and collected.get("source") == "final" and collected.get("b64"):
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break
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if attempt < _NONFINAL_RETRIES:
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kind = (
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"progressive-only partial frame"
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if collected and collected.get("source") == "partial"
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else "no image_generation_call result"
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)
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logger.warning(
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"Codex image stream ended with %s (attempt %s/%s); "
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"retrying once before failing closed.",
|
|
kind, attempt + 1, attempts,
|
|
)
|
|
except Exception as exc:
|
|
logger.debug("Codex image generation failed", exc_info=True)
|
|
return fail(f"OpenAI image generation via Codex auth failed: {exc}", "api_error")
|
|
|
|
if not collected or not collected.get("b64"):
|
|
return fail(
|
|
f"Codex response contained no image_generation_call result after {attempts} attempt(s)",
|
|
"empty_response",
|
|
)
|
|
|
|
image_source = collected.get("source") or "unknown"
|
|
b64 = collected["b64"]
|
|
|
|
# Never deliver a progressive-only frame as success (smeared previews).
|
|
if image_source != "final":
|
|
try:
|
|
pixel_hint = _png_pixel_size(base64.b64decode(b64, validate=False))
|
|
except Exception:
|
|
pixel_hint = None
|
|
detail = (
|
|
"Codex returned only a progressive partial image frame after "
|
|
f"{attempts} attempt(s); refusing to save it as a final deliverable."
|
|
)
|
|
if pixel_hint:
|
|
detail = f"{detail} partial_pixel_size={pixel_hint}."
|
|
err = fail(detail, "incomplete_image")
|
|
err["image_source"] = image_source
|
|
err["requested_size"] = size
|
|
err["partial_pixel_size"] = pixel_hint
|
|
err["nonfinal_retries"] = _NONFINAL_RETRIES
|
|
return err
|
|
|
|
try:
|
|
pixel_size = _png_pixel_size(base64.b64decode(b64))
|
|
saved_path = save_b64_image(b64, prefix=f"openai_codex_{tier_id}")
|
|
except Exception as exc:
|
|
return fail(f"Could not save image to cache: {exc}", "io_error")
|
|
|
|
return success_response(
|
|
image=str(saved_path),
|
|
model=tier_id,
|
|
prompt=prompt,
|
|
aspect_ratio=aspect,
|
|
provider="openai-codex",
|
|
modality="image" if input_images else "text",
|
|
extra={
|
|
"size": size,
|
|
"quality": meta["quality"],
|
|
"input_image_count": len(input_images),
|
|
"image_source": image_source,
|
|
"requested_size": size,
|
|
"pixel_size": pixel_size,
|
|
},
|
|
)
|
|
|
|
|
|
def register(ctx) -> None:
|
|
"""Plugin entry point — register the Codex-backed image-gen provider."""
|
|
ctx.register_image_gen_provider(OpenAICodexImageGenProvider())
|