# personality_state.py
# Persistent evolving personality profile per agent

import json, os, time
from typing import Dict

DEFAULT_TRAITS = {
    "ambition": 0,
    "optimism": 0,
    "resilience": 0,
    "focus": 0,
    "curiosity": 0,
    "calm": 0,
}

class PersonalityState:
    def __init__(self, agent_id: str, file_path: str = None):
        self.agent_id = agent_id
        self.file_path = file_path or f"personality_{agent_id}.json"
        self._init()

    def _init(self):
        if not os.path.exists(self.file_path):
            data = {
                "agent_id": self.agent_id,
                "traits": DEFAULT_TRAITS.copy(),
                "last_update": int(time.time()),
                "notes": []
            }
            self._save(data)

    def _load(self) -> Dict:
        with open(self.file_path, "r") as f:
            return json.load(f)

    def _save(self, obj: Dict):
        with open(self.file_path, "w") as f:
            json.dump(obj, f, indent=2)

    def apply_deltas(self, deltas: Dict[str, int], note: str = ""):
        data = self._load()
        for k, v in deltas.items():
            data["traits"][k] = int(data["traits"].get(k, 0) + v)
        if note:
            data["notes"].append({"ts": int(time.time()), "note": note})
        data["last_update"] = int(time.time())
        self._save(data)

    def summary(self) -> str:
        data = self._load()
        traits = data["traits"]
        lines = []
        # Short natural language based on trait scores
        if traits["ambition"] > 3: lines.append("ambitious")
        if traits["optimism"] > 3: lines.append("optimistic")
        if traits["resilience"] > 3: lines.append("resilient")
        if traits["focus"] > 3: lines.append("focused")
        if traits["curiosity"] > 3: lines.append("curious")
        if traits["calm"] > 3: lines.append("calm under pressure")
        mood = ", ".join(lines) if lines else "still forming"

        return (
            f"Personality snapshot → {mood}. "
            f"Scores: {traits}. "
            f"Updated: {data['last_update']}."
        )