Significant-Gravitas
/
AutoGPT
mirror of https://github.com/Significant-Gravitas/AutoGPT.git
1
0
Fork 1
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'master' into fix-user-feedback-json-error

pull/743/head
Wladastic 2023-04-10 19:45:39 +02:00
parent bb36634b46 4d42e14d3d
commit 1f9d66b745
26 changed files with 314 additions and 144 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@ -10,4 +10,5 @@ auto_gpt_workspace/*
venv/*
outputs/*
ai_settings.yaml
.vscode
auto-gpt.json

1
Dockerfile
View File

@ -2,6 +2,7 @@ FROM python:3.11
WORKDIR /app
COPY scripts/ /app
COPY requirements.txt /app
RUN pip install -r requirements.txt

19
README.md
View File

@ -81,7 +81,7 @@ git clone https://github.com/Torantulino/Auto-GPT.git
2. Navigate to the project directory:
*(Type this into your CMD window, you're aiming to navigate the CMD window to the repository you just downloaded)*
```
$ cd 'Auto-GPT'
cd 'Auto-GPT'
```
3. Install the required dependencies:
@ -92,7 +92,7 @@ pip install -r requirements.txt
4. Rename `.env.template` to `.env` and fill in your `OPENAI_API_KEY`. If you plan to use Speech Mode, fill in your `ELEVEN_LABS_API_KEY` as well.
- Obtain your OpenAI API key from: https://platform.openai.com/account/api-keys.
- Obtain your ElevenLabs API key from: https://elevenlabs.io. You can view your xi-api-key using the "Profile" tab on the website.
- Obtain your ElevenLabs API key from: https://beta.elevenlabs.io. You can view your xi-api-key using the "Profile" tab on the website.
- If you want to use GPT on an Azure instance, set `USE_AZURE` to `True` and provide the `OPENAI_API_BASE`, `OPENAI_API_VERSION` and `OPENAI_DEPLOYMENT_ID` values as explained here: https://pypi.org/project/openai/ in the `Microsoft Azure Endpoints` section
## 🔧 Usage
@ -114,7 +114,7 @@ python scripts/main.py --speak
## 🔍 Google API Keys Configuration
This section is optional, use the official google api if you are having issues with error 429 when running google search.
This section is optional, use the official google api if you are having issues with error 429 when running a google search.
To use the `google_official_search` command, you need to set up your Google API keys in your environment variables.
1. Go to the [Google Cloud Console](https://console.cloud.google.com/).
@ -127,6 +127,8 @@ To use the `google_official_search` command, you need to set up your Google API
8. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
9. Once you've created your search engine, click on "Control Panel" and then "Basics". Copy the "Search engine ID" and set it as an environment variable named `CUSTOM_SEARCH_ENGINE_ID` on your machine. See setting up environment variables below.
*Remember that your free daily custom search quota allows only up to 100 searches. To increase this limit, you need to assign a billing account to the project to profit from up to 10K daily searches.*
### Setting up environment variables
For Windows Users:
```
@ -185,10 +187,15 @@ are loaded for the agent at any given time.
3. Find your API key and region under the default project in the left sidebar.
### Setting up environment variables
For Windows Users:
Simply set them in the `.env` file.
Alternatively, you can set them from the command line (advanced):
For Windows Users:
```
setx PINECONE_API_KEY "YOUR_PINECONE_API_KEY"
export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp
setx PINECONE_ENV "Your pinecone region" # something like: us-east4-gcp
```
For macOS and Linux users:
@ -198,7 +205,6 @@ export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp
```
Or you can set them in the `.env` file.
## View Memory Usage
@ -222,6 +228,7 @@ If you don't have access to the GPT4 api, this mode will allow you to use Auto-G
```
python scripts/main.py --gpt3only
```
It is recommended to use a virtual machine for tasks that require high security measures to prevent any potential harm to the main computer's system and data.
## 🖼 Image Generation
By default, Auto-GPT uses DALL-e for image generation. To use Stable Diffusion, a [HuggingFace API Token](https://huggingface.co/settings/tokens) is required.

4
scripts/agent_manager.py
View File

@ -7,6 +7,7 @@ agents = {} # key, (task, full_message_history, model)
# TODO: Centralise use of create_chat_completion() to globally enforce token limit
def create_agent(task, prompt, model):
"""Create a new agent and return its key"""
global next_key
global agents
@ -32,6 +33,7 @@ def create_agent(task, prompt, model):
def message_agent(key, message):
"""Send a message to an agent and return its response"""
global agents
task, messages, model = agents[int(key)]
@ -52,6 +54,7 @@ def message_agent(key, message):
def list_agents():
"""Return a list of all agents"""
global agents
# Return a list of agent keys and their tasks
@ -59,6 +62,7 @@ def list_agents():
def delete_agent(key):
"""Delete an agent and return True if successful, False otherwise"""
global agents
try:

62
scripts/ai_config.py
View File

@ -3,7 +3,27 @@ import data
import os
class AIConfig:
def __init__(self, ai_name="", ai_role="", ai_goals=[]):
"""
A class object that contains the configuration information for the AI
Attributes:
ai_name (str): The name of the AI.
ai_role (str): The description of the AI's role.
ai_goals (list): The list of objectives the AI is supposed to complete.
"""
def __init__(self, ai_name: str="", ai_role: str="", ai_goals: list=[]) -> None:
"""
Initialize a class instance
Parameters:
ai_name (str): The name of the AI.
ai_role (str): The description of the AI's role.
ai_goals (list): The list of objectives the AI is supposed to complete.
Returns:
None
"""
self.ai_name = ai_name
self.ai_role = ai_role
self.ai_goals = ai_goals
@ -12,8 +32,19 @@ class AIConfig:
SAVE_FILE = os.path.join(os.path.dirname(__file__), '..', 'ai_settings.yaml')
@classmethod
def load(cls, config_file=SAVE_FILE):
# Load variables from yaml file if it exists
def load(cls: object, config_file: str=SAVE_FILE) -> object:
"""
Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from yaml file if yaml file exists,
else returns class with no parameters.
Parameters:
cls (class object): An AIConfig Class object.
config_file (int): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
Returns:
cls (object): A instance of given cls object
"""
try:
with open(config_file) as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
@ -26,12 +57,32 @@ class AIConfig:
return cls(ai_name, ai_role, ai_goals)
def save(self, config_file=SAVE_FILE):
def save(self, config_file: str=SAVE_FILE) -> None:
"""
Saves the class parameters to the specified file yaml file path as a yaml file.
Parameters:
config_file(str): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
Returns:
None
"""
config = {"ai_name": self.ai_name, "ai_role": self.ai_role, "ai_goals": self.ai_goals}
with open(config_file, "w") as file:
yaml.dump(config, file)
def construct_full_prompt(self):
def construct_full_prompt(self) -> str:
"""
Returns a prompt to the user with the class information in an organized fashion.
Parameters:
None
Returns:
full_prompt (str): A string containing the intitial prompt for the user including the ai_name, ai_role and ai_goals.
"""
prompt_start = """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."""
# Construct full prompt
@ -41,3 +92,4 @@ class AIConfig:
full_prompt += f"\n\n{data.load_prompt()}"
return full_prompt

36
scripts/ai_functions.py
View File

@ -5,9 +5,17 @@ from call_ai_function import call_ai_function
from json_parser import fix_and_parse_json
cfg = Config()
# Evaluating code
def evaluate_code(code: str) -> List[str]:
"""
A function that takes in a string and returns a response from create chat completion api call.
Parameters:
code (str): Code to be evaluated.
Returns:
A result string from create chat completion. A list of suggestions to improve the code.
"""
function_string = "def analyze_code(code: str) -> List[str]:"
args = [code]
description_string = """Analyzes the given code and returns a list of suggestions for improvements."""
@ -17,9 +25,17 @@ def evaluate_code(code: str) -> List[str]:
return result_string
# Improving code
def improve_code(suggestions: List[str], code: str) -> str:
"""
A function that takes in code and suggestions and returns a response from create chat completion api call.
Parameters:
suggestions (List): A list of suggestions around what needs to be improved.
code (str): Code to be improved.
Returns:
A result string from create chat completion. Improved code in response.
"""
function_string = (
"def generate_improved_code(suggestions: List[str], code: str) -> str:"
)
@ -30,10 +46,18 @@ def improve_code(suggestions: List[str], code: str) -> str:
return result_string
# Writing tests
def write_tests(code: str, focus: List[str]) -> str:
"""
A function that takes in code and focus topics and returns a response from create chat completion api call.
Parameters:
focus (List): A list of suggestions around what needs to be improved.
code (str): Code for test cases to be generated against.
Returns:
A result string from create chat completion. Test cases for the submitted code in response.
"""
function_string = (
"def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
)
@ -42,5 +66,3 @@ def write_tests(code: str, focus: List[str]) -> str:
result_string = call_ai_function(function_string, args, description_string)
return result_string

7
scripts/browse.py
View File

@ -6,6 +6,7 @@ from llm_utils import create_chat_completion
cfg = Config()
def scrape_text(url):
"""Scrape text from a webpage"""
# Most basic check if the URL is valid:
if not url.startswith('http'):
return "Error: Invalid URL"
@ -33,6 +34,7 @@ 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']))
@ -40,6 +42,7 @@ def extract_hyperlinks(soup):
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})")
@ -47,6 +50,7 @@ def format_hyperlinks(hyperlinks):
def scrape_links(url):
"""Scrape links from a webpage"""
response = requests.get(url, headers=cfg.user_agent_header)
# Check if the response contains an HTTP error
@ -64,6 +68,7 @@ def scrape_links(url):
def split_text(text, max_length=8192):
"""Split text into chunks of a maximum length"""
paragraphs = text.split("\n")
current_length = 0
current_chunk = []
@ -82,12 +87,14 @@ def split_text(text, max_length=8192):
def create_message(chunk, question):
"""Create a message for the user to summarize a chunk of text"""
return {
"role": "user",
"content": f"\"\"\"{chunk}\"\"\" Using the above text, please answer the following question: \"{question}\" -- if the question cannot be answered using the text, please summarize the text."
}
def summarize_text(text, question):
"""Summarize text using the LLM model"""
if not text:
return "Error: No text to summarize"

3
scripts/call_ai_function.py
View File

@ -1,11 +1,12 @@
from config import Config
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=None):
"""Call an AI function"""
if model is None:
model = cfg.smart_llm_model
# For each arg, if any are None, convert to "None":

16
scripts/chat.py
View File

@ -3,11 +3,9 @@ import openai
from dotenv import load_dotenv
from config import Config
import token_counter
cfg = Config()
from llm_utils import create_chat_completion
cfg = Config()
def create_chat_message(role, content):
"""
@ -46,8 +44,8 @@ def chat_with_ai(
user_input,
full_message_history,
permanent_memory,
token_limit,
debug=False):
token_limit):
"""Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory."""
while True:
try:
"""
@ -65,13 +63,15 @@ def chat_with_ai(
"""
model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
# Reserve 1000 tokens for the response
if debug:
if cfg.debug:
print(f"Token limit: {token_limit}")
send_token_limit = token_limit - 1000
relevant_memory = permanent_memory.get_relevant(str(full_message_history[-5:]), 10)
if debug:
if cfg.debug:
print('Memory Stats: ', permanent_memory.get_stats())
next_message_to_add_index, current_tokens_used, insertion_index, current_context = generate_context(
@ -110,7 +110,7 @@ def chat_with_ai(
# assert tokens_remaining >= 0, "Tokens remaining is negative. This should never happen, please submit a bug report at https://www.github.com/Torantulino/Auto-GPT"
# Debug print the current context
if debug:
if cfg.debug:
print(f"Token limit: {token_limit}")
print(f"Send Token Count: {current_tokens_used}")
print(f"Tokens remaining for response: {tokens_remaining}")

16
scripts/commands.py
View File

@ -25,6 +25,7 @@ def is_valid_int(value):
return False
def get_command(response):
"""Parse the response and return the command name and arguments"""
try:
response_json = fix_and_parse_json(response)
@ -53,6 +54,7 @@ def get_command(response):
def execute_command(command_name, arguments):
"""Execute the command and return the result"""
memory = get_memory(cfg)
try:
@ -118,11 +120,13 @@ def execute_command(command_name, arguments):
def get_datetime():
"""Return the current date and time"""
return "Current date and time: " + \
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
def google_search(query, num_results=8):
"""Return the results of a google search"""
search_results = []
for j in ddg(query, max_results=num_results):
search_results.append(j)
@ -130,6 +134,7 @@ def google_search(query, num_results=8):
return json.dumps(search_results, ensure_ascii=False, indent=4)
def google_official_search(query, num_results=8):
"""Return the results of a google search using the official Google API"""
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
import json
@ -165,6 +170,7 @@ def google_official_search(query, num_results=8):
return search_results_links
def browse_website(url, question):
"""Browse a website and return the summary and links"""
summary = get_text_summary(url, question)
links = get_hyperlinks(url)
@ -178,23 +184,27 @@ def browse_website(url, question):
def get_text_summary(url, question):
"""Return the results of a google search"""
text = browse.scrape_text(url)
summary = browse.summarize_text(text, question)
return """ "Result" : """ + summary
def get_hyperlinks(url):
"""Return the results of a google search"""
link_list = browse.scrape_links(url)
return link_list
def commit_memory(string):
"""Commit a string to memory"""
_text = f"""Committing memory with string "{string}" """
mem.permanent_memory.append(string)
return _text
def delete_memory(key):
"""Delete a memory with a given key"""
if key >= 0 and key < len(mem.permanent_memory):
_text = "Deleting memory with key " + str(key)
del mem.permanent_memory[key]
@ -206,6 +216,7 @@ def delete_memory(key):
def overwrite_memory(key, string):
"""Overwrite a memory with a given key and string"""
# Check if the key is a valid integer
if is_valid_int(key):
key_int = int(key)
@ -232,11 +243,13 @@ def overwrite_memory(key, string):
def shutdown():
"""Shut down the program"""
print("Shutting down...")
quit()
def start_agent(name, task, prompt, model=cfg.fast_llm_model):
"""Start an agent with a given name, task, and prompt"""
global cfg
# Remove underscores from name
@ -260,6 +273,7 @@ def start_agent(name, task, prompt, model=cfg.fast_llm_model):
def message_agent(key, message):
"""Message an agent with a given key and message"""
global cfg
# Check if the key is a valid integer
@ -278,10 +292,12 @@ def message_agent(key, message):
def list_agents():
"""List all agents"""
return agents.list_agents()
def delete_agent(key):
"""Delete an agent with a given key"""
result = agents.delete_agent(key)
if not result:
return f"Agent {key} does not exist."

20
scripts/config.py
View File

@ -14,6 +14,7 @@ class Singleton(abc.ABCMeta, type):
_instances = {}
def __call__(cls, *args, **kwargs):
"""Call method for the singleton metaclass."""
if cls not in cls._instances:
cls._instances[cls] = super(
Singleton, cls).__call__(
@ -31,10 +32,11 @@ class Config(metaclass=Singleton):
"""
def __init__(self):
"""Initialize the Config class"""
self.debug = False
self.continuous_mode = False
self.speak_mode = False
# TODO - make these models be self-contained, using langchain, so we can configure them once and call it good
self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
@ -77,40 +79,56 @@ class Config(metaclass=Singleton):
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_debug_mode(self, value: bool):
self.debug_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
def set_pinecone_api_key(self, value: str):
"""Set the Pinecone API key value."""
self.pinecone_api_key = value
def set_pinecone_region(self, value: str):
"""Set the Pinecone region value."""
self.pinecone_region = value
def set_debug_mode(self, value: bool):
"""Set the debug mode value."""
self.debug = value

1
scripts/data.py
View File

@ -2,6 +2,7 @@ import os
from pathlib import Path
def load_prompt():
"""Load the prompt from data/prompt.txt"""
try:
# get directory of this file:
file_dir = Path(__file__).parent

2
scripts/data/prompt.txt
View File

@ -24,7 +24,7 @@ COMMANDS:
18. Execute Python File: "execute_python_file", args: "file": "<file>"
19. Task Complete (Shutdown): "task_complete", args: "reason": "<reason>"
20. Generate Image: "generate_image", args: "prompt": "<prompt>"
21. Do Nothing; command name: "do_nothing", args: ""
21. Do Nothing: "do_nothing", args: ""
RESOURCES:

1
scripts/execute_code.py
View File

@ -3,6 +3,7 @@ import os
def execute_python_file(file):
"""Execute a Python file in a Docker container and return the output"""
workspace_folder = "auto_gpt_workspace"
print (f"Executing file '{file}' in workspace '{workspace_folder}'")

6
scripts/file_operations.py
View File

@ -4,11 +4,13 @@ import os.path
# Set a dedicated folder for file I/O
working_directory = "auto_gpt_workspace"
# Create the directory if it doesn't exist
if not os.path.exists(working_directory):
os.makedirs(working_directory)
def safe_join(base, *paths):
"""Join one or more path components intelligently."""
new_path = os.path.join(base, *paths)
norm_new_path = os.path.normpath(new_path)
@ -19,6 +21,7 @@ def safe_join(base, *paths):
def read_file(filename):
"""Read a file and return the contents"""
try:
filepath = safe_join(working_directory, filename)
with open(filepath, "r") as f:
@ -29,6 +32,7 @@ def read_file(filename):
def write_to_file(filename, text):
"""Write text to a file"""
try:
filepath = safe_join(working_directory, filename)
directory = os.path.dirname(filepath)
@ -42,6 +46,7 @@ def write_to_file(filename, text):
def append_to_file(filename, text):
"""Append text to a file"""
try:
filepath = safe_join(working_directory, filename)
with open(filepath, "a") as f:
@ -52,6 +57,7 @@ def append_to_file(filename, text):
def delete_file(filename):
"""Delete a file"""
try:
filepath = safe_join(working_directory, filename)
os.remove(filepath)

13
scripts/json_parser.py
View File

@ -30,6 +30,7 @@ def fix_and_parse_json(
json_str: str,
try_to_fix_with_gpt: bool = True
) -> Union[str, Dict[Any, Any]]:
"""Fix and parse JSON string"""
try:
json_str = json_str.replace('\t', '')
return json.loads(json_str)
@ -59,7 +60,8 @@ def fix_and_parse_json(
" 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, cfg.debug)
ai_fixed_json = fix_json(json_str, JSON_SCHEMA)
if ai_fixed_json != "failed":
return json.loads(ai_fixed_json)
else:
@ -71,13 +73,15 @@ def fix_and_parse_json(
raise e
def fix_json(json_str: str, schema: str, debug=False) -> str:
def fix_json(json_str: str, schema: str) -> str:
"""Fix the given JSON string to make it parseable and fully complient with the provided schema."""
# Try to fix the JSON using gpt:
function_string = "def fix_json(json_str: str, schema:str=None) -> str:"
args = [f"'''{json_str}'''", f"'''{schema}'''"]
description_string = "Fixes the provided JSON string to make it parseable"\
" and fully complient with the provided schema.\n If an object or"\
" field specifed in the schema isn't contained within the correct"\
" field specified in the schema isn't contained within the correct"\
" JSON, it is ommited.\n This function is brilliant at guessing"\
" when the format is incorrect."
@ -87,12 +91,13 @@ def fix_json(json_str: str, schema: str, debug=False) -> str:
result_string = call_ai_function(
function_string, args, description_string, model=cfg.fast_llm_model
)
if debug:
if cfg.debug:
print("------------ JSON FIX ATTEMPT ---------------")
print(f"Original JSON: {json_str}")
print("-----------")
print(f"Fixed JSON: {result_string}")
print("----------- END OF FIX ATTEMPT ----------------")
try:
json.loads(result_string) # just check the validity
return result_string

1
scripts/llm_utils.py
View File

@ -6,6 +6,7 @@ openai.api_key = cfg.openai_api_key
# Overly simple abstraction until we create something better
def create_chat_completion(messages, model=None, temperature=None, max_tokens=None)->str:
"""Create a chat completion using the OpenAI API"""
if cfg.use_azure:
response = openai.ChatCompletion.create(
deployment_id=cfg.openai_deployment_id,

25
scripts/main.py
View File

@ -17,6 +17,18 @@ import traceback
import yaml
import argparse
cfg = Config()
def check_openai_api_key():
"""Check if the OpenAI API key is set in config.py or as an environment variable."""
if not cfg.openai_api_key:
print(
Fore.RED +
"Please set your OpenAI API key in config.py or as an environment variable."
)
print("You can get your key from https://beta.openai.com/account/api-keys")
exit(1)
def print_to_console(
title,
@ -25,6 +37,7 @@ def print_to_console(
speak_text=False,
min_typing_speed=0.05,
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}")
@ -70,6 +83,7 @@ def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
return json_string
def print_assistant_thoughts(assistant_reply):
"""Prints the assistant's thoughts to the console"""
global ai_name
global cfg
try:
@ -137,7 +151,7 @@ def print_assistant_thoughts(assistant_reply):
def load_variables(config_file="config.yaml"):
# Load variables from yaml file if it exists
"""Load variables from yaml file if it exists, otherwise prompt the user for input"""
try:
with open(config_file) as file:
config = yaml.load(file, Loader=yaml.FullLoader)
@ -191,6 +205,7 @@ 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(
@ -219,6 +234,7 @@ Continue (y/n): """)
def prompt_user():
"""Prompt the user for input"""
ai_name = ""
# Construct the prompt
print_to_console(
@ -271,6 +287,7 @@ def prompt_user():
return config
def parse_arguments():
"""Parses the arguments passed to the script"""
global cfg
cfg.set_continuous_mode(False)
cfg.set_speak_mode(False)
@ -294,6 +311,10 @@ def parse_arguments():
print_to_console("Speak Mode: ", Fore.GREEN, "ENABLED")
cfg.set_speak_mode(True)
if args.debug:
print_to_console("Debug Mode: ", Fore.GREEN, "ENABLED")
cfg.set_debug_mode(True)
if args.gpt3only:
print_to_console("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
cfg.set_smart_llm_model(cfg.fast_llm_model)
@ -304,7 +325,7 @@ def parse_arguments():
# TODO: fill in llm values here
check_openai_api_key()
cfg = Config()
parse_arguments()
ai_name = ""

1
scripts/speak.py
View File

@ -15,6 +15,7 @@ tts_headers = {
}
def eleven_labs_speech(text, voice_index=0):
"""Speak text using elevenlabs.io's API"""
tts_url = "https://api.elevenlabs.io/v1/text-to-speech/{voice_id}".format(
voice_id=voices[voice_index])
formatted_message = {"text": text}

5
scripts/spinner.py
View File

@ -5,7 +5,9 @@ import time
class Spinner:
"""A simple spinner class"""
def __init__(self, message="Loading...", delay=0.1):
"""Initialize the spinner class"""
self.spinner = itertools.cycle(['-', '/', '|', '\\'])
self.delay = delay
self.message = message
@ -13,6 +15,7 @@ class Spinner:
self.spinner_thread = None
def spin(self):
"""Spin the spinner"""
while self.running:
sys.stdout.write(next(self.spinner) + " " + self.message + "\r")
sys.stdout.flush()
@ -20,11 +23,13 @@ class Spinner:
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
self.spinner_thread.join()
sys.stdout.write('\r' + ' ' * (len(self.message) + 2) + '\r')