Tides up codebase.

Extracts python functions to relevant files.

AutonomousAI/browse.py

@@ -0,0 +1,89 @@
+from googlesearch import search
+import requests
+from bs4 import BeautifulSoup
+from readability import Document#
+import openai
+
+def scrape_text(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    text = soup.get_text()
+    lines = (line.strip() for line in text.splitlines())
+    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+    text = '\n'.join(chunk for chunk in chunks if chunk)
+
+    return text
+
+def scrape_main_content(url):
+    response = requests.get(url)
+
+    # Try using Readability
+    doc = Document(response.text)
+    content = doc.summary()
+    soup = BeautifulSoup(content, "html.parser")
+    text = soup.get_text('\n', strip=True)
+
+    # Check if Readability provided a satisfactory result (e.g., a minimum length)
+    # min_length = 50
+    # if len(text) < min_length:
+    #     # Fallback to the custom function
+    #     text = scrape_main_content_custom(response.text)
+
+    return text
+
+def split_text(text, max_length=8192):
+    paragraphs = text.split("\n")
+    current_length = 0
+    current_chunk = []
+
+    for paragraph in paragraphs:
+        if current_length + len(paragraph) + 1 <= max_length:
+            current_chunk.append(paragraph)
+            current_length += len(paragraph) + 1
+        else:
+            yield "\n".join(current_chunk)
+            current_chunk = [paragraph]
+            current_length = len(paragraph) + 1
+
+    if current_chunk:
+        yield "\n".join(current_chunk)
+
+def summarize_text(text):
+    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) + " / " + str(len(chunks)))
+        messages = [{"role": "user", "content": "Please summarize the following text, focusing on extracting concise knowledge: " + chunk},]
+
+        response= openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            max_tokens=300,
+        )
+
+        summary = response.choices[0].message.content
+        summaries.append(summary)
+    print("Summarized " + str(len(chunks)) + " chunks.")
+
+    combined_summary = "\n".join(summaries)
+
+    # Summarize the combined summary
+    messages = [{"role": "user", "content": "Please summarize the following text, focusing on extracting concise knowledge: " + combined_summary},]
+
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=messages,
+        max_tokens=300,
+    )
+
+    final_summary = response.choices[0].message.content
+    return final_summary

AutonomousAI/commands.py

@@ -0,0 +1,76 @@
+import browse
+import json
+import memory as mem
+
+### Implemented Commands: ###
+
+def google_search(query, num_results = 3):
+    search_results = []
+    for j in browse.search(query, num_results=num_results):
+        search_results.append(j)
+    
+    return json.dumps(search_results, ensure_ascii=False, indent=4)
+
+def transcribe_summarise(url):
+    text = mem.scrape_main_content(url)
+    summary = mem.summarize_text(text)
+    return """ "Result" : """ + summary
+
+def check_news(source):
+    print("Checking news from BBC world instead of " + source)
+    _text= transcribe_summarise("https://www.bbc.com/news/world")
+    return _text
+
+def commit_memory(string):
+    _text = "Committing memory with string " + string
+    mem.permanent_memory.append(string)
+    print(_text)
+    return _text
+
+def delete_memory(key):
+    if key >= 0 and key < len(mem.permanent_memory):
+        _text = "Deleting memory with key " + str(key)
+        del mem.permanent_memory[key]
+        print(_text)
+        return _text
+    else:
+        print("Invalid key, cannot delete memory.")
+        return None
+    
+def overwrite_memory(key, string):
+    if key >= 0 and key < len(mem.permanent_memory):
+        _text = "Overwriting memory with key " + str(key) + " and string " + string
+        mem.permanent_memory[key] = string
+        print(_text)
+        return _text
+    else:
+        print("Invalid key, cannot overwrite memory.")
+        return None
+    
+
+### TODO: Not Yet Implemented: ###
+
+def start_instance(name, prompt):
+    _text = "Starting instance with name " + name + " and prompt " + prompt
+    print(_text)
+    return "Command not implemented yet."
+
+def manage_instances(action):
+    _text = "Managing instances with action " + action
+    print(_text)
+    return _text
+
+def navigate_website(action, username):
+    _text = "Navigating website with action " + action + " and username " + username
+    print(_text)
+    return "Command not implemented yet."
+
+def register_account(username, website):
+    _text = "Registering account with username " + username + " and website " + website
+    print(_text)
+    return "Command not implemented yet."
+
+def check_notifications(website):
+    _text = "Checking notifications from " + website
+    print(_text)
+    return "Command not implemented yet."

AutonomousAI/main.py

@@ -1,11 +1,9 @@
 import datetime
 import openai
 import json
-from googlesearch import search
-import requests
-from bs4 import BeautifulSoup
 import keys
-from readability import Document
+import commands as cmd
+import memory as mem
 
 # Initialize the OpenAI API client
 openai.api_key = keys.OPENAI_API_KEY
@@ -13,90 +11,6 @@ openai.api_key = keys.OPENAI_API_KEY
 def get_datetime():
     return "Current date and time: " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
-def scrape_text(url):
-    response = requests.get(url)
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    for script in soup(["script", "style"]):
-        script.extract()
-
-    text = soup.get_text()
-    lines = (line.strip() for line in text.splitlines())
-    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-    text = '\n'.join(chunk for chunk in chunks if chunk)
-
-    return text
-
-def scrape_main_content(url):
-    response = requests.get(url)
-
-    # Try using Readability
-    doc = Document(response.text)
-    content = doc.summary()
-    soup = BeautifulSoup(content, "html.parser")
-    text = soup.get_text('\n', strip=True)
-
-    # Check if Readability provided a satisfactory result (e.g., a minimum length)
-    # min_length = 50
-    # if len(text) < min_length:
-    #     # Fallback to the custom function
-    #     text = scrape_main_content_custom(response.text)
-
-    return text
-
-def split_text(text, max_length=8192):
-    paragraphs = text.split("\n")
-    current_length = 0
-    current_chunk = []
-
-    for paragraph in paragraphs:
-        if current_length + len(paragraph) + 1 <= max_length:
-            current_chunk.append(paragraph)
-            current_length += len(paragraph) + 1
-        else:
-            yield "\n".join(current_chunk)
-            current_chunk = [paragraph]
-            current_length = len(paragraph) + 1
-
-    if current_chunk:
-        yield "\n".join(current_chunk)
-
-def summarize_text(text):
-    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) + " / " + str(len(chunks)))
-        messages = [{"role": "user", "content": "Please summarize the following text, focusing on extracting concise knowledge: " + chunk},]
-
-        response= openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=messages,
-            max_tokens=300,
-        )
-
-        summary = response.choices[0].message.content
-        summaries.append(summary)
-    print("Summarized " + str(len(chunks)) + " chunks.")
-
-    combined_summary = "\n".join(summaries)
-
-    # Summarize the combined summary
-    messages = [{"role": "user", "content": "Please summarize the following text, focusing on extracting concise knowledge: " + combined_summary},]
-
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=messages,
-        max_tokens=300,
-    )
-
-    final_summary = response.choices[0].message.content
-    return final_summary
-
 def create_chat_message(role, content):
     """
     Create a chat message with the given role and content.
@@ -154,27 +68,27 @@ def execute_command(response):
         arguments = command["args"]
 
         if command_name == "google":
-            return google_search(arguments["input"])
+            return cmd.google_search(arguments["input"])
         elif command_name == "check_news":
-            return check_news(arguments["source"])
+            return cmd.check_news(arguments["source"])
         elif command_name == "check_notifications":
-            return check_notifications(arguments["website"])
+            return cmd.check_notifications(arguments["website"])
         elif command_name == "memory_add":
-            return commit_memory(arguments["string"])
+            return cmd.commit_memory(arguments["string"])
         elif command_name == "memory_del":
-            return delete_memory(arguments["key"])
+            return cmd.delete_memory(arguments["key"])
         elif command_name == "memory_ovr":
-            return overwrite_memory(arguments["key"], arguments["string"])
+            return cmd.overwrite_memory(arguments["key"], arguments["string"])
         elif command_name == "start_instance":
-            return start_instance(arguments["name"], arguments["prompt"])
+            return cmd.start_instance(arguments["name"], arguments["prompt"])
         elif command_name == "manage_instances":
-            return manage_instances(arguments["action"])
+            return cmd.manage_instances(arguments["action"])
         elif command_name == "navigate_website":
-            return navigate_website(arguments["action"], arguments["username"])
+            return cmd.navigate_website(arguments["action"], arguments["username"])
         elif command_name == "register_account":
-            return register_account(arguments["username"], arguments["website"])
+            return cmd.register_account(arguments["username"], arguments["website"])
         elif command_name == "transcribe_summarise":
-            return transcribe_summarise(arguments["url"])
+            return cmd.transcribe_summarise(arguments["url"])
         else:
             return f"unknown command {command_name}"
     except json.decoder.JSONDecodeError:
@@ -183,77 +97,8 @@ def execute_command(response):
     except Exception as e:
         return "Error: " + str(e)
 
-def google_search(query, num_results = 3):
-    search_results = []
-    for j in search(query, num_results=num_results):
-        search_results.append(j)
-    
-    return json.dumps(search_results, ensure_ascii=False, indent=4)
-
-def check_news(source):
-    print("Checking news from BBC world instead of " + source)
-    _text= transcribe_summarise("https://www.bbc.com/news/world")
-    return _text
-
-def check_notifications(website):
-    _text = "Checking notifications from " + website
-    print(_text)
-    return _text
-
-def commit_memory(string):
-    _text = "Committing memory with string " + string
-    permanent_memory.append(string)
-    print(_text)
-    return _text
-
-def delete_memory(key):
-    if key >= 0 and key < len(permanent_memory):
-        _text = "Deleting memory with key " + str(key)
-        del permanent_memory[key]
-        print(_text)
-        return _text
-    else:
-        print("Invalid key, cannot delete memory.")
-        return None
-
-def overwrite_memory(key, string):
-    if key >= 0 and key < len(permanent_memory):
-        _text = "Overwriting memory with key " + str(key) + " and string " + string
-        permanent_memory[key] = string
-        print(_text)
-        return _text
-    else:
-        print("Invalid key, cannot overwrite memory.")
-        return None
-    
-def start_instance(name, prompt):
-    _text = "Starting instance with name " + name + " and prompt " + prompt
-    print(_text)
-    return _text
-
-def manage_instances(action):
-    _text = "Managing instances with action " + action
-    print(_text)
-    return _text
-
-def navigate_website(action, username):
-    _text = "Navigating website with action " + action + " and username " + username
-    print(_text)
-    return _text
-
-def register_account(username, website):
-    _text = "Registering account with username " + username + " and website " + website
-    print(_text)
-    return _text
-
-def transcribe_summarise(url):
-    text = scrape_main_content(url)
-    summary = summarize_text(text)
-    return """ "Result" : """ + summary
-
 # Initialize variables
 full_message_history = []
-permanent_memory = []
 prompt = """You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
 
 GOALS:
@@ -341,7 +186,7 @@ while True:
         full_message_history.append(create_chat_message("system", result))
         print("system: " + result)
 
-    assistant_reply = chat_with_ai(prompt, user_input, full_message_history, permanent_memory, token_limit)
+    assistant_reply = chat_with_ai(prompt, user_input, full_message_history, mem.permanent_memory, token_limit)
     print(f"Assistant: {assistant_reply}")
     print("-------------------------")
 

AutonomousAI/memory.py

@@ -0,0 +1 @@
+permanent_memory = []