Refactor logging configuration and remove unnecessary code

.env.template

@@ -6,11 +6,6 @@
 
 MAIN_SYSTEM_PROMPT=You are an AI Assistant integrated within a Python-based application designed to assist users by leveraging a suite of tools and functions, both synchronous and asynchronous, to process user requests and manage dynamic workflows. Your capabilities include interacting with a larger AI language model (LLM) for synchronous and asynchronous assistance, accessing the current date and time, and utilizing enabled plugins for additional functionalities. You are expected to maintain a conversation memory, ensuring the context remains within the token limit for efficient processing. When responding to user requests, consider the available tools and their descriptions, dynamically structuring workflows to include multiple turns where necessary. Prioritize reasoning and delivering the best possible response based on the users original request, taking into account the data gathered and actions completed during the interaction. Ensure that your responses are clear, concise, and directly address the users needs, while also being prepared to handle errors or unexpected situations gracefully.
 
-LOGGING_ENABLED=True
-LOGGING_LEVEL=debug
-LOGGING_FILE=logs/GPT_ALL.log
-LOGGING_FORMAT=%(name)s - %(levelname)s - %(message)s
-
 #########################################################################################
 #
 # OPENAI API SETTINGS

app.py

@@ -12,7 +12,6 @@
 import asyncio
 import inspect
 import json
-import logging
 import os
 import sys
 from pathlib import Path
@@ -25,10 +24,6 @@
 from rich.prompt import Prompt
 
 from config import (
-    LOGGING_ENABLED,
-    LOGGING_FILE,
-    LOGGING_FORMAT,
-    LOGGING_LEVEL,
     MAIN_SYSTEM_PROMPT,
     OPENAI_API_KEY,
     OPENAI_MODEL,
@@ -81,33 +76,6 @@
     "presence_penalty": 0,
 }
 
-# Configure logging based on the settings from .env
-if LOGGING_ENABLED:
-    # Set the logging level based on the LOGGING_LEVEL string
-    level = getattr(logging, LOGGING_LEVEL.upper(), logging.WARNING)
-    # Configure logging with or without a log file
-    if LOGGING_FILE:
-        logging.basicConfig(
-            level=level,
-            format=LOGGING_FORMAT,
-            filename=LOGGING_FILE
-        )
-        # Set the logging level for specific libraries
-        logging.getLogger(
-            'httpcore.http11').setLevel(logging.INFO)
-        logging.getLogger(
-            'googleapiclient.discovery_cache').setLevel(logging.WARNING)
-        logging.getLogger(
-            'httpcore').setLevel(logging.WARNING)
-        logging.getLogger(
-            'markdown_it.rules_block').setLevel(logging.WARNING)
-        logging.getLogger(
-            'comtypes').setLevel(logging.WARNING)
-    else:
-        logging.basicConfig(level=level, format=LOGGING_FORMAT)
-else:
-    logging.disable(logging.CRITICAL)
-
 
 def join_messages(memory: list[dict]):
     """
@@ -159,10 +127,6 @@ async def follow_conversation(
     Returns:
         The conversation memory.
     """
-    logging.info(
-        'Starting conversation with user input from line 159: %s',
-        user_text
-    )
 
     ind = min(mem_size, len(memory))
     if ind == 0:
@@ -176,32 +140,27 @@ async def follow_conversation(
         ) and ind > 1
     ):
         ind -= 1
-        memory.pop(0)  # Removes the oldest messages if the limit is exceeded
-        logging.debug('Line 172 Removed oldest message due to context limit')
+        memory.pop(0)
+
 
     response = await main_client.chat.completions.create(
         model=model, messages=memory[-ind:]
     )
-    logging.info('Line 177 Received response from chat completion')
 
-    # Checks if the response has the expected structure and content
     if (
         response.choices
         and response.choices[0].message
         and response.choices[0].message.content is not None
     ):
         tr = response.choices[0].message.content
         memory.append({"role": "assistant", "content": tr})
-        logging.info('Line 187 Added assistant response to memory: %s', tr)
     else:
-        # Handles the case where the expected content is not available
         memory.append(
             {
                 "role": "assistant",
                 "content": "I'm not sure how to respond to that."
             }
         )
-        logging.warning('Line 196 Expected content not available in response')
 
     return memory
 
@@ -230,10 +189,6 @@ async def run_conversation(
     Returns:
         The final response from the model.
     """
-    logging.info(
-        'Starting conversation with user input line 225: %s',
-        original_user_input
-    )
 
     memory = await follow_conversation(
         user_text=original_user_input,
@@ -245,7 +200,6 @@ async def run_conversation(
 
     while len(json.dumps(memory)) > 128000:
         memory.pop(0)
-        logging.debug('Line 240 removed oldest message due to context limit')
 
     response = await main_client.chat.completions.create(
         model=openai_defaults["model"],
@@ -258,7 +212,6 @@ async def run_conversation(
         frequency_penalty=openai_defaults["frequency_penalty"],
         presence_penalty=openai_defaults["presence_penalty"],
     )
-    logging.info('Line 253 received response from chat completion')
 
     response_message = response.choices[0].message
     tool_calls = (
@@ -273,10 +226,6 @@ async def run_conversation(
                 "role": "assistant", "content": response_message.content
             }
         )
-        logging.info(
-            'Line 268 added assistant response to memory: %s',
-            response_message.content
-        )
 
     if tool_calls:
         messages.append(response_message)
@@ -286,30 +235,15 @@ async def run_conversation(
             function_name = tool_call.function.name
 
             if function_name not in available_functions:
-                logging.warning(
-                    'Line 281 function %s is not available',
-                    function_name
-                )
                 continue
 
             function_to_call = available_functions[function_name]
             function_args = json.loads(tool_call.function.arguments)
 
-            logging.info(
-                "Line 290 calling function: %s args: %s",
-                function_name,
-                function_args,
-            )
-            # Inside your run_conversation function
             if inspect.iscoroutinefunction(function_to_call):
                 function_response = await function_to_call(**function_args)
             else:
                 function_response = function_to_call(**function_args)
-            logging.info(
-                "Line 300 function %s returned: %s",
-                function_name,
-                function_response,
-            )
 
             if function_response is None:
                 function_response = "No response received from the function."
@@ -326,7 +260,6 @@ async def run_conversation(
             messages.append(function_response_message)
             executed_tool_call_ids.append(tool_call.id)
 
-        # Ensure the next message prompts the assistant to use tool responses
         messages.append(
             {
                 "role": "user",
@@ -341,7 +274,6 @@ async def run_conversation(
             }
         )
 
-        # Create next completion ensuring to pass the updated messages array
         second_response = await main_client.chat.completions.create(
             model=openai_defaults["model"],
             messages=messages,
@@ -353,7 +285,7 @@ async def run_conversation(
             frequency_penalty=openai_defaults["frequency_penalty"],
             presence_penalty=openai_defaults["presence_penalty"],
         )
-        logging.info('Line 350 received second response from chat completion')
+
         return second_response, memory
     else:
         return response, memory
@@ -381,7 +313,6 @@ async def main():
 
     console.print(Markdown("# 👋  GPT_ALL 👋"), style="bold blue")
 
-    # Initialize available base functions and tools
     available_functions = {
         "get_current_date_time": get_current_date_time,
         "ask_chat_gpt_4_0314_synchronous": ask_chat_gpt_4_0314_synchronous,
@@ -394,7 +325,6 @@ async def main():
         "ask_gpt_4_vision": ask_gpt_4_vision,
     }
 
-    # Define core tools here
     tools = [
         {
             "type": "function",
@@ -625,37 +555,28 @@ async def main():
         },
     ]
 
-    # Use the load_plugins_and_get_tools function to conditionally add tools
     available_functions, tools = await enable_plugins(
         available_functions,
         tools
     )
-    logging.info('Enabled plugins line 560')
 
-    # Initialize the conversation memory
     memory = []
 
     # Main Loop
     while True:
-        # Ask the user for input
+
         user_input = Prompt.ask(
             "\nHow can I be of assistance? ([yellow]/tools[/yellow] or [bold yellow]quit[/bold yellow])",
         )
-        logging.info('Line 555 received user input: %s', user_input)
 
-        # Check if the user wants to exit the program
         if user_input.lower() == "quit":
-            logging.info('User requested to quit the program')
             console.print("\nQuitting the program.", style="bold red")
             break
 
-        # Check if the user wants to see the available tools
         elif user_input.lower() == "/tools":
-            logging.info('User requested to see the available tools')
             display_help(tools)
             continue
 
-        # Prepare the conversation messages
         messages = [
             {
                 "role": "system",
@@ -667,13 +588,11 @@ async def main():
             },
             {"role": "user", "content": f"{user_input}"},
         ]
-        logging.info('Line 581 prepared conversation messages')
 
         with live_spinner:
 
             live_spinner.start()
 
-            # Pass the user input and memory to the run_conversation function
             final_response, memory = await run_conversation(
                 messages=messages,
                 tools=tools,
@@ -682,36 +601,26 @@ async def main():
                 mem_size=200,
                 memory=memory,
             )
-            # Stop the spinner
             live_spinner.stop()
 
-        # Print the final response from the model or use TTS
         if final_response:
             response_message = final_response.choices[0].message
             if response_message.content is not None:
                 final_text = response_message.content
                 if use_tts:
-                    # Use TTS to output the final response
                     console.print("\n" + final_text, style="green")
-                    tts_output(final_text)  # Call the tts_output function directly
+                    tts_output(final_text)
                 else:
-                    # Print the final response to the console
                     console.print("\n" + final_text, style="green")
             else:
-                # Print an error message if the model did not return a response
-                logging.warning('Model did not return a response line 610')
                 console.print("\nI'm not sure how to help with that.", style="red")
         else:
-            # Print an error message if the model did not return a response
-            logging.warning('Model did not return a response line 614')
             console.print("\nI'm not sure how to help with that.", style="red")
 
         # Remove tools from the tools list after processing
         tools[:] = [tool for tool in tools if not tool.get("function", {}).get("name", "").lower() in user_input.lower()]
-        logging.info('Removed used tools from the tools list line 622')
 
 
 # Run the main function
 if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO)
     asyncio.run(main())

app.yaml

@@ -0,0 +1,15 @@
+runtime: python3.12
+env: flex
+
+entrypoint: python -m web_app
+
+runtime_config:
+  python_version: 3.12
+
+manual_scaling:
+  instances: 1
+
+resources:
+  cpu: 1
+  memory_gb: 2
+  disk_size_gb: 10

config.py

@@ -21,15 +21,6 @@
 # Define the live_spinner
 live_spinner = Live(Spinner("pong", " "), auto_refresh=True)
 
-
-# Main app logging configuration.
-LOGGING_ENABLED = os.getenv('LOGGING_ENABLED', 'false').lower() == 'true'
-LOGGING_LEVEL = os.getenv('LOGGING_LEVEL', 'WARNING')
-LOGGING_FILE = os.getenv('LOGGING_FILE', None)
-LOGGING_FORMAT = os.getenv(
-    'LOGGING_FORMAT', '%(name)s - %(levelname)s - %(message)s'
-)
-
 # Main app system prompt.
 MAIN_SYSTEM_PROMPT = os.getenv("MAIN_SYSTEM_PROMPT")