File size: 2,022 Bytes
29cdc9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | 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)
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