Create self_correction.py

self_correction.py

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+# self_correction.py
+from typing import Dict, Any
+
+class SelfCorrector:
+    """
+    Implements the "Self-Correction Loop" from Paper 2.
+    It diagnoses low scores and maps them to correction plans.
+    """
+    
+    def __init__(self, threshold: float = 3.0):
+        self.threshold = threshold
+
+    def is_good_enough(self, v_fitness_scores: Dict[str, int]) -> bool:
+        """
+        Checks if the solution is good enough to be presented.
+        Checks if *all* scores are at or above the threshold.
+        """
+        print(f"Checking scores {v_fitness_scores} against threshold {self.threshold}")
+        for score in v_fitness_scores.values():
+            if score < self.threshold:
+                print("Score is too low. Initiating Self-Correction.")
+                return False
+        print("Score is good. Solution accepted.")
+        return True
+
+    def get_correction_plan(self, v_fitness_json: Dict[str, Any]) -> str:
+        """
+        Implements the "Diagnostic Error-to-Belbin Role Mapping" (Paper 2).
+        It analyzes the full v_fitness JSON (with justifications)
+        and generates a "Chain-of-Thought" correction prompt.
+        """
+        
+        # 1. Find the lowest-scoring criterion
+        lowest_score = 5
+        lowest_metric = "None"
+        for metric, data in v_fitness_json.items():
+            if data.get('score', 5) < lowest_score:
+                lowest_score = data.get('score', 5)
+                lowest_metric = metric
+        
+        failure_justification = v_fitness_json.get(lowest_metric, {}).get('justification', "No justification provided.")
+        
+        # 2. Map low score to a failure diagnosis (from Paper 2)
+        if lowest_metric == "Novelty":
+            failure_diagnosis = f"Ideation Failure (Low {lowest_metric}). The judge's feedback was: '{failure_justification}'"
+        elif lowest_metric == "Usefulness_Feasibility":
+            failure_diagnosis = f"Compositional Error (Low {lowest_metric}). The judge's feedback was: '{failure_justification}'"
+        elif lowest_metric == "Cultural_Appropriateness":
+            failure_diagnosis = f"Sensitivity Error (Low {lowest_metric}). The judge's feedback was: '{failure_justification}'"
+        else:
+            failure_diagnosis = f"General Quality Failure (Low {lowest_metric}). The judge's feedback was: '{failure_justification}'"
+
+        # 3. Generate the "Chain-of-Thought" correction prompt (Paper 2, section 4.9.2)
+        correction_prompt = f"""
+        YOUR PREVIOUS ATTEMPT FAILED.
+        
+        FAILURE ANALYSIS:
+        Your solution was evaluated and received a very low score for {lowest_metric}.
+        {failure_diagnosis}
+
+        YOUR TASK:
+        You MUST re-generate a new solution. This new solution must *specifically* address this failure.
+        1.  **Analyze the Failure**: Briefly explain *why* the previous solution failed to be {lowest_metric.lower()}.
+        2.  **Formulate a New Plan**: Outline a new plan that directly fixes this specific failure.
+        3.  **Write the Corrected Solution**: Generate the full, new solution based on this plan.
+        """
+        return correction_prompt