advanced_bio_agent.py

import sys
import time
from dataclasses import dataclass

# Ensure we can import the module
try:
    from ljpw_autopoiesis import SelfHealingEngine, HarmonyState
except ImportError:
    sys.path.insert(0, "src")
    from ljpw_autopoiesis import SelfHealingEngine, HarmonyState

class AdvancedBioAgent:
    """
    An advanced agent that persists until perfection.
    It uses a 'Conscious Loop' to repeatedly heal its output until
    a specific Harmony threshold is reached.
    """
    
    def __init__(self, name="Architect-Alpha"):
        self.name = name
        # We set max_ticks=5 per cycle to allow for incremental improvement
        self.immune_system = SelfHealingEngine(verbose=False, max_ticks=5)
        print(f"[{self.name}] Online. Goal: Structural Perfection.")

    def conceive(self, filename: str, rough_code: str, target_harmony: float = 0.75) -> str:
        """
        Bring an idea to life and refine it until it meets the target harmony.
        
        Args:
            filename: Name of the file to generate
            rough_code: The initial broken draft
            target_harmony: The LJPW Harmony score required (0-1.0+)
        """
        print(f"\n[{self.name}] Blueprinting: {filename}")
        current_code = rough_code
        generation = 0
        
        while True:
            generation += 1
            print(f"[{self.name}] Generation {generation}...", end=" ", flush=True)
            
            # Run the healing engine
            # We capture the result to analyze metrics
            result = self.immune_system.heal_source(current_code, filename=filename)
            
            current_harmony = result.final_harmony.harmony()
            phase = result.final_harmony.phase()
            
            print(f"Harmony: {current_harmony:.3f} [{phase}] | Gaps: {len(result.final_harmony.to_dict()) if hasattr(result.final_harmony, 'to_dict') else '?'}")
            
            # Visualize the state
            self._visualize_state(result.final_harmony)
            
            # Update code
            current_code = result.healed_source
            
            # Check exit conditions
            if current_harmony >= target_harmony:
                print(f"\n[{self.name}] TARGET ACHIEVED. Structure is stable.")
                break
            
            if not result.source_changed and generation > 1:
                print(f"\n[{self.name}] STASIS REACHED. Cannot improve further with current knowledge.")
                break
                
            if generation >= 10:
                print(f"\n[{self.name}] FATIGUE. Stopping after 10 generations.")
                break
                
            # If we fixed syntax errors (Harmony dropped or stayed low but phase changed),
            # we must continue to fix the revealed gaps.
            time.sleep(0.5) # Pacing
            
        return current_code

    def _visualize_state(self, h: HarmonyState):
        """Visual feedback of the agent's internal state."""
        # Simple bar chart
        bars = int(h.harmony() * 10)
        print(f"    Health: [{'#' * bars}{'-' * (20 - bars)}] ({h.harmony():.2f})")

if __name__ == "__main__":
    # A complex Neural Network implementation draft
    # Contains:
    # - Syntax errors (missing colons, brackets)
    # - Indentation errors (mixed tabs)
    # - Naming violations (bad class names)
    # - Long lines (matrix math comments)
    # - Bare excepts (math errors)
    
    neural_draft = """
import random
import math

class simple_neuron:
    def __init__(self, weights, bias):
        self.weights = weights
        self.bias = bias
        
    def activate(self, inputs)
        # Calculate dot product
        total = 0
        for i in range(len(inputs)):
            total = total + inputs[i] * self.weights[i]
            
        total = total + self.bias
        return self._sigmoid(total)
        
    def _sigmoid(self, x):
        try:
            return 1 / (1 + math.exp(-x))
        except:
            return 0

class neural_network:
    def __init__(self, input_size, hidden_size, output_size):
        self.hidden_layer = []
        self.output_layer = []
        
        # Initialize hidden layer
        for i in range(hidden_size):
            weights = [random.random() for _ in range(input_size)]
            self.hidden_layer.append(simple_neuron(weights, random.random()))
            
        # Initialize output layer
        for i in range(output_size):
            weights = [random.random() for _ in range(hidden_size)]
            self.output_layer.append(simple_neuron(weights, random.random()))
            
    def feed_forward(self, inputs):
        hidden_outputs = []
        for neuron in self.hidden_layer:
            hidden_outputs.append(neuron.activate(inputs))
            
        final_outputs = []
        for neuron in self.output_layer:
            final_outputs.append(neuron.activate(hidden_outputs))
            
        return final_outputs

def Run_Training_Simulation():
    print("Initializing Neural Architecture...")
    # Create a 2-3-1 network
    nn = neural_network(2, 3, 1)
    
    inputs = [0.5, 0.8]
    output = nn.feed_forward(inputs)
    
    long_log_message = "Training step complete with input vector " + str(inputs) + " resulting in output vector " + str(output) + " which indicates the network is functioning but untraind."
    print(long_log_message)

if __name__ == "__main__":
    Run_Training_Simulation()
"""

    architect = AdvancedBioAgent()
    final_code = architect.conceive("neural_net.py", neural_draft, target_harmony=7.5)
    
    print("\n" + "="*40)
    print("GENERATED NEURAL LIBRARY")
    print("="*40)
    print(final_code)
    
    with open("neural_net.py", "w") as f:
        f.write(final_code)