Fixed simple pylint warnings

yolo.py

@@ -140,14 +140,14 @@ def get_os_friendly_name():
 
     return os_name
 
-def get_system_prompt(user_prompt, shell):
+def get_system_prompt(shell):
     """
-    Retrieves and constructs a system prompt by replacing placeholders 
+    Retrieves and constructs a system prompt by replacing placeholders
     in a predefined template with specific values.
 
-    The function finds the absolute path of the currently executing file 
+    The function finds the absolute path of the currently executing file
-    and, based on this path, identifies the directory of PROMPT_FILE. 
+    and, based on this path, identifies the directory of PROMPT_FILE.
-    It reads the file and replaces the placeholders {shell} and {os} 
+    It reads the file and replaces the placeholders {shell} and {os}
     with the provided shell parameter and the friendly name of the operating system, respectively.
 
     Parameters
@@ -160,7 +160,7 @@ def get_system_prompt(user_prompt, shell):
     Returns
     -------
     str
-        The system prompt, constructed from the template prompt in PROMPT_FILE 
+        The system prompt, constructed from the template prompt in PROMPT_FILE
         with the shell and OS placeholders replaced with actual values.
     """
     yolo_path = os.path.abspath(__file__)
@@ -171,22 +171,23 @@ def get_system_prompt(user_prompt, shell):
     system_prompt = open(prompt_file,"r").read()
     system_prompt = system_prompt.replace("{shell}", shell)
     system_prompt = system_prompt.replace("{os}", get_os_friendly_name())
-    
+
     return system_prompt
 
 def chat_completion(client, query, config):
     """
     Generate a chat-based completion for a given query using a specified model.
-    
+
     This function sends a user query to a chat model and returns the generated response.
-    
+
     Parameters:
     client (object): The client object to interact with the chat service.
     query (str): The user's query to send to the chat model.
     config (dict): Configuration settings for the chat service, which should include:
         - "shell" (str): Type of shell to use in the system prompt.
         - "model" (str): The specific model to use for the chat completion.
-        - "temperature" (float): Sampling temperature to use for the response generation (higher values mean the model will take more risks).
+        - "temperature" (float): Sampling temperature to use for the response generation (higher
+          values mean the model will take more risks).
         - "max_tokens" (int): Maximum number of tokens to generate in the chat response.
 
     Returns:
@@ -198,8 +199,8 @@ def chat_completion(client, query, config):
     if query == "":
         print ("No user prompt specified.")
         sys.exit(-1)
- 
+
-    system_prompt = get_system_prompt(query, config["shell"])
+    system_prompt = get_system_prompt(config["shell"])
 
     # Ensure query is a question
     if query[-1:] != "?" and query[-1:] != ".":
@@ -213,7 +214,7 @@ def chat_completion(client, query, config):
     ],
     temperature=config["temperature"],
     max_tokens=config["max_tokens"])
- 
+
     return response
 
 def check_for_issue(response):
@@ -273,6 +274,7 @@ def missing_posix_display():
 
     return display == b'\n'
 
+# TODO: Change the output according the configuration
 def prompt_user_input(config, response):
     """
     Print the command proposal in blue and prompt the user for next action based on the safety
@@ -336,6 +338,7 @@ def evaluate_input(config, user_input, command):
             # Unix: /bin/bash /bin/zsh: uses -c both Ubuntu and macOS should work, others might not
             subprocess.run([config["shell"], "-c", command], shell=False, check=True)
 
+    # TODO: change call_open_ai function
     if user_input.upper() == "M":
         print("Modify prompt: ", end = '')
         modded_query = input()
@@ -391,7 +394,7 @@ def main():
     result = chat_completion(client, user_prompt, config)
     check_for_issue(result)
     check_for_markdown(result)
- 
+
     user_input = prompt_user_input(config, result)
     print()
     evaluate_input(config, user_input, result)