Apply autopep8 formatting to entire codebase

scripts/agent_manager.py

@@ -1,14 +1,16 @@
 import openai
 
 next_key = 0
-agents = {} # key, (task, full_message_history, model)
+agents = {}  # key, (task, full_message_history, model)
 
 # Create new GPT agent
+
+
 def create_agent(task, prompt, model):
     global next_key
     global agents
 
-    messages = [{"role": "user", "content": prompt},]
+    messages = [{"role": "user", "content": prompt}, ]
 
     # Start GTP3 instance
     response = openai.ChatCompletion.create(
@@ -22,12 +24,15 @@ def create_agent(task, prompt, model):
     messages.append({"role": "assistant", "content": agent_reply})
 
     key = next_key
-    next_key += 1 # This is done instead of len(agents) to make keys unique even if agents are deleted
+    # This is done instead of len(agents) to make keys unique even if agents
+    # are deleted
+    next_key += 1
 
     agents[key] = (task, messages, model)
 
     return key, agent_reply
 
+
 def message_agent(key, message):
     global agents
 
@@ -50,12 +55,14 @@ def message_agent(key, message):
 
     return agent_reply
 
+
 def list_agents():
     global agents
 
     # Return a list of agent keys and their tasks
     return [(key, task) for key, (task, _, _) in agents.items()]
 
+
 def delete_agent(key):
     global agents
 
@@ -64,5 +71,3 @@ def delete_agent(key):
         return True
     except KeyError:
         return False
-
-

scripts/ai_functions.py

@@ -2,10 +2,14 @@
 import json
 import openai
 
-def call_ai_function(function, args, description, model = "gpt-4"):
+
+def call_ai_function(function, args, description, model="gpt-4"):
     # parse args to comma seperated string
     args = ", ".join(args)
-    messages = [{"role": "system", "content": f"You are now the following python function: ```# {description}\n{function}```\n\nOnly respond with your `return` value."},{"role": "user", "content": args}]
+    messages = [{"role": "system",
+                 "content": f"You are now the following python function: ```# {description}\n{function}```\n\nOnly respond with your `return` value."},
+                {"role": "user",
+                 "content": args}]
 
     response = openai.ChatCompletion.create(
         model=model,
@@ -15,7 +19,8 @@ def call_ai_function(function, args, description, model = "gpt-4"):
 
     return response.choices[0].message["content"]
 
-### Evaluating code
+# Evaluating code
+
 
 def evaluate_code(code: str) -> List[str]:
     function_string = "def analyze_code(code: str) -> List[str]:"
@@ -26,7 +31,7 @@ def evaluate_code(code: str) -> List[str]:
     return json.loads(result_string)
 
 
-### Improving code
+# Improving code
 
 def improve_code(suggestions: List[str], code: str) -> str:
     function_string = "def generate_improved_code(suggestions: List[str], code: str) -> str:"
@@ -37,12 +42,12 @@ def improve_code(suggestions: List[str], code: str) -> str:
     return result_string
 
 
-### Writing tests
+# Writing tests
 
 def write_tests(code: str, focus: List[str]) -> str:
     function_string = "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
     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)
-    return result_string
+    return result_string

scripts/browse.py

@@ -1,7 +1,7 @@
 from googlesearch import search
 import requests
 from bs4 import BeautifulSoup
-from readability import Document#
+from readability import Document
 import openai
 
 
@@ -24,18 +24,21 @@ def scrape_text(url):
 
     return text
 
+
 def extract_hyperlinks(soup):
     hyperlinks = []
     for link in soup.find_all('a', href=True):
         hyperlinks.append((link.text, link['href']))
     return hyperlinks
 
+
 def format_hyperlinks(hyperlinks):
     formatted_links = []
     for link_text, link_url in hyperlinks:
         formatted_links.append(f"{link_text} ({link_url})")
     return formatted_links
 
+
 def scrape_links(url):
     response = requests.get(url)
 
@@ -49,9 +52,10 @@ def scrape_links(url):
         script.extract()
 
     hyperlinks = extract_hyperlinks(soup)
-    
+
     return format_hyperlinks(hyperlinks)
 
+
 def split_text(text, max_length=8192):
     paragraphs = text.split("\n")
     current_length = 0
@@ -69,22 +73,33 @@ def split_text(text, max_length=8192):
     if current_chunk:
         yield "\n".join(current_chunk)
 
-def summarize_text(text, is_website = True):
+
+def summarize_text(text, is_website=True):
     if text == "":
         return "Error: No text to summarize"
-    
+
     print("Text length: " + str(len(text)) + " characters")
     summaries = []
     chunks = list(split_text(text))
 
     for i, chunk in enumerate(chunks):
-        print("Summarizing chunk " + str(i+1) + " / " + str(len(chunks)))
+        print("Summarizing chunk " + str(i + 1) + " / " + str(len(chunks)))
         if is_website:
-            messages = [{"role": "user", "content": "Please summarize the following website text, do not describe the general website, but instead concisely extract the specifc information this subpage contains.: " + chunk},]
+            messages = [
+                {
+                    "role": "user",
+                    "content": "Please summarize the following website text, do not describe the general website, but instead concisely extract the specifc information this subpage contains.: " +
+                    chunk},
+            ]
         else:
-            messages = [{"role": "user", "content": "Please summarize the following text, focusing on extracting concise and specific information: " + chunk},]
-
-        response= openai.ChatCompletion.create(
+            messages = [
+                {
+                    "role": "user",
+                    "content": "Please summarize the following text, focusing on extracting concise and specific information: " +
+                    chunk},
+            ]
+
+        response = openai.ChatCompletion.create(
             model="gpt-3.5-turbo",
             messages=messages,
             max_tokens=300,
@@ -98,9 +113,19 @@ def summarize_text(text, is_website = True):
 
     # Summarize the combined summary
     if is_website:
-        messages = [{"role": "user", "content": "Please summarize the following website text, do not describe the general website, but instead concisely extract the specifc information this subpage contains.: " + combined_summary},]
+        messages = [
+            {
+                "role": "user",
+                "content": "Please summarize the following website text, do not describe the general website, but instead concisely extract the specifc information this subpage contains.: " +
+                combined_summary},
+        ]
     else:
-        messages = [{"role": "user", "content": "Please summarize the following text, focusing on extracting concise and specific infomation: " + combined_summary},]
+        messages = [
+            {
+                "role": "user",
+                "content": "Please summarize the following text, focusing on extracting concise and specific infomation: " +
+                combined_summary},
+        ]
 
     response = openai.ChatCompletion.create(
         model="gpt-3.5-turbo",
@@ -109,4 +134,4 @@ def summarize_text(text, is_website = True):
     )
 
     final_summary = response.choices[0].message.content
-    return final_summary
+    return final_summary

scripts/chat.py

@@ -5,6 +5,7 @@
 # Initialize the OpenAI API client
 openai.api_key = keys.OPENAI_API_KEY
 
+
 def create_chat_message(role, content):
     """
     Create a chat message with the given role and content.
@@ -18,7 +19,14 @@ def create_chat_message(role, content):
     """
     return {"role": role, "content": content}
 
-def chat_with_ai(prompt, user_input, full_message_history, permanent_memory, token_limit, debug = False):
+
+def chat_with_ai(
+        prompt,
+        user_input,
+        full_message_history,
+        permanent_memory,
+        token_limit,
+        debug=False):
     while True:
         try:
             """
@@ -34,8 +42,12 @@ def chat_with_ai(prompt, user_input, full_message_history, permanent_memory, tok
             Returns:
             str: The AI's response.
             """
-            current_context = [create_chat_message("system", prompt), create_chat_message("system", f"Permanent memory: {permanent_memory}")]
-            current_context.extend(full_message_history[-(token_limit - len(prompt) - len(permanent_memory) - 10):])
+            current_context = [
+                create_chat_message(
+                    "system", prompt), create_chat_message(
+                    "system", f"Permanent memory: {permanent_memory}")]
+            current_context.extend(
+                full_message_history[-(token_limit - len(prompt) - len(permanent_memory) - 10):])
             current_context.extend([create_chat_message("user", user_input)])
 
             # Debug print the current context
@@ -45,7 +57,8 @@ def chat_with_ai(prompt, user_input, full_message_history, permanent_memory, tok
                     # Skip printing the prompt
                     if message["role"] == "system" and message["content"] == prompt:
                         continue
-                    print(f"{message['role'].capitalize()}: {message['content']}")
+                    print(
+                        f"{message['role'].capitalize()}: {message['content']}")
                 print("----------- END OF CONTEXT ----------------")
 
             response = openai.ChatCompletion.create(
@@ -56,8 +69,12 @@ def chat_with_ai(prompt, user_input, full_message_history, permanent_memory, tok
             assistant_reply = response.choices[0].message["content"]
 
             # Update full message history
-            full_message_history.append(create_chat_message("user", user_input))
-            full_message_history.append(create_chat_message("assistant", assistant_reply))
+            full_message_history.append(
+                create_chat_message(
+                    "user", user_input))
+            full_message_history.append(
+                create_chat_message(
+                    "assistant", assistant_reply))
 
             return assistant_reply
         except openai.RateLimitError: