Code review changes

Dockerfile

@@ -2,7 +2,7 @@ FROM python:3.11
 
 WORKDIR /app
 COPY scripts/ /app
-COPY requirements.txt /app/requirements.txt
+
 RUN pip install -r requirements.txt
 
 CMD ["python", "main.py"]

scripts/ai_config.py

@@ -13,7 +13,6 @@ class AIConfig:
     # Soon this will go in a folder where it remembers more stuff about the run(s)
     SAVE_FILE = "last_run_ai_settings.yaml"
 
-
     @classmethod
     def load(cls, config_file=SAVE_FILE):
         """Load variables from yaml file if it exists, otherwise use defaults."""
@@ -29,7 +28,6 @@ class AIConfig:
 
         return cls(ai_name, ai_role, ai_goals)
 
-
     def save(self, config_file=SAVE_FILE):
         """Save variables to yaml file."""
         config = {"ai_name": self.ai_name, "ai_role": self.ai_role, "ai_goals": self.ai_goals}

scripts/ai_functions.py

@@ -1,5 +1,5 @@
 from typing import List, Optional
-from json import dumps
+import json
 from config import Config
 from call_ai_function import call_ai_function
 from json_parser import fix_and_parse_json
@@ -23,7 +23,7 @@ def improve_code(suggestions: List[str], code: str) -> str:
     function_string = (
         "def generate_improved_code(suggestions: List[str], code: str) -> str:"
     )
-    args = [dumps(suggestions), code]
+    args = [json.dumps(suggestions), code]
     description_string = """Improves the provided code based on the suggestions provided, making no other changes."""
 
     result_string = call_ai_function(function_string, args, description_string)
@@ -36,7 +36,7 @@ def write_tests(code: str, focus: List[str]) -> str:
     function_string = (
         "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
     )
-    args = [code, dumps(focus)]
+    args = [code, json.dumps(focus)]
     description_string = """Generates test cases for the existing code, focusing on specific areas if required."""
 
     result_string = call_ai_function(function_string, args, description_string)

scripts/browse.py

@@ -1,14 +1,13 @@
-from requests import get
+import requests
 from bs4 import BeautifulSoup
 from config import Config
 from llm_utils import create_chat_completion
 
 cfg = Config()
 
-
 def scrape_text(url):
     """Scrape text from a webpage"""
-    response = get(url)
+    response = requests.get(url)
 
     # Check if the response contains an HTTP error
     if response.status_code >= 400:
@@ -30,26 +29,22 @@ def scrape_text(url):
 def extract_hyperlinks(soup):
     """Extract hyperlinks from a BeautifulSoup object"""
     hyperlinks = []
-
     for link in soup.find_all('a', href=True):
         hyperlinks.append((link.text, link['href']))
-
     return hyperlinks
 
 
 def format_hyperlinks(hyperlinks):
     """Format hyperlinks into a list of strings"""
     formatted_links = []
-
     for link_text, link_url in hyperlinks:
         formatted_links.append(f"{link_text} ({link_url})")
-
     return formatted_links
 
 
 def scrape_links(url):
     """Scrape hyperlinks from a webpage"""
-    response = get(url)
+    response = requests.get(url)
 
     # Check if the response contains an HTTP error
     if response.status_code >= 400:
@@ -72,7 +67,6 @@ def split_text(text, max_length=8192):
     current_chunk = []
 
     for paragraph in paragraphs:
-
         if current_length + len(paragraph) + 1 <= max_length:
             current_chunk.append(paragraph)
             current_length += len(paragraph) + 1

scripts/call_ai_function.py

@@ -1,8 +1,8 @@
 from config import Config
-from llm_utils import create_chat_completion
+
 cfg = Config()
 
-
+from llm_utils import create_chat_completion
 # This is a magic function that can do anything with no-code. See
 # https://github.com/Torantulino/AI-Functions for more info.
 def call_ai_function(function, args, description, model=cfg.smart_llm_model):

scripts/chat.py

@@ -1,11 +1,11 @@
-from time import sleep
+import time
 import openai
 from dotenv import load_dotenv
 from config import Config
 import token_counter
 from llm_utils import create_chat_completion
-cfg = Config()
 
+cfg = Config()
 
 def create_chat_message(role, content):
     """
@@ -48,10 +48,8 @@ def chat_with_ai(
             """
             model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
             # Reserve 1000 tokens for the response
-
             if debug:
                 print(f"Token limit: {token_limit}")          
-            
             send_token_limit = token_limit - 1000
 
             current_context = [
@@ -73,7 +71,6 @@ def chat_with_ai(
                 message_to_add = full_message_history[next_message_to_add_index]
 
                 tokens_to_add = token_counter.count_message_tokens([message_to_add], model)
-
                 if current_tokens_used + tokens_to_add > send_token_limit:
                     break
 
@@ -99,16 +96,13 @@ def chat_with_ai(
                 print(f"Send Token Count: {current_tokens_used}")
                 print(f"Tokens remaining for response: {tokens_remaining}")
                 print("------------ CONTEXT SENT TO AI ---------------")
-
                 for message in current_context:
                     # Skip printing the prompt
-
                     if message["role"] == "system" and message["content"] == prompt:
                         continue
                     print(
                         f"{message['role'].capitalize()}: {message['content']}")
                     print()
-
                 print("----------- END OF CONTEXT ----------------")
 
             # TODO: use a model defined elsewhere, so that model can contain temperature and other settings we care about
@@ -130,4 +124,4 @@ def chat_with_ai(
         except openai.error.RateLimitError:
             # TODO: WHen we switch to langchain, this is built in
             print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
-            sleep(10)
+            time.sleep(10)

scripts/commands.py

@@ -1,7 +1,7 @@
 import browse
 import json
 import memory as mem
-from datetime import datetime
+import datetime
 import agent_manager as agents
 import speak
 from config import Config
@@ -110,7 +110,7 @@ def execute_command(command_name, arguments):
 def get_datetime():
     """Return the current date and time"""
     return "Current date and time: " + \
-        datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
 
 def google_search(query, num_results=8):

scripts/config.py

@@ -44,52 +44,42 @@ class Config(metaclass=Singleton):
         # Initialize the OpenAI API client
         openai.api_key = self.openai_api_key
 
-
     def set_continuous_mode(self, value: bool):
         """Set the continuous mode value."""
         self.continuous_mode = value
 
-
     def set_speak_mode(self, value: bool):
         """Set the speak mode value."""
         self.speak_mode = value
 
-
     def set_fast_llm_model(self, value: str):
         """Set the fast LLM model value."""
         self.fast_llm_model = value
 
-
     def set_smart_llm_model(self, value: str):
         """Set the smart LLM model value."""
         self.smart_llm_model = value
 
-
     def set_fast_token_limit(self, value: int):
         """Set the fast token limit value."""
         self.fast_token_limit = value
 
-
     def set_smart_token_limit(self, value: int):
         """Set the smart token limit value."""
         self.smart_token_limit = value
 
-
     def set_openai_api_key(self, value: str):
         """Set the OpenAI API key value."""
         self.openai_api_key = value
 
-
     def set_elevenlabs_api_key(self, value: str):
         """Set the ElevenLabs API key value."""
         self.elevenlabs_api_key = value
 
-
     def set_google_api_key(self, value: str):
         """Set the Google API key value."""
         self.google_api_key = value
 
-
     def set_custom_search_engine_id(self, value: str):
         """Set the custom search engine ID value."""
         self.custom_search_engine_id = value

scripts/data.py

@@ -1,4 +1,4 @@
-from os import path
+import os
 from pathlib import Path
 SRC_DIR = Path(__file__).parent
 
@@ -6,7 +6,7 @@ def load_prompt():
     """Load the prompt from data/prompt.txt"""
     try:
         # get directory of this file:
-        file_dir = Path(path.dirname(path.realpath(__file__)))
+        file_dir = Path(os.path.dirname(os.path.realpath(__file__)))
         data_dir = file_dir / "data"
         prompt_file = data_dir / "prompt.txt"
         # Load the promt from data/prompt.txt

scripts/execute_code.py

@@ -1,5 +1,5 @@
 import docker
-from os import path
+import os
 
 
 def execute_python_file(file):
@@ -11,9 +11,9 @@ def execute_python_file(file):
     if not file.endswith(".py"):
         return "Error: Invalid file type. Only .py files are allowed."
 
-    file_path = path.join(workspace_folder, file)
+    file_path = os.path.join(workspace_folder, file)
 
-    if not path.isfile(file_path):
+    if not os.path.isfile(file_path):
         return f"Error: File '{file}' does not exist."
 
     try:

scripts/file_operations.py

@@ -36,10 +36,8 @@ def write_to_file(filename, text):
     try:
         filepath = safe_join(working_directory, filename)
         directory = os.path.dirname(filepath)
-
         if not os.path.exists(directory):
             os.makedirs(directory)
-
         with open(filepath, "w") as f:
             f.write(text)
         return "File written to successfully."

scripts/json_parser.py

@@ -1,7 +1,6 @@
 import json
 from call_ai_function import call_ai_function
 from config import Config
-
 cfg = Config()
 
 def fix_and_parse_json(json_str: str, try_to_fix_with_gpt: bool = True):
@@ -38,18 +37,15 @@ def fix_and_parse_json(json_str: str, try_to_fix_with_gpt: bool = True):
           json_str = json_str[:last_brace_index+1]
           return json.loads(json_str)
         except Exception as e:
-
           if try_to_fix_with_gpt:
             print(f"Warning: Failed to parse AI output, attempting to fix.\n If you see this warning frequently, it's likely that your prompt is confusing the AI. Try changing it up slightly.")
             # Now try to fix this up using the ai_functions
             ai_fixed_json = fix_json(json_str, json_schema, False)
-
             if ai_fixed_json != "failed":
               return json.loads(ai_fixed_json)
             else:
               print(f"Failed to fix ai output, telling the AI.") # This allows the AI to react to the error message, which usually results in it correcting its ways.
               return json_str
-            
           else:
             raise e
         
@@ -63,11 +59,9 @@ def fix_json(json_str: str, schema: str, debug=False) -> str:
     # If it doesn't already start with a "`", add one:
     if not json_str.startswith("`"):
       json_str = "```json\n" + json_str + "\n```"
-
     result_string = call_ai_function(
         function_string, args, description_string, model=cfg.fast_llm_model
     )
-
     if debug:
         print("------------ JSON FIX ATTEMPT ---------------")
         print(f"Original JSON: {json_str}")

scripts/llm_utils.py

@@ -1,6 +1,5 @@
 import openai
 from config import Config
-
 cfg = Config()
 
 openai.api_key = cfg.openai_api_key

scripts/main.py

@@ -27,24 +27,17 @@ def print_to_console(
         max_typing_speed=0.01):
     """Prints text to the console with a typing effect"""
     global cfg
-
     if speak_text and cfg.speak_mode:
         speak.say_text(f"{title}. {content}")
-
     print(title_color + title + " " + Style.RESET_ALL, end="")
-
     if content:
-
         if isinstance(content, list):
             content = " ".join(content)
         words = content.split()
-
         for i, word in enumerate(words):
             print(word, end="", flush=True)
-
             if i < len(words) - 1:
                 print(" ", end="", flush=True)
-
             typing_speed = random.uniform(min_typing_speed, max_typing_speed)
             time.sleep(typing_speed)
             # type faster after each word
@@ -88,7 +81,6 @@ def print_assistant_thoughts(assistant_reply):
         if assistant_thoughts_plan:
             print_to_console("PLAN:", Fore.YELLOW, "")
             # If it's a list, join it into a string
-
             if isinstance(assistant_thoughts_plan, list):
                 assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
             elif isinstance(assistant_thoughts_plan, dict):
@@ -96,7 +88,6 @@ def print_assistant_thoughts(assistant_reply):
 
             # Split the input_string using the newline character and dashes
             lines = assistant_thoughts_plan.split('\n')
-
             for line in lines:
                 line = line.lstrip("- ")
                 print_to_console("- ", Fore.GREEN, line.strip())
@@ -131,13 +122,11 @@ def load_variables(config_file="config.yaml"):
     # Prompt the user for input if config file is missing or empty values
     if not ai_name:
         ai_name = input("Name your AI: ")
-
         if ai_name == "":
             ai_name = "Entrepreneur-GPT"
 
     if not ai_role:        
         ai_role = input(f"{ai_name} is: ")
-
         if ai_role == "":
             ai_role = "an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth."
 
@@ -146,20 +135,16 @@ def load_variables(config_file="config.yaml"):
         print("For example: \nIncrease net worth, Grow Twitter Account, Develop and manage multiple businesses autonomously'")
         print("Enter nothing to load defaults, enter nothing when finished.")
         ai_goals = []
-
         for i in range(5):
             ai_goal = input(f"Goal {i+1}: ")
-
             if ai_goal == "":
                 break
             ai_goals.append(ai_goal)
-
         if len(ai_goals) == 0:
             ai_goals = ["Increase net worth", "Grow Twitter Account", "Develop and manage multiple businesses autonomously"]
          
     # Save variables to yaml file
     config = {"ai_name": ai_name, "ai_role": ai_role, "ai_goals": ai_goals}
-
     with open(config_file, "w") as file:
         documents = yaml.dump(config, file)
 
@@ -168,7 +153,6 @@ def load_variables(config_file="config.yaml"):
 
     # Construct full prompt
     full_prompt = f"You are {ai_name}, {ai_role}\n{prompt_start}\n\nGOALS:\n\n"
-
     for i, goal in enumerate(ai_goals):
         full_prompt += f"{i+1}. {goal}\n"
 
@@ -179,7 +163,6 @@ def load_variables(config_file="config.yaml"):
 def construct_prompt():
     """Construct the prompt for the AI to respond to"""
     config = AIConfig.load()
-
     if config.ai_name:
         print_to_console(
             f"Welcome back! ",
@@ -191,7 +174,6 @@ def construct_prompt():
             Role:  {config.ai_role}
             Goals: {config.ai_goals}  
             Continue (y/n): """)        
-        
         if should_continue.lower() == "n":
             config = AIConfig()
 
@@ -222,9 +204,7 @@ def prompt_user():
         "Name your AI: ",
         Fore.GREEN,
         "For example, 'Entrepreneur-GPT'")    
-    
     ai_name = input("AI Name: ")
-
     if ai_name == "":
         ai_name = "Entrepreneur-GPT"
 
@@ -240,7 +220,6 @@ def prompt_user():
         Fore.GREEN,
         "For example, 'an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth.'")
     ai_role = input(f"{ai_name} is: ")
-
     if ai_role == "":
         ai_role = "an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth."
 
@@ -249,18 +228,13 @@ def prompt_user():
         "Enter up to 5 goals for your AI: ",
         Fore.GREEN,
         "For example: \nIncrease net worth, Grow Twitter Account, Develop and manage multiple businesses autonomously'")    
-    
     print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
     ai_goals = []
-
     for i in range(5):
         ai_goal = input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
-
         if ai_goal == "":
             break
-
         ai_goals.append(ai_goal)
-
     if len(ai_goals) == 0:
         ai_goals = ["Increase net worth", "Grow Twitter Account",
                     "Develop and manage multiple businesses autonomously"]
@@ -268,7 +242,6 @@ def prompt_user():
     config = AIConfig(ai_name, ai_role, ai_goals)
     return config
 
-
 def parse_arguments():
     """Parses the arguments passed to the script"""
     global cfg
@@ -347,7 +320,6 @@ while True:
             flush=True)
         while True:
             console_input = input(Fore.MAGENTA + "Input:" + Style.RESET_ALL)            
-            
             if console_input.lower() == "y":
                 user_input = "GENERATE NEXT COMMAND JSON"
                 break

scripts/speak.py

@@ -2,7 +2,6 @@ import os
 from playsound import playsound
 import requests
 from config import Config
-
 cfg = Config()
 import gtts
 
@@ -20,7 +19,8 @@ def eleven_labs_speech(text, voice_index=0):
     tts_url = "https://api.elevenlabs.io/v1/text-to-speech/{voice_id}".format(
         voice_id=voices[voice_index])
     formatted_message = {"text": text}
-    response = requests.post(tts_url, headers=tts_headers, json=formatted_message)
+    response = requests.post(
+        tts_url, headers=tts_headers, json=formatted_message)
 
     if response.status_code == 200:
         with open("speech.mpeg", "wb") as f:

scripts/spinner.py

@@ -14,7 +14,6 @@ class Spinner:
         self.running = False
         self.spinner_thread = None
 
-
     def spin(self):
         """Spin the spinner"""
         while self.running:
@@ -23,14 +22,12 @@ class Spinner:
             time.sleep(self.delay)
             sys.stdout.write('\b' * (len(self.message) + 2))
 
-
     def __enter__(self):
         """Start the spinner"""
         self.running = True
         self.spinner_thread = threading.Thread(target=self.spin)
         self.spinner_thread.start()
 
-
     def __exit__(self, exc_type, exc_value, exc_traceback):
         """Stop the spinner"""
         self.running = False

scripts/token_counter.py

@@ -1,7 +1,6 @@
 import tiktoken
 from typing import List, Dict
 
-
 def count_message_tokens(messages : List[Dict[str, str]], model : str = "gpt-3.5-turbo-0301") -> int:
     """
     Returns the number of tokens used by a list of messages.
@@ -18,7 +17,6 @@ def count_message_tokens(messages : List[Dict[str, str]], model : str = "gpt-3.5
     except KeyError:
         print("Warning: model not found. Using cl100k_base encoding.")
         encoding = tiktoken.get_encoding("cl100k_base")
-
     if model == "gpt-3.5-turbo":
         # !Node: gpt-3.5-turbo may change over time. Returning num tokens assuming gpt-3.5-turbo-0301.")
         return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
@@ -34,19 +32,15 @@ def count_message_tokens(messages : List[Dict[str, str]], model : str = "gpt-3.5
     else:
         raise NotImplementedError(f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")
     num_tokens = 0
-
     for message in messages:
         num_tokens += tokens_per_message
-
         for key, value in message.items():
             num_tokens += len(encoding.encode(value))
-
             if key == "name":
                 num_tokens += tokens_per_name
     num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
     return num_tokens
 
-
 def count_string_tokens(string: str, model_name: str) -> int:
     """
     Returns the number of tokens in a text string.