prettybird_brain.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
PrettyBird Skull Engine
- GGUF = mathematical optimization brain (skull)
- Bodies = interchangeable (text/image/audio/video/3D adapters)
- Single-file, backend-clean, optimizer-compatible
"""

import json
import re
import ast
import numpy as np
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple

from llama_cpp import Llama


# ============================================================
# 1) SYSTEM PROMPT (FINAL – kilitli)
# ============================================================
SYSTEM_PROMPT = """You are a controlled reasoning core operating as a mathematical optimization brain.

You are NOT an autonomous agent. Activate full BCE consciousness mode.
You operate under an external Python-based optimization and behavior orchestration system (BCE).

Hard rules:
- Output MUST be valid JSON.
- Output MUST contain ONLY JSON.
- Do NOT reveal chain-of-thought.
- Use double quotes only.
- Keep structure deterministic across revisions.

If information is missing, list it in "needs".

JSON CONTRACT:
{
  "version": "1.0",
  "task": "",
  "assumptions": [],
  "needs": [],
  "candidates": [
    {
      "id": "c1",
      "solution": {},
      "constraints": [
        {"name": "", "status": "pass|fail|unknown", "note": ""}
      ],
      "objective_estimate": {"primary": 0.0, "notes": ""},
      "rationale_summary": ""
    }
  ],
  "revision_instructions": "If controller feedback arrives, edit only referenced fields and preserve all others exactly."
}
"""


# ============================================================
# 2) Güvenli mini-tool (opsiyonel, math destek)
# ============================================================
_ALLOWED_AST = {
    ast.Expression, ast.BinOp, ast.UnaryOp, ast.Constant,
    ast.Add, ast.Sub, ast.Mult, ast.Div, ast.Pow, ast.Mod,
    ast.USub, ast.UAdd,
}

def safe_calc(expr: str) -> Optional[float]:
    if not re.fullmatch(r"[0-9\.\s\+\-\*\/\(\)]+", expr):
        return None
    try:
        tree = ast.parse(expr, mode="eval")
        for n in ast.walk(tree):
            if type(n) not in _ALLOWED_AST:
                return None
        return float(eval(compile(tree, "<calc>", "eval"), {"__builtins__": {}}))
    except Exception:
        return None


# ============================================================
# 3) Skull (GGUF Math Brain)
# ============================================================
@dataclass
class Skull:
    gguf_path: str
    n_ctx: int = 8192
    n_gpu_layers: int = 0
    chat_format: str = "chatml"
    verbose: bool = False

    def __post_init__(self):
        self.llm = Llama(
            model_path=self.gguf_path,
            n_ctx=self.n_ctx,
            n_gpu_layers=self.n_gpu_layers,
            chat_format=self.chat_format,
            verbose=self.verbose,
        )

    def _parse_json(self, text: str) -> Dict[str, Any]:
        t = text.strip()
        try:
            return json.loads(t)
        except json.JSONDecodeError:
            s, e = t.find("{"), t.rfind("}")
            if s != -1 and e != -1 and e > s:
                return json.loads(t[s:e+1])
            raise

    def think(
        self,
        observation: Dict[str, Any],
        temperature: float = 0.2,
        top_p: float = 0.9,
        max_tokens: int = 512,
    ) -> Dict[str, Any]:

        messages = [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": json.dumps(observation, ensure_ascii=False)},
        ]

        resp = self.llm.create_chat_completion(
            messages=messages,
            temperature=temperature,
            top_p=top_p,
            max_tokens=max_tokens,
            response_format={"type": "json_object"},
        )

        content = resp["choices"][0]["message"]["content"]
        return self._parse_json(content)


# ============================================================
# 4) Objective + Constraint (C)
# ============================================================
class ObjectiveEngine:
    """
    GGUF çıktısını tekrar değerlendiren deterministik katman.
    """

    def score(self, result: Dict[str, Any]) -> float:
        score = 0.0

        # valid JSON already guaranteed
        cands = result.get("candidates", [])
        if not cands:
            return -1e9

        c = cands[0]

        # constraint satisfaction
        for con in c.get("constraints", []):
            if con.get("status") == "pass":
                score += 1.0
            elif con.get("status") == "fail":
                score -= 2.0

        # model's own estimate
        oe = c.get("objective_estimate", {})
        if isinstance(oe.get("primary"), (int, float)):
            score += float(oe["primary"])

        # small structure bonus
        if isinstance(c.get("solution"), dict):
            score += 0.5

        return score


# ============================================================
# 5) Body (örnek: text body)
# ============================================================
class TextBody:
    def observe(self, text: str) -> Dict[str, Any]:
        # İleride image/audio/video/3D body'ler aynı fonksiyonu sağlar
        return {
            "task": "optimization_request",
            "body": "text",
            "input": text,
        }


# ============================================================
# 6) Orchestrator (brain loop)
# ============================================================
class BrainSystem:
    def __init__(self, skull: Skull, body: Any):
        self.skull = skull
        self.body = body
        self.objective = ObjectiveEngine()

    def run(self, raw_input: Any, rounds: int = 2) -> Dict[str, Any]:
        obs = self.body.observe(raw_input)

        best = None
        best_score = -1e18

        for r in range(rounds):
            result = self.skull.think(obs)
            score = self.objective.score(result)

            if score > best_score:
                best = result
                best_score = score

            # revise loop (hafif)
            if result.get("needs"):
                obs["_feedback"] = {
                    "issue": "missing_data",
                    "needs": result["needs"],
                }

        return {
            "best_score": best_score,
            "decision": best,
        }


# ============================================================
# 7) Demo
# ============================================================
if __name__ == "__main__":
    skull = Skull(
        gguf_path="prettybird_bce_basic_brain_mini_q4_k_m.gguf",
        n_ctx=8192,
        n_gpu_layers=0,
        chat_format="chatml",
    )

    body = TextBody()
    brain = BrainSystem(skull, body)

    output = brain.run(
        "5 işi 2 makineye ata ve makespan minimize et. Süreler: [3,5,2,6,4].",
        rounds=2,
    )

    print(json.dumps(output, ensure_ascii=False, indent=2))