deep-swarm/camel/utils/commons.py

# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
import importlib
import logging
import os
import platform
import re
import socket
import subprocess
import time
import zipfile
from functools import wraps
from http import HTTPStatus
from typing import (
    Any,
    Callable,
    Dict,
    List,
    Mapping,
    Optional,
    Set,
    Type,
    TypeVar,
    cast,
)
from urllib.parse import urlparse

import pydantic
import requests
from pydantic import BaseModel

from camel.logger import get_logger
from camel.types import TaskType

from .constants import Constants

F = TypeVar('F', bound=Callable[..., Any])

logger = get_logger(__name__)


def print_text_animated(
    text, delay: float = 0.02, end: str = "", log_level: int = logging.INFO
):
    r"""Prints the given text with an animated effect.

    Args:
        text (str): The text to print.
        delay (float, optional): The delay between each character printed.
            (default: :obj:`0.02`)
        end (str, optional): The end character to print after each
            character of text. (default: :obj:`""`)
        log_level (int, optional): The log level to use.
            See https://docs.python.org/3/library/logging.html#levels
            (default: :obj:`logging.INFO`)
    """
    if logger.isEnabledFor(log_level):
        # timestamp and other prefixes
        logger.log(log_level, '')

        for char in text:
            print(char, end=end, flush=True)
            time.sleep(delay)
        # Close the log entry
        logger.log(log_level, '')
    else:
        # This may be relevant for logging frameworks
        logger.log(log_level, text)


def get_prompt_template_key_words(template: str) -> Set[str]:
    r"""Given a string template containing curly braces {}, return a set of
    the words inside the braces.

    Args:
        template (str): A string containing curly braces.

    Returns:
        List[str]: A list of the words inside the curly braces.

    Example:
        >>> get_prompt_template_key_words('Hi, {name}! How are you {status}?')
        {'name', 'status'}
    """
    return set(re.findall(r'{([^}]*)}', template))


def get_first_int(string: str) -> Optional[int]:
    r"""Returns the first integer number found in the given string.

    If no integer number is found, returns None.

    Args:
        string (str): The input string.

    Returns:
        int or None: The first integer number found in the string, or None if
            no integer number is found.
    """
    match = re.search(r'\d+', string)
    if match:
        return int(match.group())
    else:
        return None


def download_tasks(task: TaskType, folder_path: str) -> None:
    r"""Downloads task-related files from a specified URL and extracts them.

    This function downloads a zip file containing tasks based on the specified
    `task` type from a predefined URL, saves it to `folder_path`, and then
    extracts the contents of the zip file into the same folder. After
    extraction, the zip file is deleted.

    Args:
        task (TaskType): An enum representing the type of task to download.
        folder_path (str): The path of the folder where the zip file will be
                           downloaded and extracted.
    """
    # Define the path to save the zip file
    zip_file_path = os.path.join(folder_path, "tasks.zip")

    # Download the zip file from the Google Drive link
    response = requests.get(
        "https://huggingface.co/datasets/camel-ai/"
        f"metadata/resolve/main/{task.value}_tasks.zip"
    )

    # Save the zip file
    with open(zip_file_path, "wb") as f:
        f.write(response.content)

    with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
        zip_ref.extractall(folder_path)

    # Delete the zip file
    os.remove(zip_file_path)


def get_task_list(task_response: str) -> List[str]:
    r"""Parse the response of the Agent and return task list.

    Args:
        task_response (str): The string response of the Agent.

    Returns:
        List[str]: A list of the string tasks.
    """

    new_tasks_list = []
    task_string_list = task_response.strip().split('\n')
    # each task starts with #.
    for task_string in task_string_list:
        task_parts = task_string.strip().split(".", 1)
        if len(task_parts) == 2:
            task_id = ''.join(s for s in task_parts[0] if s.isnumeric())
            task_name = re.sub(r'[^\w\s_]+', '', task_parts[1]).strip()
            if task_name.strip() and task_id.isnumeric():
                new_tasks_list.append(task_name)
    return new_tasks_list


def check_server_running(server_url: str) -> bool:
    r"""Check whether the port refered by the URL to the server
    is open.

    Args:
        server_url (str): The URL to the server running LLM inference
            service.

    Returns:
        bool: Whether the port is open for packets (server is running).
    """
    parsed_url = urlparse(server_url)
    url_tuple = (parsed_url.hostname, parsed_url.port)

    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    result = sock.connect_ex(url_tuple)
    sock.close()

    # if the port is open, the result should be 0.
    return result == 0


def dependencies_required(*required_modules: str) -> Callable[[F], F]:
    r"""A decorator to ensure that specified Python modules
    are available before a function executes.

    Args:
        required_modules (str): The required modules to be checked for
            availability.

    Returns:
        Callable[[F], F]: The original function with the added check for
            required module dependencies.

    Raises:
        ImportError: If any of the required modules are not available.

    Example:
        ::

            @dependencies_required('numpy', 'pandas')
            def data_processing_function():
                # Function implementation...
    """

    def decorator(func: F) -> F:
        @wraps(func)
        def wrapper(*args: Any, **kwargs: Any) -> Any:
            missing_modules = [
                m for m in required_modules if not is_module_available(m)
            ]
            if missing_modules:
                raise ImportError(
                    f"Missing required modules: {', '.join(missing_modules)}"
                )
            return func(*args, **kwargs)

        return cast(F, wrapper)

    return decorator


def is_module_available(module_name: str) -> bool:
    r"""Check if a module is available for import.

    Args:
        module_name (str): The name of the module to check for availability.

    Returns:
        bool: True if the module can be imported, False otherwise.
    """
    try:
        importlib.import_module(module_name)
        return True
    except ImportError:
        return False


def api_keys_required(*required_keys: str) -> Callable[[F], F]:
    r"""A decorator to check if the required API keys are
    presented in the environment variables or as an instance attribute.

    Args:
        required_keys (str): The required API keys to be checked.

    Returns:
        Callable[[F], F]: The original function with the added check
            for required API keys.

    Raises:
        ValueError: If any of the required API keys are missing in the
            environment variables and the instance attribute.

    Example:
        ::

            @api_keys_required('API_KEY_1', 'API_KEY_2')
            def some_api_function():
                # Function implementation...
    """

    def decorator(func: F) -> F:
        @wraps(func)
        def wrapper(*args: Any, **kwargs: Any) -> Any:
            missing_environment_keys = [
                k for k in required_keys if k not in os.environ
            ]
            if (
                not (args and getattr(args[0], '_api_key', None))
                and missing_environment_keys
            ):
                raise ValueError(
                    f"Missing API keys: {', '.join(missing_environment_keys)}"
                )
            return func(*args, **kwargs)

        return cast(F, wrapper)

    return decorator


def get_system_information():
    r"""Gathers information about the operating system.

    Returns:
        dict: A dictionary containing various pieces of OS information.
    """
    sys_info = {
        "OS Name": os.name,
        "System": platform.system(),
        "Release": platform.release(),
        "Version": platform.version(),
        "Machine": platform.machine(),
        "Processor": platform.processor(),
        "Platform": platform.platform(),
    }

    return sys_info


def to_pascal(snake: str) -> str:
    """Convert a snake_case string to PascalCase.

    Args:
        snake (str): The snake_case string to be converted.

    Returns:
        str: The converted PascalCase string.
    """
    # Check if the string is already in PascalCase
    if re.match(r'^[A-Z][a-zA-Z0-9]*([A-Z][a-zA-Z0-9]*)*$', snake):
        return snake
    # Remove leading and trailing underscores
    snake = snake.strip('_')
    # Replace multiple underscores with a single one
    snake = re.sub('_+', '_', snake)
    # Convert to PascalCase
    return re.sub(
        '_([0-9A-Za-z])',
        lambda m: m.group(1).upper(),
        snake.title(),
    )


def get_pydantic_major_version() -> int:
    r"""Get the major version of Pydantic.

    Returns:
        int: The major version number of Pydantic if installed, otherwise 0.
    """
    try:
        return int(pydantic.__version__.split(".")[0])
    except ImportError:
        return 0


def get_pydantic_object_schema(pydantic_params: Type[BaseModel]) -> Dict:
    r"""Get the JSON schema of a Pydantic model.

    Args:
        pydantic_params (Type[BaseModel]): The Pydantic model class to retrieve
            the schema for.

    Returns:
        dict: The JSON schema of the Pydantic model.
    """
    return pydantic_params.model_json_schema()


def func_string_to_callable(code: str):
    r"""Convert a function code string to a callable function object.

    Args:
        code (str): The function code as a string.

    Returns:
        Callable[..., Any]: The callable function object extracted from the
            code string.
    """
    local_vars: Mapping[str, object] = {}
    exec(code, globals(), local_vars)
    func = local_vars.get(Constants.FUNC_NAME_FOR_STRUCTURED_OUTPUT)
    return func


def json_to_function_code(json_obj: Dict) -> str:
    r"""Generate a Python function code from a JSON schema.

    Args:
        json_obj (dict): The JSON schema object containing properties and
            required fields, and json format is follow openai tools schema

    Returns:
        str: The generated Python function code as a string.
    """
    properties = json_obj.get('properties', {})
    required = json_obj.get('required', [])

    if not properties or not required:
        raise ValueError(
            "JSON schema must contain 'properties' and 'required' fields"
        )

    args = []
    docstring_args = []
    return_keys = []

    prop_to_python = {
        'string': 'str',
        'number': 'float',
        'integer': 'int',
        'boolean': 'bool',
    }

    for prop in required:
        description = properties[prop]['description']
        prop_type = properties[prop]['type']
        python_type = prop_to_python.get(prop_type, prop_type)
        args.append(f"{prop}: {python_type}")
        docstring_args.append(
            f"        {prop} ({python_type}): {description}."
        )
        return_keys.append(prop)

    # extract entity of schema
    args_str = ", ".join(args)
    docstring_args_str = "\n".join(docstring_args)
    return_keys_str = ", ".join(return_keys)

    # function template
    function_code = f'''
def {Constants.FUNC_NAME_FOR_STRUCTURED_OUTPUT}({args_str}):
    r"""Return response with a specified json format.
    Args:
{docstring_args_str}
    Returns:
        Dict: A dictionary containing {return_keys_str}.
    """
    return {{{", ".join([f'"{prop}": {prop}' for prop in required])}}}
    '''

    return function_code


def text_extract_from_web(url: str) -> str:
    r"""Get the text information from given url.

    Args:
        url (str): The website you want to search.

    Returns:
        str: All texts extract from the web.
    """
    try:
        import requests
        from newspaper import Article

        # Request the target page
        article = Article(url)
        article.download()
        article.parse()
        text = article.text

    except requests.RequestException as e:
        text = f"Can't access {url}, error: {e}"

    except Exception as e:
        text = f"Can't extract text from {url}, error: {e}"

    return text


def create_chunks(text: str, n: int) -> List[str]:
    r"""Returns successive n-sized chunks from provided text. Split a text
    into smaller chunks of size n".

    Args:
        text (str): The text to be split.
        n (int): The max length of a single chunk.

    Returns:
        List[str]: A list of split texts.
    """

    chunks = []
    i = 0
    while i < len(text):
        # Find the nearest end of sentence within a range of 0.5 * n
        # and 1.5 * n tokens
        j = min(i + int(1.2 * n), len(text))
        while j > i + int(0.8 * n):
            # Decode the tokens and check for full stop or newline
            chunk = text[i:j]
            if chunk.endswith(".") or chunk.endswith("\n"):
                break
            j -= 1
        # If no end of sentence found, use n tokens as the chunk size
        if j == i + int(0.8 * n):
            j = min(i + n, len(text))
        chunks.append(text[i:j])
        i = j
    return chunks


def is_docker_running() -> bool:
    r"""Check if the Docker daemon is running.

    Returns:
        bool: True if the Docker daemon is running, False otherwise.
    """
    try:
        result = subprocess.run(
            ["docker", "info"],
            check=True,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
        )
        return result.returncode == 0
    except (subprocess.CalledProcessError, FileNotFoundError):
        return False


try:
    if os.getenv("AGENTOPS_API_KEY") is not None:
        from agentops import (
            ToolEvent,
            record,
        )
    else:
        raise ImportError
except (ImportError, AttributeError):
    ToolEvent = None


def agentops_decorator(func):
    r"""Decorator that records the execution of a function if ToolEvent is
    available.

    Parameters:
        func (callable): The function to be decorated.

    Returns:
        callable: The wrapped function which records its execution details.
    """

    @wraps(func)
    def wrapper(*args, **kwargs):
        if ToolEvent:
            tool_event = ToolEvent(name=func.__name__, params=kwargs)
            result = func(*args, **kwargs)
            tool_event.returns = result
            record(tool_event)
            return result
        return func(*args, **kwargs)

    return wrapper


class AgentOpsMeta(type):
    r"""Metaclass that automatically decorates all callable attributes with
    the agentops_decorator,
    except for the 'get_tools' method.

    Methods:
    __new__(cls, name, bases, dct):
        Creates a new class with decorated methods.
    """

    def __new__(cls, name, bases, dct):
        if ToolEvent:
            for attr, value in dct.items():
                if callable(value) and attr != 'get_tools':
                    dct[attr] = agentops_decorator(value)
        return super().__new__(cls, name, bases, dct)


def track_agent(*args, **kwargs):
    r"""Mock track agent decorator for AgentOps."""

    def noop(f):
        return f

    return noop


def handle_http_error(response: requests.Response) -> str:
    r"""Handles the HTTP errors based on the status code of the response.

    Args:
        response (requests.Response): The HTTP response from the API call.

    Returns:
        str: The error type, based on the status code.
    """
    if response.status_code == HTTPStatus.UNAUTHORIZED:
        return "Unauthorized. Check your access token."
    elif response.status_code == HTTPStatus.FORBIDDEN:
        return "Forbidden. You do not have permission to perform this action."
    elif response.status_code == HTTPStatus.NOT_FOUND:
        return "Not Found. The resource could not be located."
    elif response.status_code == HTTPStatus.TOO_MANY_REQUESTS:
        return "Too Many Requests. You have hit the rate limit."
    else:
        return "HTTP Error"


def retry_request(
    func: Callable, retries: int = 3, delay: int = 1, *args: Any, **kwargs: Any
) -> Any:
    r"""Retries a function in case of any errors.

    Args:
        func (Callable): The function to be retried.
        retries (int): Number of retry attempts. (default: :obj:`3`)
        delay (int): Delay between retries in seconds. (default: :obj:`1`)
        *args: Arguments to pass to the function.
        **kwargs: Keyword arguments to pass to the function.

    Returns:
        Any: The result of the function call if successful.

    Raises:
        Exception: If all retry attempts fail.
    """
    for attempt in range(retries):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            print(f"Attempt {attempt + 1}/{retries} failed: {e}")
            if attempt < retries - 1:
                time.sleep(delay)
            else:
                raise