OpenAPI revisions for compatibility with Fern SDK generator

daras_ai_v2/asr.py

@@ -3,7 +3,6 @@
 import os.path
 import tempfile
 import typing
-from enum import Enum
 
 import gooey_gui as gui
 import requests
@@ -14,6 +13,7 @@
 from daras_ai.image_input import upload_file_from_bytes, gs_url_to_uri
 from daras_ai_v2 import settings
 from daras_ai_v2.azure_asr import azure_asr
+from daras_ai_v2.custom_enum import GooeyEnum
 from daras_ai_v2.exceptions import (
     raise_for_status,
     UserError,
@@ -204,7 +204,7 @@
 GHANA_NLP_MAXLEN = 500
 
 
-class AsrModels(Enum):
+class AsrModels(GooeyEnum):
     whisper_large_v2 = "Whisper Large v2 (openai)"
     whisper_large_v3 = "Whisper Large v3 (openai)"
     whisper_hindi_large_v2 = "Whisper Hindi Large v2 (Bhashini)"
@@ -277,7 +277,7 @@ class AsrOutputJson(typing_extensions.TypedDict):
     chunks: typing_extensions.NotRequired[list[AsrChunk]]
 
 
-class AsrOutputFormat(Enum):
+class AsrOutputFormat(GooeyEnum):
     text = "Text"
     json = "JSON"
     srt = "SRT"
@@ -290,7 +290,7 @@ class TranslationModel(typing.NamedTuple):
     supports_auto_detect: bool = False
 
 
-class TranslationModels(TranslationModel, Enum):
+class TranslationModels(TranslationModel, GooeyEnum):
     google = TranslationModel(
         label="Google Translate",
         supports_glossary=True,

daras_ai_v2/base.py

@@ -127,6 +127,7 @@ class BasePage:
     title: str
     workflow: Workflow
     slug_versions: list[str]
+    sdk_method_name: str
 
     sane_defaults: dict = {}
 
@@ -144,6 +145,9 @@ class BasePage:
     )
 
     class RequestModel(BaseModel):
+        class Config:
+            use_enum_values = True
+
         functions: list[RecipeFunction] | None = Field(
             title="🧩 Developer Tools and Functions",
         )
@@ -317,6 +321,12 @@ def sentry_event_set_user(self, event, hint):
             }
         return event
 
+    @classmethod
+    def get_openapi_extra(cls) -> dict[str, typing.Any]:
+        return {
+            "x-fern-sdk-method-name": cls.sdk_method_name,
+        }
+
     def refresh_state(self):
         sr = self.current_sr
         channel = self.realtime_channel_name(sr.run_id, sr.uid)

daras_ai_v2/custom_enum.py

@@ -1,12 +1,38 @@
+import functools
 import typing
 from enum import Enum
 
 import typing_extensions
 
+
+def cached_classmethod(func: typing.Callable):
+    """
+    This cache is a hack to get around a bug where
+    dynamic Enums with the same name cause a crash
+    when generating the OpenAPI spec.
+    """
+
+    @functools.wraps(func)
+    def wrapper(cls):
+        if not hasattr(cls, "_cached_classmethod"):
+            cls._cached_classmethod = {}
+        if id(func) not in cls._cached_classmethod:
+            cls._cached_classmethod[id(func)] = func(cls)
+
+        return cls._cached_classmethod[id(func)]
+
+    return wrapper
+
+
 T = typing.TypeVar("T", bound="GooeyEnum")
 
 
 class GooeyEnum(Enum):
+    @property
+    def api_value(self):
+        # api_value is usually the name
+        return self.name
+
     @classmethod
     def db_choices(cls):
         return [(e.db_value, e.label) for e in cls]
@@ -20,12 +46,23 @@ def from_db(cls, db_value) -> typing_extensions.Self:
 
     @classmethod
     @property
-    def api_choices(cls):
-        return typing.Literal[tuple(e.name for e in cls)]
+    @cached_classmethod
+    def api_enum(cls):
+        """
+        Enum that is useful as a type in API requests.
+
+        Maps `api_value`->`api_value` (default: `name`->`name`)
+        for all values.
+
+        The title (same as the Enum class name) will be
+        used as the new Enum's title. This will be passed
+        on to the OpenAPI schema and the generated SDK.
+        """
+        return Enum(cls.__name__, {e.api_value: e.api_value for e in cls})
 
     @classmethod
-    def from_api(cls, name: str) -> typing_extensions.Self:
+    def from_api(cls, api_value: str) -> typing_extensions.Self:
         for e in cls:
-            if e.name == name:
+            if e.api_value == api_value:
                 return e
-        raise ValueError(f"Invalid {cls.__name__} {name=}")
+        raise ValueError(f"Invalid {cls.__name__} {api_value=}")

daras_ai_v2/embedding_model.py

@@ -1,7 +1,6 @@
 import hashlib
 import io
 import typing
-from enum import Enum
 from functools import partial
 
 import numpy as np
@@ -13,6 +12,7 @@
 from jinja2.lexer import whitespace_re
 from loguru import logger
 
+from daras_ai_v2.custom_enum import GooeyEnum
 from daras_ai_v2.gpu_server import call_celery_task
 from daras_ai_v2.language_model import get_openai_client
 from daras_ai_v2.redis_cache import (
@@ -25,7 +25,7 @@ class EmbeddingModel(typing.NamedTuple):
     label: str
 
 
-class EmbeddingModels(Enum):
+class EmbeddingModels(EmbeddingModel, GooeyEnum):
     openai_3_large = EmbeddingModel(
         model_id=("openai-text-embedding-3-large-prod-ca-1", "text-embedding-3-large"),
         label="Text Embedding 3 Large (OpenAI)",
@@ -65,12 +65,8 @@ class EmbeddingModels(Enum):
     )
 
     @property
-    def model_id(self) -> typing.Iterable[str] | str:
-        return self.value.model_id
-
-    @property
-    def label(self) -> str:
-        return self.value.label
+    def db_value(self):
+        return self.name
 
     @classmethod
     def get(cls, key, default=None):

daras_ai_v2/image_segmentation.py

@@ -1,14 +1,13 @@
-from enum import Enum
-
 import requests
 
+from daras_ai_v2.custom_enum import GooeyEnum
 from daras_ai_v2.exceptions import raise_for_status
 from daras_ai_v2.gpu_server import (
     call_celery_task_outfile,
 )
 
 
-class ImageSegmentationModels(Enum):
+class ImageSegmentationModels(str, GooeyEnum):
     dis = "Dichotomous Image Segmentation"
     u2net = "U²-Net"
 

daras_ai_v2/img_model_settings_widgets.py

@@ -2,11 +2,10 @@
 
 from daras_ai_v2.enum_selector_widget import enum_selector, enum_multiselect
 from daras_ai_v2.stable_diffusion import (
-    Text2ImgModels,
+    TextToImageModels,
     InpaintingModels,
-    Img2ImgModels,
+    ImageToImageModels,
     ControlNetModels,
-    controlnet_model_explanations,
     Schedulers,
 )
 
@@ -37,10 +36,10 @@ def img_model_settings(
     negative_prompt_setting(selected_model)
 
     num_outputs_setting(selected_model)
-    if models_enum is not Img2ImgModels:
+    if models_enum is not ImageToImageModels:
         output_resolution_setting()
 
-    if models_enum is Text2ImgModels:
+    if models_enum is TextToImageModels:
         sd_2_upscaling_setting()
 
     col1, col2 = gui.columns(2)
@@ -49,11 +48,11 @@ def img_model_settings(
         guidance_scale_setting(selected_model)
 
     with col2:
-        if models_enum is Img2ImgModels and not gui.session_state.get(
+        if models_enum is ImageToImageModels and not gui.session_state.get(
             "selected_controlnet_model"
         ):
             prompt_strength_setting(selected_model)
-        if selected_model == Img2ImgModels.instruct_pix2pix.name:
+        if selected_model == ImageToImageModels.instruct_pix2pix.name:
             instruct_pix2pix_settings()
 
     if show_scheduler:
@@ -73,10 +72,10 @@ def model_selector(
     high_explanation: str = "At {high} the control nets will be applied tightly to the prompted visual, possibly overriding the prompt",
 ):
     controlnet_unsupported_models = [
-        Img2ImgModels.instruct_pix2pix.name,
-        Img2ImgModels.dall_e.name,
-        Img2ImgModels.jack_qiao.name,
-        Img2ImgModels.sd_2.name,
+        ImageToImageModels.instruct_pix2pix.name,
+        ImageToImageModels.dall_e.name,
+        ImageToImageModels.jack_qiao.name,
+        ImageToImageModels.sd_2.name,
     ]
     col1, col2 = gui.columns(2)
     with col1:
@@ -96,12 +95,12 @@ def model_selector(
             """
         )
         if (
-            models_enum is Img2ImgModels
+            models_enum is ImageToImageModels
             and gui.session_state.get("selected_model") in controlnet_unsupported_models
         ):
             if "selected_controlnet_model" in gui.session_state:
                 gui.session_state["selected_controlnet_model"] = None
-        elif models_enum is Img2ImgModels:
+        elif models_enum is ImageToImageModels:
             enum_multiselect(
                 ControlNetModels,
                 label=controlnet_explanation,
@@ -130,9 +129,7 @@ def controlnet_settings(
     if not models:
         return
 
-    if extra_explanations is None:
-        extra_explanations = {}
-    explanations = controlnet_model_explanations | extra_explanations
+    extra_explanations = extra_explanations or {}
 
     state_values = gui.session_state.get("controlnet_conditioning_scale", [])
     new_values = []
@@ -157,7 +154,9 @@ def controlnet_settings(
             pass
         new_values.append(
             controlnet_weight_setting(
-                selected_controlnet_model=model, explanations=explanations, key=key
+                selected_controlnet_model=model,
+                extra_explanations=extra_explanations,
+                key=key,
             ),
         )
     gui.session_state["controlnet_conditioning_scale"] = new_values
@@ -166,13 +165,13 @@ def controlnet_settings(
 def controlnet_weight_setting(
     *,
     selected_controlnet_model: str,
-    explanations: dict[ControlNetModels, str],
+    extra_explanations: dict[ControlNetModels, str],
     key: str = "controlnet_conditioning_scale",
 ):
     model = ControlNetModels[selected_controlnet_model]
     return gui.slider(
         label=f"""
-        {explanations[model]}.
+        {extra_explanations.get(model, model.explanation)}.
         """,
         key=key,
         min_value=CONTROLNET_CONDITIONING_SCALE_RANGE[0],
@@ -215,7 +214,7 @@ def quality_setting(selected_models=None):
         return
     if any(
         [
-            selected_model in [Text2ImgModels.dall_e_3.name]
+            selected_model in [TextToImageModels.dall_e_3.name]
             for selected_model in selected_models
         ]
     ):
@@ -375,8 +374,8 @@ def sd_2_upscaling_setting():
 
 def scheduler_setting(selected_model: str = None):
     if selected_model in [
-        Text2ImgModels.dall_e.name,
-        Text2ImgModels.jack_qiao,
+        TextToImageModels.dall_e.name,
+        TextToImageModels.jack_qiao,
     ]:
         return
     enum_selector(
@@ -395,8 +394,8 @@ def scheduler_setting(selected_model: str = None):
 
 def guidance_scale_setting(selected_model: str = None):
     if selected_model in [
-        Text2ImgModels.dall_e.name,
-        Text2ImgModels.jack_qiao,
+        TextToImageModels.dall_e.name,
+        TextToImageModels.jack_qiao,
     ]:
         return
     gui.slider(
@@ -435,8 +434,8 @@ def instruct_pix2pix_settings():
 
 def prompt_strength_setting(selected_model: str = None):
     if selected_model in [
-        Img2ImgModels.dall_e.name,
-        Img2ImgModels.instruct_pix2pix.name,
+        ImageToImageModels.dall_e.name,
+        ImageToImageModels.instruct_pix2pix.name,
     ]:
         return
 
@@ -458,7 +457,7 @@ def prompt_strength_setting(selected_model: str = None):
 
 
 def negative_prompt_setting(selected_model: str = None):
-    if selected_model in [Text2ImgModels.dall_e.name]:
+    if selected_model in [TextToImageModels.dall_e.name]:
         return
 
     gui.text_area(

daras_ai_v2/language_model.py

@@ -25,6 +25,7 @@
 
 from daras_ai.image_input import gs_url_to_uri, bytes_to_cv2_img, cv2_img_to_bytes
 from daras_ai_v2.asr import get_google_auth_session
+from daras_ai_v2.custom_enum import GooeyEnum
 from daras_ai_v2.exceptions import raise_for_status, UserError
 from daras_ai_v2.gpu_server import call_celery_task
 from daras_ai_v2.text_splitter import (
@@ -72,7 +73,7 @@ class LLMSpec(typing.NamedTuple):
     supports_json: bool = False
 
 
-class LargeLanguageModels(Enum):
+class LargeLanguageModels(GooeyEnum):
     # https://platform.openai.com/docs/models/gpt-4o
     gpt_4_o = LLMSpec(
         label="GPT-4o (openai)",
@@ -474,7 +475,9 @@ def get_entry_text(entry: ConversationEntry) -> str:
     )
 
 
-ResponseFormatType = typing.Literal["text", "json_object"]
+class ResponseFormatType(str, GooeyEnum):
+    text = "text"
+    json_object = "json_object"
 
 
 def run_language_model(