import os
import sys

# Append parent directory to path to allow importing from the baseline core
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')))

try:
    # Importing the foundation architecture you built previously
    from src.model import TriHeadAttention, FluidicMemoryManifold
except ImportError:
    print("[-] Warning: Baseline Vitalis Core modules not found in path.")
    print("[-] Ensure DevCore is correctly positioned relative to the core architecture.")

class CoreBridge:
    def __init__(self, mode="inference"):
        self.mode = mode
        print("[+] CoreBridge Initialized: Preparing Tri-Head synchronization.")
        # In a full deployment, this is where we load the hardened W_core weights
        self.manifold_active = True 

    def analyze_and_correct(self, flawed_code, traceback_error):
        """
        Passes the failed code and terminal traceback through the Sensu/Ratio/Cor heads.
        """
        if not self.manifold_active:
            return flawed_code
            
        print(f"[*] Sensu Head: Ingesting traceback sequence...")
        print(f"[*] Ratio Head: Aligning semantic logic trees...")
        print(f"[*] Cor Head: Stabilizing fluidic weights for output generation...")
        
        # Here we will eventually format the prompt for the actual model.
        # Format: <CODE>{flawed_code}</CODE> <ERROR>{traceback_error}</ERROR> <TASK>Fix</TASK>
        
        # For our immediate architectural test of the bridge, we define the dynamic prompt structure:
        dynamic_prompt = f"Analyze structural failure:\n{traceback_error}\nCorrect the following logic:\n{flawed_code}"
        
        # We will connect the actual torch inference loop in the next iteration.
        return dynamic_prompt

if __name__ == "__main__":
    bridge = CoreBridge()
    test_analysis = bridge.analyze_and_correct("print(10/0)", "ZeroDivisionError")
    print("\n[+] Dynamic Prompt Ready for Engine Execution:")
    print(test_analysis)