refactor logging

services/adaptor_gen/adaptor_gen.py

@@ -3,9 +3,9 @@
 
 from signature_generator import signature_generator as sig_gen
 from code_generator import code_generator as code_gen
+from util import createLogger
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+logger = createLogger("adaptor_gen")
 
 
 class Payload(DictObj):

services/code_generator/code_generator.py

@@ -1,5 +1,4 @@
-import logging
-from util import DictObj
+from util import DictObj, createLogger
 
 from .utils import (
     generate_code_prompt,
@@ -8,8 +7,7 @@
 from inference import inference
 
 
-logging.basicConfig(level=logging.INFO, filename="out.txt")
-logger = logging.getLogger(__name__)
+logger = createLogger("code_generator")
 
 
 class Payload(DictObj):
@@ -21,9 +19,9 @@ class Payload(DictObj):
 # generate adaptor code based on a model and signature
 def main(dataDict) -> str:
     data = Payload(dataDict)
-
+    logger.generate("Running code generator with model {}".format(data.model))
     result = generate(data.model, data.signature, data.get("api_key"))
-
+    logger.generate("Code generation complete!")
     return result
 
 

services/code_generator/prompts.py

@@ -1,8 +1,7 @@
 import copy
-import logging
+from util import createLogger
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+logger = createLogger("code_generator.prompts")
 
 
 prompts = {

services/code_generator/utils.py

@@ -1,7 +1,10 @@
 import logging
-import os
 from .prompts import generate_prompt
 
+from util import createLogger
+
+logger = createLogger("code_generator.utils")
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

services/inference/models/codet5.py

@@ -3,8 +3,9 @@
 
 from transformers import RobertaTokenizer, T5ForConditionalGeneration
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+from util import createLogger
+
+logger = createLogger("inference.codet5")
 
 
 """

services/inference/models/gpt3_turbo.py

@@ -1,10 +1,11 @@
-import logging
 import os
 
 from openai import OpenAI
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+from util import createLogger
+
+logger = createLogger("inference.gpt3_turbo")
+
 
 OPENAI_API_KEY = os.getenv(
     "OPENAI_API_KEY",

services/inference/models/gpt_ft.py

@@ -1,10 +1,10 @@
-import logging
 import os
 
 from openai import OpenAI
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+from util import createLogger
+
+logger = createLogger("inference.gpt_ft")
 
 OPENAI_API_KEY = os.getenv(
     "OPENAI_API_KEY",

services/signature_generator/signature_generator.py

@@ -1,5 +1,3 @@
-import logging
-
 import inference.inference
 
 from .prompts import generate_prompt
@@ -12,9 +10,9 @@
 
 from inference import inference
 
-# TODO the platform should deal with logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+from util import createLogger
+
+logger = createLogger("signature_generator")
 
 
 class Payload(DictObj):

services/signature_generator/utils.py

@@ -1,10 +1,9 @@
-import logging
-import os
-
 import spacy
 from spacy.tokens import Doc, Token
 
-logger = logging.getLogger(__name__)
+from util import createLogger
+
+logger = createLogger("signature_generator.utils")
 
 # Load the English language model
 nlp = spacy.load("en_core_web_sm")