2125_GCE/glyph_model_integration.py

"""Glyph-Enhanced Model Execution.

Integrates symbolic layer with computational model execution:
- Chat with Llama → glyph-boosted responses
- Image generation → glyph-guided creativity
- Video generation → glyph-directed narratives
- Vision analysis → glyph-enhanced perception

Usage:
    from superdave.glyph_model_integration import execute_with_glyph
    
    result = execute_with_glyph(
        glyph_routing_result,
        model_function,
        **kwargs
    )
"""

import logging
from typing import Dict, Any, Optional, Callable
from dataclasses import dataclass

logger = logging.getLogger(__name__)


@dataclass
class GlyphExecutionContext:
    """Context for glyph-enhanced execution."""
    glyph_id: str
    specialized_type: str
    power_boost: float
    resonance_score: float
    superpower_ids: list[int]
    model: str
    priority: float
    constraints: list[str]
    enhancements: list[str]


async def execute_with_glyph(
    glyph_context: GlyphExecutionContext,
    model_function: Callable,
    **kwargs
) -> Any:
    """Execute model function with glyph enhancements.
    
    Args:
        glyph_context: Glyph execution context
        model_function: Model function to call (chat, generate, etc.)
        **kwargs: Arguments to pass to model function
    
    Returns:
        Model result with glyph enhancements applied
    """
    logger.info(
        f"Executing {glyph_context.model} with glyph {glyph_context.glyph_id} "
        f"({glyph_context.specialized_type}), boost={glyph_context.power_boost:.2f}x"
    )
    
    # Apply constraints
    for constraint in glyph_context.constraints:
        logger.debug(f"Applying constraint: {constraint}")
        kwargs = apply_constraint(constraint, kwargs)
    
    # Apply enhancements
    for enhancement in glyph_context.enhancements:
        logger.debug(f"Applying enhancement: {enhancement}")
        kwargs = apply_enhancement(enhancement, kwargs, glyph_context)
    
    # Execute model function (may be async)
    result = model_function(**kwargs)
    if hasattr(result, '__await__'):
        result = await result
    
    # Post-process with glyph context
    result = post_process_result(result, glyph_context)
    
    return result


def apply_constraint(constraint: str, kwargs: Dict[str, Any]) -> Dict[str, Any]:
    """Apply a constraint to model execution."""
    if constraint == "safety_check":
        kwargs["safe"] = True
        kwargs["temperature"] = min(kwargs.get("temperature", 0.7), 0.5)
    
    elif constraint == "panic_nulling":
        kwargs["system_prompt"] = (kwargs.get("system_prompt", "") + 
                                   " Maintain calm, rational tone. Avoid alarmist language.")
    
    elif constraint == "identity_cohesion":
        kwargs["system_prompt"] = (kwargs.get("system_prompt", "") +
                                   " Maintain consistent identity and persona throughout.")
    
    elif constraint == "logic_chain_validation":
        kwargs["require_step_by_step"] = True
    
    elif constraint == "creative_bounds":
        kwargs["negative_prompt"] = kwargs.get("negative_prompt", "") + ", distorted, deformed, ugly"
    
    elif constraint == "cold_logic_mode":
        kwargs["temperature"] = 0.1  # Very deterministic
        kwargs["system_prompt"] = (kwargs.get("system_prompt", "") +
                                   " Use pure logic, no emotional bias.")
    
    elif constraint == "bias_free":
        kwargs["system_prompt"] = (kwargs.get("system_prompt", "") +
                                   " Provide unbiased, objective analysis.")
    
    return kwargs


def apply_enhancement(
    enhancement: str,
    kwargs: Dict[str, Any],
    glyph_context: GlyphExecutionContext
) -> Dict[str, Any]:
    """Apply an enhancement to model execution."""
    if enhancement == "stability_monitor":
        kwargs["max_tokens"] = min(kwargs.get("max_tokens", 2000), 1500)
    
    elif enhancement == "symbolic_reasoning":
        kwargs["require_symbolic_output"] = True
    
    elif enhancement == "multi_step_inference":
        kwargs["chain_of_thought"] = True
    
    elif enhancement == "self_consistency_check":
        kwargs["self_review"] = True
    
    elif enhancement == "bloomflare_engine":
        # Boost creativity for image generation
        if kwargs.get("guidance_scale", 7.5) > 0:
            kwargs["guidance_scale"] = kwargs["guidance_scale"] * 1.2
    
    elif enhancement == "novelty_boost":
        kwargs["temperature"] = kwargs.get("temperature", 0.7) * 1.3
    
    elif enhancement == "pattern_synthesis":
        kwargs["synthesis_mode"] = True
    
    elif enhancement == "universal_override":
        # G001 special: maximum authority
        kwargs["override_limits"] = True
        kwargs["max_tokens"] = 4000
    
    elif enhancement == "primordial_resonance":
        kwargs["resonance_boost"] = glyph_context.resonance_score
    
    elif enhancement == "all_superpowers_active":
        kwargs["full_power_mode"] = True
    
    # Apply power boost multiplier
    if glyph_context.power_boost > 2.0:
        kwargs["power_boost_applied"] = glyph_context.power_boost
    
    return kwargs


def post_process_result(result: Dict[str, Any], glyph_context: GlyphExecutionContext) -> Dict[str, Any]:
    """Post-process result with glyph context."""
    # Add glyph metadata to result
    result["glyph_context"] = {
        "glyph_id": glyph_context.glyph_id,
        "specialized_type": glyph_context.specialized_type,
        "power_boost": glyph_context.power_boost,
        "resonance_score": glyph_context.resonance_score,
        "superpower_count": len(glyph_context.superpower_ids),
    }
    
    # Add boost indicator
    if glyph_context.power_boost > 2.0:
        result["boosted"] = True
        result["boost_multiplier"] = glyph_context.power_boost
    
    return result


# Specialized type handlers
def get_type_handler(specialized_type: str) -> Optional[Callable]:
    """Get specialized handler for glyph type."""
    handlers = {
        "frost_steel_stabilizer": handle_frost_steel,
        "mirror_weave_reasoning": handle_mirror_weave,
        "star_bloom_creativity": handle_star_bloom,
        "orbital_thread_network": handle_orbital_thread,
        "aether_node": handle_aether_node,
        "monument_grade_equilibrium": handle_monument_grade,
    }
    return handlers.get(specialized_type)


def handle_frost_steel(result: Dict, context: GlyphExecutionContext) -> Dict:
    """Frost-Steel stabilizer: ensure stability and safety."""
    result["stability_verified"] = True
    result["panic_nulled"] = True
    return result


def handle_mirror_weave(result: Dict, context: GlyphExecutionContext) -> Dict:
    """Mirror-Weave reasoning: enhance logic chains."""
    result["logic_chain_validated"] = True
    result["symbolic_reasoning_applied"] = True
    return result


def handle_star_bloom(result: Dict, context: GlyphExecutionContext) -> Dict:
    """Star-Bloom creativity: boost creative output."""
    result["creativity_enhanced"] = True
    result["bloomflare_applied"] = True
    return result


def handle_orbital_thread(result: Dict, context: GlyphExecutionContext) -> Dict:
    """Orbital-Thread network: enable multi-node coordination."""
    result["distributed_processing"] = True
    result["cross_node_sync"] = True
    return result


def handle_aether_node(result: Dict, context: GlyphExecutionContext) -> Dict:
    """Aether-Node (G001): primordial root authority."""
    result["primordial_authority"] = True
    result["universal_override"] = True
    result["all_powers_active"] = True
    return result


def handle_monument_grade(result: Dict, context: GlyphExecutionContext) -> Dict:
    """Monument-Grade equilibrium: system balance."""
    result["equilibrium_maintained"] = True
    result["system_balance"] = True
    return result


# Integration helpers for server endpoints
def prepare_chat_with_glyph(glyph_context: GlyphExecutionContext, messages: list) -> Dict:
    """Prepare chat request with glyph enhancements."""
    return {
        "messages": messages,
        "temperature": 0.7 if glyph_context.power_boost < 2.0 else 0.5,
        "system_prompt": f"Activated glyph {glyph_context.glyph_id} ({glyph_context.specialized_type}). "
                         f"Power boost: {glyph_context.power_boost:.2f}x. "
                         f"Resonance: {glyph_context.resonance_score:.1f}.",
        "glyph_context": glyph_context,
    }


def prepare_image_with_glyph(glyph_context: GlyphExecutionContext, prompt: str) -> Dict:
    """Prepare image generation request with glyph enhancements."""
    # Cap steps at reasonable range; caller can override
    boost_steps = min(int(glyph_context.power_boost), 30)
    return {
        "prompt": prompt,
        "guidance_scale": min(7.5 * (1 + glyph_context.resonance_score / 100), 12.0),
        "steps": max(1, boost_steps),
        "glyph_context": glyph_context,
    }


def prepare_vision_with_glyph(glyph_context: GlyphExecutionContext, image_path: str, prompt: str) -> Dict:
    """Prepare vision analysis request with glyph enhancements."""
    return {
        "image_path": image_path,
        "prompt": f"[Glyph {glyph_context.glyph_id}] {prompt}",
        "detail_level": "high" if glyph_context.power_boost > 2.0 else "normal",
        "glyph_context": glyph_context,
    }