Fix a bug in meta-reference inference when stream=False

Also introduce a gross hack (to cover grosser(?) hack) to ensure
non-stream requests don't send back responses in SSE format. Not sure
which of these hacks is grosser.

llama_stack/distribution/server/server.py

@@ -58,13 +58,28 @@ def is_async_iterator_type(typ):
     )
 
 
-def create_sse_event(data: Any) -> str:
+def create_sse_event(data: Any, **kwargs) -> str:
     if isinstance(data, BaseModel):
         data = data.json()
     else:
         data = json.dumps(data)
 
+    # !!FIX THIS ASAP!! grossest hack ever; not really SSE
+    #
+    # we use the return type of the function to determine if there's an AsyncGenerator
+    # and change the implementation to send SSE. unfortunately, chat_completion() takes a
+    # parameter called stream which _changes_ the return type. one correct way to fix this is:
+    #
+    # - have separate underlying functions for streaming and non-streaming because they need
+    #   to operate differently anyhow
+    # - do a late binding of the return type based on the parameters passed in
+    if kwargs.get("stream", False):
         return f"data: {data}\n\n"
+    else:
+        print(
+            f"!!FIX THIS ASAP!! Sending non-SSE event because client really is non-SSE: {data}"
+        )
+        return data
 
 
 async def global_exception_handler(request: Request, exc: Exception):
@@ -226,7 +241,7 @@ def create_dynamic_typed_route(func: Any, method: str):
             async def sse_generator(event_gen):
                 try:
                     async for item in event_gen:
-                        yield create_sse_event(item)
+                        yield create_sse_event(item, **kwargs)
                         await asyncio.sleep(0.01)
                 except asyncio.CancelledError:
                     print("Generator cancelled")

llama_stack/providers/impls/meta_reference/inference/inference.py

@@ -6,7 +6,7 @@
 
 import asyncio
 
-from typing import AsyncIterator, List, Union
+from typing import AsyncGenerator, List
 
 from llama_models.sku_list import resolve_model
 
@@ -58,9 +58,7 @@ class MetaReferenceInferenceImpl(Inference):
         tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
-    ) -> AsyncIterator[
+    ) -> AsyncGenerator:
-        Union[ChatCompletionResponseStreamChunk, ChatCompletionResponse]
-    ]:
         # wrapper request to make it easier to pass around (internal only, not exposed to API)
         request = ChatCompletionRequest(
             model=model,
@@ -117,6 +115,7 @@ class MetaReferenceInferenceImpl(Inference):
 
                 if not ipython and buffer.startswith("<|python_tag|>"):
                     ipython = True
+                    if request.stream:
                         yield ChatCompletionResponseStreamChunk(
                             event=ChatCompletionResponseEvent(
                                 event_type=ChatCompletionResponseEventType.progress,
@@ -126,6 +125,7 @@ class MetaReferenceInferenceImpl(Inference):
                                 ),
                             )
                         )
+
                     buffer = buffer[len("<|python_tag|>") :]
                     continue