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from pathlib import Path
import yaml
from pydantic import BaseModel, Field
class AIProfile(BaseModel):
"""
Object to hold the AI's personality.
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.
api_budget (float): The maximum dollar value for API calls (0.0 means infinite)
"""
ai_name: str = ""
ai_role: str = ""
ai_goals: list[str] = Field(default_factory=list[str])
api_budget: float = 0.0
@staticmethod
def load(ai_settings_file: str | Path) -> "AIProfile":
"""
Returns class object with parameters (ai_name, ai_role, ai_goals, api_budget)
loaded from yaml file if it exists, else returns class with no parameters.
Parameters:
ai_settings_file (Path): The path to the config yaml file.
Returns:
cls (object): An instance of given cls object
"""
try:
with open(ai_settings_file, encoding="utf-8") as file:
config_params = yaml.load(file, Loader=yaml.SafeLoader) or {}
except FileNotFoundError:
config_params = {}
ai_name = config_params.get("ai_name", "")
ai_role = config_params.get("ai_role", "")
ai_goals = [
str(goal).strip("{}").replace("'", "").replace('"', "")
if isinstance(goal, dict)
else str(goal)
for goal in config_params.get("ai_goals", [])
]
api_budget = config_params.get("api_budget", 0.0)
return AIProfile(
ai_name=ai_name, ai_role=ai_role, ai_goals=ai_goals, api_budget=api_budget
)
def save(self, ai_settings_file: str | Path) -> None:
"""
Saves the class parameters to the specified file yaml file path as a yaml file.
Parameters:
ai_settings_file (Path): The path to the config yaml file.
Returns:
None
"""
with open(ai_settings_file, "w", encoding="utf-8") as file:
yaml.dump(self.dict(), file, allow_unicode=True)
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