Add ai speech

src/marvin/components/ai_image.py

@@ -1,5 +1,4 @@
 import asyncio
-import inspect
 from functools import partial, wraps
 from typing import (
     Any,
@@ -20,20 +19,14 @@
 
 from marvin.client.openai import AsyncMarvinClient, MarvinClient
 from marvin.components.prompt.fn import PromptFunction
+from marvin.prompts.images import IMAGE_PROMPT
 from marvin.utilities.jinja import (
     BaseEnvironment,
 )
 
 T = TypeVar("T")
 P = ParamSpec("P")
 
-DEFAULT_PROMPT = inspect.cleandoc(
-    """
-    {{_doc}}
-    {{_return_value}}
-    """
-)
-
 
 class AIImageKwargs(TypedDict):
     environment: NotRequired[BaseEnvironment]
@@ -45,7 +38,7 @@ class AIImageKwargs(TypedDict):
 class AIImageKwargsDefaults(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())
     environment: Optional[BaseEnvironment] = None
-    prompt: Optional[str] = DEFAULT_PROMPT
+    prompt: Optional[str] = IMAGE_PROMPT
     client: Optional[Client] = None
     aclient: Optional[AsyncClient] = None
 
@@ -54,7 +47,7 @@ class AIImage(BaseModel, Generic[P]):
     model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())
     fn: Optional[Callable[P, Any]] = None
     environment: Optional[BaseEnvironment] = None
-    prompt: Optional[str] = Field(default=DEFAULT_PROMPT)
+    prompt: Optional[str] = Field(default=IMAGE_PROMPT)
     client: Client = Field(default_factory=lambda: MarvinClient().client)
     aclient: AsyncClient = Field(default_factory=lambda: AsyncMarvinClient().client)
 

src/marvin/components/ai_speech.py

@@ -0,0 +1,145 @@
+import asyncio
+from functools import partial, wraps
+from typing import (
+    Any,
+    Callable,
+    Coroutine,
+    Generic,
+    Optional,
+    TypedDict,
+    TypeVar,
+    Union,
+    overload,
+)
+
+from openai import AsyncClient, Client
+from openai._base_client import HttpxBinaryResponseContent as AudioResponse
+from pydantic import BaseModel, ConfigDict, Field
+from typing_extensions import NotRequired, ParamSpec, Self, Unpack
+
+from marvin.client.openai import AsyncMarvinClient, MarvinClient
+from marvin.components.prompt.fn import PromptFunction
+from marvin.prompts.speech import SPEECH_PROMPT
+from marvin.utilities.jinja import (
+    BaseEnvironment,
+)
+
+T = TypeVar("T")
+P = ParamSpec("P")
+
+
+class AISpeechKwargs(TypedDict):
+    environment: NotRequired[BaseEnvironment]
+    prompt: NotRequired[str]
+    client: NotRequired[Client]
+    aclient: NotRequired[AsyncClient]
+
+
+class AISpeechKwargsDefaults(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())
+    environment: Optional[BaseEnvironment] = None
+    prompt: Optional[str] = SPEECH_PROMPT
+    client: Optional[Client] = None
+    aclient: Optional[AsyncClient] = None
+
+
+class AISpeech(BaseModel, Generic[P]):
+    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())
+    fn: Optional[Callable[P, Any]] = None
+    environment: Optional[BaseEnvironment] = None
+    prompt: Optional[str] = Field(default=SPEECH_PROMPT)
+    client: Client = Field(default_factory=lambda: MarvinClient().client)
+    aclient: AsyncClient = Field(default_factory=lambda: AsyncMarvinClient().client)
+
+    def __call__(
+        self, *args: P.args, **kwargs: P.kwargs
+    ) -> Union[AudioResponse, Coroutine[Any, Any, AudioResponse]]:
+        if asyncio.iscoroutinefunction(self.fn):
+            return self.acall(*args, **kwargs)
+        return self.call(*args, **kwargs)
+
+    def call(self, *args: P.args, **kwargs: P.kwargs) -> AudioResponse:
+        prompt_str = self.as_prompt(*args, **kwargs)
+        return MarvinClient(client=self.client).speak(input=prompt_str)
+
+    async def acall(self, *args: P.args, **kwargs: P.kwargs) -> AudioResponse:
+        prompt_str = self.as_prompt(*args, **kwargs)
+        return await AsyncMarvinClient(client=self.aclient).speak(input=prompt_str)
+
+    def as_prompt(
+        self,
+        *args: P.args,
+        **kwargs: P.kwargs,
+    ) -> str:
+        tool_call = PromptFunction[BaseModel].as_tool_call(
+            fn=self.fn,
+            environment=self.environment,
+            prompt=self.prompt,
+        )
+        return tool_call(*args, **kwargs).messages[0].content
+
+    @overload
+    @classmethod
+    def as_decorator(
+        cls: type[Self],
+        **kwargs: Unpack[AISpeechKwargs],
+    ) -> Callable[P, Self]:
+        pass
+
+    @overload
+    @classmethod
+    def as_decorator(
+        cls: type[Self],
+        fn: Callable[P, Any],
+        **kwargs: Unpack[AISpeechKwargs],
+    ) -> Self:
+        pass
+
+    @classmethod
+    def as_decorator(
+        cls: type[Self],
+        fn: Optional[Callable[P, Any]] = None,
+        **kwargs: Unpack[AISpeechKwargs],
+    ) -> Union[Self, Callable[[Callable[P, Any]], Self]]:
+        passed_kwargs: dict[str, Any] = {
+            k: v for k, v in kwargs.items() if v is not None
+        }
+        if fn is None:
+            return partial(
+                cls,
+                **passed_kwargs,
+            )
+
+        return cls(
+            fn=fn,
+            **passed_kwargs,
+        )
+
+
+def ai_speech(
+    fn: Optional[Callable[P, Any]] = None,
+    **kwargs: Unpack[AISpeechKwargs],
+) -> Union[
+    Callable[
+        [Callable[P, Any]],
+        Callable[P, Union[AudioResponse, Coroutine[Any, Any, AudioResponse]]],
+    ],
+    Callable[P, Union[AudioResponse, Coroutine[Any, Any, AudioResponse]]],
+]:
+    def wrapper(
+        func: Callable[P, Any], *args_: P.args, **kwargs_: P.kwargs
+    ) -> Union[AudioResponse, Coroutine[Any, Any, AudioResponse]]:
+        f = AISpeech[P].as_decorator(
+            func, **AISpeechKwargsDefaults(**kwargs).model_dump(exclude_none=True)
+        )
+        return f(*args_, **kwargs_)
+
+    if fn is not None:
+        return wraps(fn)(partial(wrapper, fn))
+
+    def decorator(
+        fn: Callable[P, Any],
+    ) -> Callable[P, Union[AudioResponse, Coroutine[Any, Any, AudioResponse]]]:
+        return wraps(fn)(partial(wrapper, fn))
+
+    return decorator

src/marvin/prompts/images.py

@@ -0,0 +1,8 @@
+import inspect
+
+IMAGE_PROMPT = inspect.cleandoc(
+    """
+    {{_doc | default("", true)}}
+    {{_return_value | default("", true)}}
+    """
+)

src/marvin/prompts/speech.py

@@ -0,0 +1,8 @@
+import inspect
+
+SPEECH_PROMPT = inspect.cleandoc(
+    """
+    {{_doc | default("", true)}}
+    {{_return_value | default("", true)}}
+    """
+)

src/marvin/utilities/jinja.py

@@ -186,7 +186,7 @@ def render_to_messages(
         **kwargs: Any,
     ) -> list[Message]:
         pairs = split_text_by_tokens(
-            text=self.render(**kwargs),
+            text=self.render(**kwargs).strip(),
             split_tokens=[f"\n{role}" for role in self.roles.keys()],
         )
         return [