import yaml
from core import codex_informer
from core.codex_informer import CodexInformer
from core.eil_processor    import EILProcessor
from core.esil_inference   import ESILInference
from core.hei_inference    import HEIInference
from core.eris_reasoner    import ERISReasoner
from core.fec_controller   import FECController

# ─── GLOBAL INITIALIZATION ────────────────────────────────────────────────────────
codex = CodexInformer(
    emotion_families_path="config/emotion_families.yaml",
    arc_mapping_path="config/arc_mapping.yaml",
    resonance_mapping_path="config/resonance_mapping.yaml"
)

# EIL & ESIL use codex for lookups
eil   = EILProcessor(codex)
esil  = ESILInference(codex)

# HEIInference now takes codex so it can resolve families
hei   = HEIInference(codex)

# ERISReasoner also takes codex to resolve arc/resonance
eris  = ERISReasoner(codex)

# FECController remains parameterless
fec   = FECController()

# Load Response Strategies YAML
with open('config/response_strategies.yaml', 'r', encoding='utf-8') as f:
    response_strategies = yaml.safe_load(f)['response_strategies']

def run_mec_pipeline(user_input_text: str, force_hei: bool = False):
    """
    Full MEC pipeline:
      1) EILProcessor.infer_emotion
      2) ESILInference.infer_esil
      3) Routing: ERIS vs. HEI fallback based on confidence or force_hei
      4) FECController.generate_prompt
    Returns:
      fusion_prompt (str), final_uesp (dict), empathic_response (str), EmID (str)
    """
    # 1️⃣ EIL
    eil_packet = eil.infer_emotion(user_input_text)

    # 2️⃣ ESIL
    esil_packet = esil.infer_esil(eil_packet)

    # 3️⃣ Routing / HEI fallback
    if force_hei or esil_packet.get("confidence_score", 1.0) < 0.65:
        pseudo_esp = hei.detect_low_signal(esil_packet)
        final_uesp = eris.reason_emotion_state(pseudo_esp)
    else:
        final_uesp = eris.reason_emotion_state(esil_packet)

    # 4️⃣ FEC
    fusion_prompt = fec.generate_prompt(final_uesp)

    # 5️⃣ Simulated Empathic Response
    fam_code = final_uesp.get('primary_emotion_code') or final_uesp.get('Primary Emotion Code')
    # Load arc_lookup from codex or arc_mapping.yaml
    arc_lookup = codex.arc_mapping if hasattr(codex, 'arc_mapping') else {}
    emotion_arc = arc_lookup.get(fam_code, 'TBD')  # Get the arc based on the family
    rsm_code = response_strategies.get(fam_code, {}).get('rsm_code', 'RSM-UNKNOWN')
    strategy_name = response_strategies.get(fam_code, {}).get('strategy', 'Strategy not defined')
    sample_response = response_strategies.get(fam_code, {}).get('sample_response', 'No response available')

    # Get Emotion ID (EmID)
    emid = final_uesp.get('emid', 'No EmID available')

    empathic_response = (
        f"Response Strategy Code: {rsm_code}\n"
        f"Response Strategy: {strategy_name}\n\n"
        f"{sample_response}"
    )

    return fusion_prompt, final_uesp, empathic_response, emid

def get_demo_scenarios():
    """
    Load demo scenarios from the YAML file.
    Returns a dictionary of label-text pairs.
    """
    with open('config/demo_scenarios.yaml', 'r', encoding='utf-8') as f:
        test_scenarios = yaml.safe_load(f).get('test_scenarios', [])
    return {item['label']: item['text'] for item in test_scenarios}

# Example usage
if __name__ == "__main__":
    prompt, uesp, empathic_response = run_mec_pipeline("I’m feeling anxious about tomorrow.", force_hei=False)
    print("Fusion Prompt:\n", prompt)
    print("Final UESP:\n", uesp)
    print("Empathic Response:\n", empathic_response)