fix linting

autogpt/__main__.py

@@ -74,9 +74,9 @@ def main() -> None:
     # this is particularly important for indexing and referencing pinecone memory
     memory = get_memory(cfg, init=True)
     logger.typewriter_log(
-        f"Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
+        "Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
     )
-    logger.typewriter_log(f"Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
+    logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
     system_prompt = ai_config.construct_full_prompt()
     if cfg.debug_mode:
         logger.typewriter_log("Prompt:", Fore.GREEN, system_prompt)

autogpt/agent/agent.py

@@ -19,18 +19,25 @@ class Agent:
         memory: The memory object to use.
         full_message_history: The full message history.
         next_action_count: The number of actions to execute.
-        system_prompt: The system prompt is the initial prompt that defines everything the AI needs to know to achieve its task successfully.
+        system_prompt: The system prompt is the initial prompt that defines everything
-        Currently, the dynamic and customizable information in the system prompt are ai_name, description and goals.
+          the AI needs to know to achieve its task successfully.
+        Currently, the dynamic and customizable information in the system prompt are
+          ai_name, description and goals.
 
-        triggering_prompt: The last sentence the AI will see before answering. For Auto-GPT, this prompt is:
+        triggering_prompt: The last sentence the AI will see before answering.
-            Determine which next command to use, and respond using the format specified above:
+            For Auto-GPT, this prompt is:
-            The triggering prompt is not part of the system prompt because between the system prompt and the triggering
+            Determine which next command to use, and respond using the format specified
-            prompt we have contextual information that can distract the AI and make it forget that its goal is to find the next task to achieve.
+              above:
+            The triggering prompt is not part of the system prompt because between the
+              system prompt and the triggering
+            prompt we have contextual information that can distract the AI and make it
+              forget that its goal is to find the next task to achieve.
             SYSTEM PROMPT
             CONTEXTUAL INFORMATION (memory, previous conversations, anything relevant)
             TRIGGERING PROMPT
 
-        The triggering prompt reminds the AI about its short term meta task (defining the next task)
+        The triggering prompt reminds the AI about its short term meta task
+        (defining the next task)
     """
 
     def __init__(
@@ -96,14 +103,13 @@ class Agent:
                 try:
                     print_assistant_thoughts(self.ai_name, assistant_reply_json)
                     command_name, arguments = get_command(assistant_reply_json)
-                    # command_name, arguments = assistant_reply_json_valid["command"]["name"], assistant_reply_json_valid["command"]["args"]
                     if cfg.speak_mode:
                         say_text(f"I want to execute {command_name}")
                 except Exception as e:
                     logger.error("Error: \n", str(e))
 
             if not cfg.continuous_mode and self.next_action_count == 0:
-                ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
+                # ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
                 # Get key press: Prompt the user to press enter to continue or escape
                 # to exit
                 logger.typewriter_log(
@@ -177,10 +183,13 @@ class Agent:
                     command_name, arguments = plugin.pre_command(
                         command_name, arguments
                     )
-                result = (
+                command_result = execute_command(
-                    f"Command {command_name} returned: "
+                    self.command_registry,
-                    f"{execute_command(self.command_registry, command_name, arguments, self.config.prompt_generator)}"
+                    command_name,
+                    arguments,
+                    self.config.prompt_generator,
                 )
+                result = f"Command {command_name} returned: " f"{command_result}"
 
                 for plugin in cfg.plugins:
                     result = plugin.post_command(command_name, result)

tests/unit/test_browse_scrape_text.py

@@ -9,16 +9,20 @@ Code Analysis
 
 Objective:
 The objective of the "scrape_text" function is to scrape the text content from
-a given URL and return it as a string, after removing any unwanted HTML tags and scripts.
+a given URL and return it as a string, after removing any unwanted HTML tags and
+ scripts.
 
 Inputs:
 - url: a string representing the URL of the webpage to be scraped.
 
 Flow:
-1. Send a GET request to the given URL using the requests library and the user agent header from the config file.
+1. Send a GET request to the given URL using the requests library and the user agent
+ header from the config file.
 2. Check if the response contains an HTTP error. If it does, return an error message.
-3. Use BeautifulSoup to parse the HTML content of the response and extract all script and style tags.
+3. Use BeautifulSoup to parse the HTML content of the response and extract all script
-4. Get the text content of the remaining HTML using the get_text() method of BeautifulSoup.
+ and style tags.
+4. Get the text content of the remaining HTML using the get_text() method of
+ BeautifulSoup.
 5. Split the text into lines and then into chunks, removing any extra whitespace.
 6. Join the chunks into a single string with newline characters between them.
 7. Return the cleaned text.
@@ -27,9 +31,12 @@ Outputs:
 - A string representing the cleaned text content of the webpage.
 
 Additional aspects:
-- The function uses the requests library and BeautifulSoup to handle the HTTP request and HTML parsing, respectively.
+- The function uses the requests library and BeautifulSoup to handle the HTTP request
-- The function removes script and style tags from the HTML to avoid including unwanted content in the text output.
+ and HTML parsing, respectively.
-- The function uses a generator expression to split the text into lines and chunks, which can improve performance for large amounts of text.
+- The function removes script and style tags from the HTML to avoid including unwanted
+ content in the text output.
+- The function uses a generator expression to split the text into lines and chunks,
+ which can improve performance for large amounts of text.
 """
 
 
@@ -40,26 +47,33 @@ class TestScrapeText:
         expected_text = "This is some sample text"
         mock_response = mocker.Mock()
         mock_response.status_code = 200
-        mock_response.text = f"<html><body><div><p style='color: blue;'>{expected_text}</p></div></body></html>"
+        mock_response.text = (
+            "<html><body><div><p style='color: blue;'>"
+            f"{expected_text}</p></div></body></html>"
+        )
         mocker.patch("requests.Session.get", return_value=mock_response)
 
-        # Call the function with a valid URL and assert that it returns the expected text
+        # Call the function with a valid URL and assert that it returns the
+        #  expected text
         url = "http://www.example.com"
         assert scrape_text(url) == expected_text
 
-    # Tests that the function returns an error message when an invalid or unreachable url is provided.
+    # Tests that the function returns an error message when an invalid or unreachable
+    #  url is provided.
     def test_invalid_url(self, mocker):
         # Mock the requests.get() method to raise an exception
         mocker.patch(
             "requests.Session.get", side_effect=requests.exceptions.RequestException
         )
 
-        # Call the function with an invalid URL and assert that it returns an error message
+        # Call the function with an invalid URL and assert that it returns an error
+        #  message
         url = "http://www.invalidurl.com"
         error_message = scrape_text(url)
         assert "Error:" in error_message
 
-    # Tests that the function returns an empty string when the html page contains no text to be scraped.
+    # Tests that the function returns an empty string when the html page contains no
+    #  text to be scraped.
     def test_no_text(self, mocker):
         # Mock the requests.get() method to return a response with no text
         mock_response = mocker.Mock()
@@ -71,7 +85,8 @@ class TestScrapeText:
         url = "http://www.example.com"
         assert scrape_text(url) == ""
 
-    # Tests that the function returns an error message when the response status code is an http error (>=400).
+    # Tests that the function returns an error message when the response status code is
+    #  an http error (>=400).
     def test_http_error(self, mocker):
         # Mock the requests.get() method to return a response with a 404 status code
         mocker.patch("requests.Session.get", return_value=mocker.Mock(status_code=404))