# core/esil_inference.py
# Master Emotional Core (MEC) - ESIL Inference

from core.codex_informer import CodexInformer

class ESILInference:
    def __init__(self, enable_gradient_blending=True, blend_maximum=3, confidence_threshold=0.65):
        self.enable_gradient_blending = enable_gradient_blending
        self.blend_maximum = blend_maximum
        self.confidence_threshold = confidence_threshold
        # Initialize Codex Informer for shared emotion lookups
        self.codex_informer = CodexInformer()

    def infer_esil(self, eil_packet):
        phrases = eil_packet.get("phrases", [])
        emotion_candidates = eil_packet.get("emotion_candidates", [])

        # Trigger HEI if vague phrases detected:
        low_conf_phrases = ["meh", "...", "idk", "whatever", "fine"]
        
        # Check if any low-confidence phrases are present
        if any(lp in phrases for lp in low_conf_phrases):
            confidence_score = 0.3
        else:
            confidence_score = 0.85

        # Retrieve emotion family, arc, and resonance from Codex Informer
        primary_emotion_code = eil_packet.get("primary_emotion_code", "UNK")
        
        # Ensure the primary emotion code is resolved correctly by CodexInformer
        emotion_data = self.codex_informer.resolve_emotion_family(primary_emotion_code)

        emotion_family = emotion_data['emotion_family']
        arc = emotion_data['arc']
        resonance = emotion_data['resonance']

        # If no emotion family is found, flag it as a "hidden emotion"
        if emotion_family == "Unknown":
            emotion_family = "Hidden Emotion Detected"  # Fallback for hidden emotion logic

        # Build ESIL packet with updated emotion data from Codex Informer
        esil_packet = {
            "blend_weights": [
                {"emotion": "Pending", "weight": 0.8}
            ],
            "trajectory": "Stable",
            "confidence_score": confidence_score,
            "emotion_family": emotion_family,  # From Codex Informer
            "arc": arc,  # From Codex Informer
            "resonance": resonance,  # From Codex Informer
            "primary_emotion_code": primary_emotion_code,  # <-- PATCH INCLUDED
            "source_metadata": eil_packet.get("metadata", {}),
            "tokens": phrases
        }

        # Confidence routing logic: Directly to ERIS if confidence is high
        if confidence_score >= self.confidence_threshold:
            routing_decision = "proceed_to_eris"
        # Trigger HEI if confidence is low and unresolved
        elif confidence_score < self.confidence_threshold:
            routing_decision = "escalate_to_hei"

        esil_packet['routing_decision'] = routing_decision
        print(f"[ESILInference] ESIL Packet with Routing Decision: {esil_packet}")
        
        return esil_packet