manual_slop/src/personas.py

"""Personas module: Persona dataclass + PersonaManager CRUD.

Per module_taxonomy_refactor_20260627 Phase 3.4, the Persona dataclass
moved from src/models.py into this module. PersonaManager (the ops layer
that loads/saves Persona instances to TOML) was already here.
"""
from __future__ import annotations

import tomllib

from dataclasses import dataclass, field
from pathlib    import Path
from typing     import Dict, Any, Optional

from src        import paths
from src.type_aliases import Metadata


@dataclass
class Persona:
 name: str
 preferred_models: list[Metadata] = field(default_factory=list)
 system_prompt: str = ''
 tool_preset: Optional[str] = None
 bias_profile: Optional[str] = None
 context_preset: Optional[str] = None
 aggregation_strategy: Optional[str] = None

 @property
 def provider(self) -> str:
  if not self.preferred_models: return ""
  return self.preferred_models[0].get("provider") or ""

 @property
 def model(self) -> str:
  if not self.preferred_models: return ""
  return self.preferred_models[0].get("model") or ""

 @property
 def temperature(self) -> float:
  if not self.preferred_models: return 0.0
  return float(self.preferred_models[0].get("temperature") or 0.0)

 @property
 def top_p(self) -> float:
  if not self.preferred_models: return 1.0
  return float(self.preferred_models[0].get("top_p") or 1.0)

 @property
 def max_output_tokens(self) -> int:
  if not self.preferred_models: return 0
  return int(self.preferred_models[0].get("max_output_tokens") or 0)

 def to_dict(self) -> Metadata:
  res = {"system_prompt": self.system_prompt}
  if self.preferred_models:
   processed = []
   for m in self.preferred_models:
    if isinstance(m, str):
     processed.append({"model": m})
    else:
     processed.append(m)
   res["preferred_models"] = processed
  if self.tool_preset          is not None: res["tool_preset"] = self.tool_preset
  if self.bias_profile         is not None: res["bias_profile"] = self.bias_profile
  if self.context_preset       is not None: res["context_preset"] = self.context_preset
  if self.aggregation_strategy is not None: res["aggregation_strategy"] = self.aggregation_strategy
  return res

 @classmethod
 def from_dict(cls, name: str, data: Metadata) -> "Persona":
  raw_models = data.get("preferred_models", [])
  parsed_models = []
  for m in raw_models:
   if isinstance(m, str):
    parsed_models.append({"model": m})
   else:
    parsed_models.append(m)
  legacy = {}
  for k in ["provider", "model", "temperature", "top_p", "max_output_tokens"]:
   if data.get(k) is not None:
    legacy[k] = data[k]
  if legacy:
   if not parsed_models:
    parsed_models.append(legacy)
   else:
    for k, v in legacy.items():
     if k not in parsed_models[0] or parsed_models[0][k] is None:
      parsed_models[0][k] = v
  return cls(
   name                 = name,
   preferred_models     = parsed_models,
   system_prompt        = data.get("system_prompt", ""),
   tool_preset          = data.get("tool_preset"),
   bias_profile         = data.get("bias_profile"),
   context_preset       = data.get("context_preset"),
   aggregation_strategy = data.get("aggregation_strategy"),
  )


class PersonaManager:
 """Manages Persona profiles across global and project-specific files."""

 def __init__(self, project_root: Optional[Path] = None):
  self.project_root = project_root

 def _get_path(self, scope: str) -> Path:
  if scope == "global":
   return paths.get_global_personas_path()
  elif scope == "project":
   if not self.project_root:
    raise ValueError("Project root is not set, cannot resolve project scope.")
   return paths.get_project_personas_path(self.project_root)
  else:
   raise ValueError("Invalid scope, must be 'global' or 'project'")

 def load_all(self) -> Dict[str, Persona]:
  personas = {}
  global_path = paths.get_global_personas_path()
  global_data = self._load_file(global_path)
  for name, data in global_data.get("personas", {}).items():
   personas[name] = Persona.from_dict(name, data)
  if self.project_root:
   project_path = paths.get_project_personas_path(self.project_root)
   project_data = self._load_file(project_path)
   for name, data in project_data.get("personas", {}).items():
    personas[name] = Persona.from_dict(name, data)
  return personas

 def save_persona(self, persona: Persona, scope: str = "project") -> None:
  path = self._get_path(scope)
  data = self._load_file(path)
  if "personas" not in data:
   data["personas"] = {}
  data["personas"][persona.name] = persona.to_dict()
  self._save_file(path, data)

 def get_persona_scope(self, name: str) -> str:
  """Returns the scope ('global' or 'project') of a persona by name."""
  if self.project_root:
   project_path = paths.get_project_personas_path(self.project_root)
   project_data = self._load_file(project_path)
   if name in project_data.get("personas", {}):
    return "project"
  global_path = paths.get_global_personas_path()
  global_data = self._load_file(global_path)
  if name in global_data.get("personas", {}):
   return "global"
  return "project"

 def delete_persona(self, name: str, scope: str = "project") -> None:
  path = self._get_path(scope)
  data = self._load_file(path)
  if "personas" in data and name in data["personas"]:
   del data["personas"][name]
   self._save_file(path, data)

 def _load_file(self, path: Path) -> Dict[str, Any]:
  if not path.exists():
   return {}
  try:
   with open(path, "rb") as f:
    return tomllib.load(f)
  except Exception:
   return {}

 def _save_file(self, path: Path, data: Dict[str, Any]) -> None:
  path.parent.mkdir(parents=True, exist_ok=True)
  # tomli_w is loaded on-demand to keep the main-thread import graph lean.
  import tomli_w
  with open(path, "wb") as f:
   tomli_w.dump(data, f)