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refactor(all-files): removing all print statements; adding pre-commit + flake8 to prevent future regressions

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This commit is contained in:
Krrish Dholakia 2023-11-04 12:50:15 -07:00
parent 38ff9f2b6f
commit 6b40546e59
9 changed files with 39 additions and 50 deletions
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39
litellm/utils.py
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@ -285,7 +285,8 @@ class TextCompletionResponse(OpenAIObject):
############################################################
def print_verbose(print_statement):
if litellm.set_verbose:
print(f"LiteLLM: {print_statement}")
import logging
logging.info(f"LiteLLM: {print_statement}")
####### LOGGING ###################
from enum import Enum
@ -538,8 +539,6 @@ class Logging:
print_verbose("reaches api manager for updating model cost")
litellm.apiManager.update_cost(completion_obj=result, user=self.user)
if callback == "cache":
# print("entering logger first time")
# print(self.litellm_params["stream_response"])
if litellm.cache != None and self.model_call_details.get('optional_params', {}).get('stream', False) == True:
litellm_call_id = self.litellm_params["litellm_call_id"]
if litellm_call_id in self.litellm_params["stream_response"]:
@ -550,10 +549,7 @@ class Logging:
self.litellm_params["stream_response"][litellm_call_id]["choices"][0]["message"]["content"] += result["content"]
else: # init a streaming response for this call id
new_model_response = ModelResponse(choices=[Choices(message=Message(content="default"))])
#print("creating new model response")
#print(new_model_response)
self.litellm_params["stream_response"][litellm_call_id] = new_model_response
#print("adding to cache for", litellm_call_id)
litellm.cache.add_cache(self.litellm_params["stream_response"][litellm_call_id], **self.model_call_details)
if callback == "promptlayer":
print_verbose("reaches promptlayer for logging!")
@ -576,7 +572,6 @@ class Logging:
print_verbose("reaches supabase for streaming logging!")
result = kwargs["complete_streaming_response"]
# print(kwargs)
model = kwargs["model"]
messages = kwargs["messages"]
optional_params = kwargs.get("optional_params", {})
@ -732,11 +727,11 @@ def exception_logging(
model_call_details
) # Expectation: any logger function passed in by the user should accept a dict object
except Exception as e:
print(
print_verbose(
f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
)
except Exception as e:
print(
print_verbose(
f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
)
pass
@ -799,7 +794,6 @@ def client(original_function):
return logging_obj
except Exception as e: # DO NOT BLOCK running the function because of this
print_verbose(f"[Non-Blocking] {traceback.format_exc()}; args - {args}; kwargs - {kwargs}")
print(e)
pass
def crash_reporting(*args, **kwargs):
@ -1776,9 +1770,9 @@ def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None, api_
custom_llm_provider = "bedrock"
if custom_llm_provider is None or custom_llm_provider=="":
print()
print("\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m")
print()
print() # noqa
print("\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m") # noqa
print() # noqa
raise ValueError(f"LLM Provider NOT provided. Pass in the LLM provider you are trying to call. E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/{model}',..)` Learn more: https://docs.litellm.ai/docs/providers")
return model, custom_llm_provider, dynamic_api_key, api_base
except Exception as e:
@ -2772,7 +2766,7 @@ def get_all_keys(llm_provider=None):
def get_model_list():
global last_fetched_at
global last_fetched_at, print_verbose
try:
# if user is using hosted product -> get their updated model list
user_email = (
@ -2784,7 +2778,7 @@ def get_model_list():
if user_email:
# make the api call
last_fetched_at = time.time()
print(f"last_fetched_at: {last_fetched_at}")
print_verbose(f"last_fetched_at: {last_fetched_at}")
response = requests.post(
url="http://api.litellm.ai/get_model_list",
headers={"content-type": "application/json"},
@ -2820,10 +2814,10 @@ def exception_type(
global user_logger_fn, liteDebuggerClient
exception_mapping_worked = False
if litellm.suppress_debug_info is False:
print()
print("\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m")
print("LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'.")
print()
print() # noqa
print("\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m") # noqa
print("LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'.") # noqa
print() # noqa
try:
if isinstance(original_exception, OriginalError):
# Handle the OpenAIError
@ -3401,7 +3395,7 @@ def exception_type(
model=model
)
elif hasattr(original_exception, "status_code"):
print(f"status code: {original_exception.status_code}")
print_verbose(f"status code: {original_exception.status_code}")
if original_exception.status_code == 401:
exception_mapping_worked = True
raise AuthenticationError(
@ -4267,12 +4261,11 @@ def completion_with_fallbacks(**kwargs):
return response
except Exception as e:
print(e)
print_verbose(e)
rate_limited_models.add(model)
model_expiration_times[model] = (
time.time() + 60
) # cool down this selected model
# print(f"rate_limited_models {rate_limited_models}")
pass
return response
@ -4417,7 +4410,7 @@ def trim_messages(
return final_messages
except Exception as e: # [NON-Blocking, if error occurs just return final_messages
print("Got exception while token trimming", e)
print_verbose("Got exception while token trimming", e)
return messages
def get_valid_models():