forked from Zakaria/hermes-agent
Hermes-agent
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Zakaria
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"""OpenAI image generation backend.
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Exposes OpenAI's ``gpt-image-2`` model at three quality tiers as an
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:class:`ImageGenProvider` implementation. The tiers are implemented as
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three virtual model IDs so the ``hermes tools`` model picker and the
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``image_gen.model`` config key behave like any other multi-model backend:
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gpt-image-2-low ~15s fastest, good for iteration
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gpt-image-2-medium ~40s default — balanced
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gpt-image-2-high ~2min slowest, highest fidelity
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All three hit the same underlying API model (``gpt-image-2``) with a
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different ``quality`` parameter. Output is base64 JSON → saved under
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``$HERMES_HOME/cache/images/``.
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Selection precedence (first hit wins):
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1. ``OPENAI_IMAGE_MODEL`` env var (escape hatch for scripts / tests)
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2. ``image_gen.openai.model`` in ``config.yaml``
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3. ``image_gen.model`` in ``config.yaml`` (when it's one of our tier IDs)
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4. :data:`DEFAULT_MODEL` — ``gpt-image-2-medium``
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"""
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from __future__ import annotations
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import logging
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import os
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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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error_response,
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resolve_aspect_ratio,
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save_b64_image,
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save_url_image,
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success_response,
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)
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Model catalog
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# ---------------------------------------------------------------------------
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#
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# All three IDs resolve to the same underlying API model with a different
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# ``quality`` setting. ``api_model`` is what gets sent to OpenAI;
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# ``quality`` is the knob that changes generation time and output fidelity.
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API_MODEL = "gpt-image-2"
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_MODELS: Dict[str, Dict[str, Any]] = {
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"gpt-image-2-low": {
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"display": "GPT Image 2 (Low)",
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"speed": "~15s",
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"strengths": "Fast iteration, lowest cost",
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"quality": "low",
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},
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"gpt-image-2-medium": {
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"display": "GPT Image 2 (Medium)",
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"speed": "~40s",
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"strengths": "Balanced — default",
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"quality": "medium",
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},
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"gpt-image-2-high": {
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"display": "GPT Image 2 (High)",
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"speed": "~2min",
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"strengths": "Highest fidelity, strongest prompt adherence",
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"quality": "high",
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},
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}
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DEFAULT_MODEL = "gpt-image-2-medium"
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_SIZES = {
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"landscape": "1536x1024",
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"square": "1024x1024",
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"portrait": "1024x1536",
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}
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def _load_openai_config() -> Dict[str, Any]:
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"""Read ``image_gen`` from config.yaml (returns {} on any failure)."""
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try:
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from hermes_cli.config import load_config
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cfg = load_config()
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section = cfg.get("image_gen") if isinstance(cfg, dict) else None
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return section if isinstance(section, dict) else {}
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except Exception as exc:
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logger.debug("Could not load image_gen config: %s", exc)
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return {}
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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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env_override = os.environ.get("OPENAI_IMAGE_MODEL")
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if env_override and env_override in _MODELS:
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return env_override, _MODELS[env_override]
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cfg = _load_openai_config()
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openai_cfg = cfg.get("openai") if isinstance(cfg.get("openai"), dict) else {}
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candidate: Optional[str] = None
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if isinstance(openai_cfg, dict):
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value = openai_cfg.get("model")
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if isinstance(value, str) and value in _MODELS:
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candidate = value
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if candidate is None:
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top = cfg.get("model")
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if isinstance(top, str) and top in _MODELS:
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candidate = top
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if candidate is not None:
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return candidate, _MODELS[candidate]
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return DEFAULT_MODEL, _MODELS[DEFAULT_MODEL]
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# ---------------------------------------------------------------------------
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# Provider
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# ---------------------------------------------------------------------------
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class OpenAIImageGenProvider(ImageGenProvider):
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"""OpenAI ``images.generate`` backend — gpt-image-2 at low/medium/high."""
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@property
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def name(self) -> str:
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return "openai"
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@property
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def display_name(self) -> str:
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return "OpenAI"
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def is_available(self) -> bool:
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if not os.environ.get("OPENAI_API_KEY"):
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return False
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try:
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import openai # 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 [
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{
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"id": model_id,
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"display": meta["display"],
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"speed": meta["speed"],
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"strengths": meta["strengths"],
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"price": "varies",
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}
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for model_id, meta in _MODELS.items()
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]
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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",
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"badge": "paid",
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"tag": "gpt-image-2 at low/medium/high quality tiers",
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"env_vars": [
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{
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"key": "OPENAI_API_KEY",
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"prompt": "OpenAI API key",
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"url": "https://platform.openai.com/api-keys",
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},
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],
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}
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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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**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 error_response(
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error="Prompt is required and must be a non-empty string",
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error_type="invalid_argument",
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provider="openai",
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aspect_ratio=aspect,
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)
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if not os.environ.get("OPENAI_API_KEY"):
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return error_response(
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error=(
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"OPENAI_API_KEY not set. Run `hermes tools` → Image "
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"Generation → OpenAI to configure, or `hermes setup` "
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"to add the key."
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),
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error_type="auth_required",
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provider="openai",
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aspect_ratio=aspect,
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)
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try:
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import openai
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except ImportError:
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return error_response(
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error="openai Python package not installed (pip install openai)",
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error_type="missing_dependency",
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provider="openai",
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aspect_ratio=aspect,
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)
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tier_id, meta = _resolve_model()
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size = _SIZES.get(aspect, _SIZES["square"])
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# gpt-image-2 returns b64_json unconditionally and REJECTS
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# ``response_format`` as an unknown parameter. Don't send it.
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payload: Dict[str, Any] = {
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"model": API_MODEL,
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"prompt": prompt,
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"size": size,
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"n": 1,
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"quality": meta["quality"],
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}
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try:
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client = openai.OpenAI()
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response = client.images.generate(**payload)
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except Exception as exc:
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logger.debug("OpenAI image generation failed", exc_info=True)
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return error_response(
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error=f"OpenAI image generation failed: {exc}",
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error_type="api_error",
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provider="openai",
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model=tier_id,
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prompt=prompt,
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aspect_ratio=aspect,
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)
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data = getattr(response, "data", None) or []
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if not data:
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return error_response(
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error="OpenAI returned no image data",
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error_type="empty_response",
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provider="openai",
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model=tier_id,
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prompt=prompt,
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aspect_ratio=aspect,
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)
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first = data[0]
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b64 = getattr(first, "b64_json", None)
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url = getattr(first, "url", None)
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revised_prompt = getattr(first, "revised_prompt", None)
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if b64:
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try:
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saved_path = save_b64_image(b64, prefix=f"openai_{tier_id}")
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except Exception as exc:
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return error_response(
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error=f"Could not save image to cache: {exc}",
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error_type="io_error",
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provider="openai",
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model=tier_id,
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prompt=prompt,
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aspect_ratio=aspect,
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)
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image_ref = str(saved_path)
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elif url:
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# Defensive — gpt-image-2 returns b64 today, but OpenAI's API
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# has previously returned URLs. Cache the bytes locally so the
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# gateway never tries to fetch an ephemeral / signed URL after
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# it expires — same rationale as the xAI provider (#26942).
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try:
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saved_path = save_url_image(url, prefix=f"openai_{tier_id}")
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except Exception as exc:
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logger.warning(
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"OpenAI image URL %s could not be cached (%s); falling back to bare URL.",
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url,
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exc,
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)
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image_ref = url
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else:
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image_ref = str(saved_path)
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else:
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return error_response(
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error="OpenAI response contained neither b64_json nor URL",
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error_type="empty_response",
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provider="openai",
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model=tier_id,
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prompt=prompt,
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aspect_ratio=aspect,
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)
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extra: Dict[str, Any] = {"size": size, "quality": meta["quality"]}
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if revised_prompt:
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extra["revised_prompt"] = revised_prompt
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return success_response(
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image=image_ref,
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model=tier_id,
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prompt=prompt,
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aspect_ratio=aspect,
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provider="openai",
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extra=extra,
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)
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# ---------------------------------------------------------------------------
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# Plugin entry point
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# ---------------------------------------------------------------------------
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def register(ctx) -> None:
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"""Plugin entry point — wire ``OpenAIImageGenProvider`` into the registry."""
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ctx.register_image_gen_provider(OpenAIImageGenProvider())
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