Hermes-agent

plugins/image_gen/fal/__init__.py

@@ -0,0 +1,182 @@
+"""FAL.ai image generation backend.
+
+Wraps the 18-model FAL catalog (FLUX 2, Z-Image, Nano Banana, GPT
+Image 1.5, Recraft, Imagen 4, Qwen, Ideogram, …) as an
+:class:`ImageGenProvider` implementation.
+
+The heavy lifting — model catalog, payload construction, request
+submission, managed-Nous-gateway selection, Clarity Upscaler chaining
+— lives in :mod:`tools.image_generation_tool`. This plugin reaches into
+that module via call-time indirection (``import tools.image_generation_tool as _it``)
+so:
+
+* the existing test suite (``tests/tools/test_image_generation.py``,
+  ``tests/tools/test_managed_media_gateways.py``) keeps patching
+  ``image_tool._submit_fal_request`` / ``image_tool.fal_client`` /
+  ``image_tool._managed_fal_client`` without modification, and
+* there's exactly one canonical FAL code path on disk — the plugin is a
+  registration adapter, not a parallel implementation.
+
+See issue #26241 for the migration plan and the
+``plugin-extraction-test-patch-compatibility.md`` rules this follows.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from typing import Any, Dict, List, Optional
+
+from agent.image_gen_provider import (
+    DEFAULT_ASPECT_RATIO,
+    ImageGenProvider,
+    resolve_aspect_ratio,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class FalImageGenProvider(ImageGenProvider):
+    """FAL.ai image generation backend.
+
+    Delegates to ``tools.image_generation_tool.image_generate_tool`` so
+    the in-tree FAL implementation (model catalog, payload builder,
+    managed-gateway selection, Clarity Upscaler chaining) is the single
+    source of truth. Everything is resolved at call time via the
+    ``_it`` indirection so tests can monkey-patch the legacy module.
+    """
+
+    @property
+    def name(self) -> str:
+        return "fal"
+
+    @property
+    def display_name(self) -> str:
+        return "FAL.ai"
+
+    def is_available(self) -> bool:
+        # Available when direct FAL_KEY is set OR the managed Nous
+        # gateway resolves a fal-queue origin. Both checks come from the
+        # legacy module so this provider tracks whatever logic ships
+        # there.
+        import tools.image_generation_tool as _it
+        try:
+            return bool(_it.check_fal_api_key())
+        except Exception:  # noqa: BLE001 — defensive; never break the picker
+            return False
+
+    def list_models(self) -> List[Dict[str, Any]]:
+        import tools.image_generation_tool as _it
+        return [
+            {
+                "id": model_id,
+                "display": meta.get("display", model_id),
+                "speed": meta.get("speed", ""),
+                "strengths": meta.get("strengths", ""),
+                "price": meta.get("price", ""),
+            }
+            for model_id, meta in _it.FAL_MODELS.items()
+        ]
+
+    def default_model(self) -> Optional[str]:
+        import tools.image_generation_tool as _it
+        return _it.DEFAULT_MODEL
+
+    def get_setup_schema(self) -> Dict[str, Any]:
+        return {
+            "name": "FAL.ai",
+            "badge": "paid",
+            "tag": "Pick from flux-2-klein, flux-2-pro, gpt-image, nano-banana, etc.",
+            "env_vars": [
+                {
+                    "key": "FAL_KEY",
+                    "prompt": "FAL API key",
+                    "url": "https://fal.ai/dashboard/keys",
+                },
+            ],
+        }
+
+    def generate(
+        self,
+        prompt: str,
+        aspect_ratio: str = DEFAULT_ASPECT_RATIO,
+        **kwargs: Any,
+    ) -> Dict[str, Any]:
+        """Generate an image via the legacy FAL pipeline.
+
+        Forwards prompt + aspect_ratio (and any forward-compat extras
+        the schema supports) into :func:`tools.image_generation_tool.image_generate_tool`,
+        then reshapes its JSON-string response into the provider-ABC
+        dict format consumed by ``_dispatch_to_plugin_provider``.
+        """
+        import tools.image_generation_tool as _it
+
+        aspect = resolve_aspect_ratio(aspect_ratio)
+        passthrough = {
+            key: kwargs[key]
+            for key in (
+                "num_inference_steps",
+                "guidance_scale",
+                "num_images",
+                "output_format",
+                "seed",
+            )
+            if key in kwargs and kwargs[key] is not None
+        }
+
+        try:
+            raw = _it.image_generate_tool(
+                prompt=prompt,
+                aspect_ratio=aspect,
+                **passthrough,
+            )
+        except Exception as exc:  # noqa: BLE001 — never raise out of generate
+            logger.warning("FAL image_generate_tool raised: %s", exc, exc_info=True)
+            return {
+                "success": False,
+                "image": None,
+                "error": f"FAL image generation failed: {exc}",
+                "error_type": type(exc).__name__,
+                "provider": "fal",
+                "prompt": prompt,
+                "aspect_ratio": aspect,
+            }
+
+        try:
+            response = json.loads(raw) if isinstance(raw, str) else raw
+        except Exception:  # noqa: BLE001
+            response = {"success": False, "image": None, "error": "Invalid JSON from FAL pipeline"}
+
+        if not isinstance(response, dict):
+            response = {
+                "success": False,
+                "image": None,
+                "error": "FAL pipeline returned a non-dict response",
+                "error_type": "provider_contract",
+            }
+
+        # Stamp provider/prompt/aspect_ratio so downstream consumers see
+        # the uniform shape declared in ``agent.image_gen_provider``.
+        response.setdefault("provider", "fal")
+        response.setdefault("prompt", prompt)
+        response.setdefault("aspect_ratio", aspect)
+        # Annotate model best-effort — the legacy pipeline resolves it
+        # internally, so query it after the fact for the response shape.
+        if "model" not in response:
+            try:
+                model_id, _meta = _it._resolve_fal_model()
+                response["model"] = model_id
+            except Exception:  # noqa: BLE001
+                pass
+        return response
+
+
+# ---------------------------------------------------------------------------
+# Plugin entry point
+# ---------------------------------------------------------------------------
+
+
+def register(ctx) -> None:
+    """Plugin entry point — wire ``FalImageGenProvider`` into the registry."""
+    ctx.register_image_gen_provider(FalImageGenProvider())

plugins/image_gen/fal/plugin.yaml

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+name: fal
+version: 1.0.0
+description: "FAL.ai image generation backend (flux-2-klein, flux-2-pro, nano-banana, gpt-image-1.5, recraft-v3, etc.)."
+author: NousResearch
+kind: backend
+requires_env:
+  - FAL_KEY