add EventLogger for inference

llama_toolchain/inference/client.py

@@ -13,6 +13,7 @@ from .api import (
     Inference,
     UserMessage,
 )
+from .event_logger import EventLogger
 
 
 class InferenceClient(Inference):
@@ -56,14 +57,15 @@ async def run_main(host: str, port: int):
         messages=[message],
         stream=True,
     )
-    async for event in client.chat_completion(
+    iterator = client.chat_completion(
         ChatCompletionRequest(
             model=InstructModel.llama3_8b_chat,
             messages=[message],
             stream=True,
         )
-    ):
+    )
-        print(event)
+    async for log in EventLogger().log(iterator):
+        log.print()
 
 
 def main(host: str, port: int):

llama_toolchain/inference/event_logger.py

@@ -0,0 +1,33 @@
+
+from termcolor import cprint 
+from llama_toolchain.inference.api import (
+    ChatCompletionResponseEventType,
+)
+
+
+class LogEvent:
+    def __init__(
+        self,
+        content: str = "",
+        end: str = "\n",
+        color="white",
+    ):
+        self.content = content
+        self.color = color
+        self.end = "\n" if end is None else end
+
+    def print(self, flush=True):
+        cprint(f"{self.content}", color=self.color, end=self.end, flush=flush)
+
+
+class EventLogger:
+    async def log(self, event_generator, stream=True):
+        async for chunk in event_generator:
+            event = chunk.event
+            if event.event_type == ChatCompletionResponseEventType.start:
+                yield LogEvent("Assistant> ", color="cyan", end="")
+            elif event.event_type == ChatCompletionResponseEventType.progress:
+                yield LogEvent(event.delta, color="yellow", end="")
+            elif event.event_type == ChatCompletionResponseEventType.complete:
+                yield LogEvent("")
+