refactor: add black formatting

litellm/deprecated_litellm_server/main.py

@@ -33,7 +33,7 @@
 # llm_model_list: Optional[list] = None
 # server_settings: Optional[dict] = None
 
-# set_callbacks() # sets litellm callbacks for logging if they exist in the environment 
+# set_callbacks() # sets litellm callbacks for logging if they exist in the environment
 
 # if "CONFIG_FILE_PATH" in os.environ:
 #     llm_router, llm_model_list, server_settings = load_router_config(router=llm_router, config_file_path=os.getenv("CONFIG_FILE_PATH"))
@@ -44,7 +44,7 @@
 # @router.get("/models")  # if project requires model list
 # def model_list():
 #     all_models = litellm.utils.get_valid_models()
-#     if llm_model_list: 
+#     if llm_model_list:
 #         all_models += llm_model_list
 #     return dict(
 #         data=[
@@ -79,8 +79,8 @@
 # @router.post("/v1/embeddings")
 # @router.post("/embeddings")
 # async def embedding(request: Request):
-#     try: 
-#         data = await request.json() 
+#     try:
+#         data = await request.json()
 #         # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers
 #         if os.getenv("AUTH_STRATEGY", None) == "DYNAMIC" and "authorization" in request.headers: # if users pass LLM api keys as part of header
 #             api_key = request.headers.get("authorization")
@@ -106,13 +106,13 @@
 #         data = await request.json()
 #         server_model = server_settings.get("completion_model", None) if server_settings else None
 #         data["model"] = server_model or model or data["model"]
-#         ## CHECK KEYS ## 
+#         ## CHECK KEYS ##
 #         # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers
 #         # env_validation = litellm.validate_environment(model=data["model"])
 #         # if (env_validation['keys_in_environment'] is False or os.getenv("AUTH_STRATEGY", None) == "DYNAMIC") and ("authorization" in request.headers or "api-key" in request.headers): # if users pass LLM api keys as part of header
 #         #     if "authorization" in request.headers:
 #         #         api_key = request.headers.get("authorization")
-#         #     elif "api-key" in request.headers: 
+#         #     elif "api-key" in request.headers:
 #         #         api_key = request.headers.get("api-key")
 #         #     print(f"api_key in headers: {api_key}")
 #         #     if " " in api_key:
@@ -122,11 +122,11 @@
 #         #         api_key = api_key
 #         #         data["api_key"] = api_key
 #         #         print(f"api_key in data: {api_key}")
-#         ## CHECK CONFIG ## 
+#         ## CHECK CONFIG ##
 #         if llm_model_list and data["model"] in [m["model_name"] for m in llm_model_list]:
-#             for m in llm_model_list: 
-#                 if data["model"] == m["model_name"]: 
-#                     for key, value in m["litellm_params"].items(): 
+#             for m in llm_model_list:
+#                 if data["model"] == m["model_name"]:
+#                     for key, value in m["litellm_params"].items():
 #                         data[key] = value
 #                     break
 #         response = litellm.completion(
@@ -145,21 +145,21 @@
 # @router.post("/router/completions")
 # async def router_completion(request: Request):
 #     global llm_router
-#     try: 
+#     try:
 #         data = await request.json()
-#         if "model_list" in data: 
+#         if "model_list" in data:
 #             llm_router = litellm.Router(model_list=data.pop("model_list"))
-#         if llm_router is None: 
+#         if llm_router is None:
 #             raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body")
-        
+
 #         # openai.ChatCompletion.create replacement
-#         response = await llm_router.acompletion(model="gpt-3.5-turbo", 
+#         response = await llm_router.acompletion(model="gpt-3.5-turbo",
 #                         messages=[{"role": "user", "content": "Hey, how's it going?"}])
 
 #         if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
 #                 return StreamingResponse(data_generator(response), media_type='text/event-stream')
 #         return response
-#     except Exception as e: 
+#     except Exception as e:
 #         error_traceback = traceback.format_exc()
 #         error_msg = f"{str(e)}\n\n{error_traceback}"
 #         return {"error": error_msg}
@@ -167,11 +167,11 @@
 # @router.post("/router/embedding")
 # async def router_embedding(request: Request):
 #     global llm_router
-#     try: 
+#     try:
 #         data = await request.json()
-#         if "model_list" in data: 
+#         if "model_list" in data:
 #             llm_router = litellm.Router(model_list=data.pop("model_list"))
-#         if llm_router is None: 
+#         if llm_router is None:
 #             raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body")
 
 #         response = await llm_router.aembedding(model="gpt-3.5-turbo",  # type: ignore
@@ -180,7 +180,7 @@
 #         if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
 #                 return StreamingResponse(data_generator(response), media_type='text/event-stream')
 #         return response
-#     except Exception as e: 
+#     except Exception as e:
 #         error_traceback = traceback.format_exc()
 #         error_msg = f"{str(e)}\n\n{error_traceback}"
 #         return {"error": error_msg}
@@ -190,4 +190,4 @@
 #     return "LiteLLM: RUNNING"
 
 
-# app.include_router(router)
+# app.include_router(router)