Update agent_logic.py

agent_logic.py

@@ -1,8 +1,10 @@
-# agent_logic.py (Milestone 5 - FINAL & ROBUST)
+# agent_logic.py (Milestone 5 - FINAL & ROBUST + LOGGING)
 import asyncio
 from typing import AsyncGenerator, Dict, Optional
 import json
 import os
+import uuid
+import datetime
 import google.generativeai as genai
 from anthropic import AsyncAnthropic
 from openai import AsyncOpenAI
@@ -102,31 +104,48 @@ class StrategicSelectorAgent:
         yield f"Diagnosis: {classification}"
 
     async def solve(self, problem: str) -> AsyncGenerator[str, None]:
-        classification_generator = self._classify_problem(problem)
-        classification = ""
-        async for status_update in classification_generator:
-            yield status_update
-            if "Diagnosis: " in status_update:
-                classification = status_update.split(": ")[-1]
-        
-        if "Error generating response" in classification:
-            yield "Classifier failed. Defaulting to Single Agent."
-            classification = "Direct_Procedure"
+        # --- 1. Initialize Logging ---
+        run_id = str(uuid.uuid4())[:8]
+        debug_log = {
+            "run_id": run_id,
+            "timestamp": datetime.datetime.now().isoformat(),
+            "problem": problem,
+            "classification": "",
+            "trace": []
+        }
 
-        solution_draft = "" 
-        v_fitness_json = {}
-        scores = {}
-        
         try:
+            classification_generator = self._classify_problem(problem)
+            classification = ""
+            async for status_update in classification_generator:
+                yield status_update
+                if "Diagnosis: " in status_update:
+                    classification = status_update.split(": ")[-1]
+            
+            debug_log["classification"] = classification
+
+            if "Error generating response" in classification:
+                yield "Classifier failed. Defaulting to Single Agent."
+                classification = "Direct_Procedure"
+
+            solution_draft = "" 
+            v_fitness_json = {}
+            scores = {}
+            
             # --- MAIN LOOP (Self-Correction) ---
             for i in range(2): 
-                
                 current_problem = problem
                 if i > 0:
                     yield f"--- (Loop {i}) Score is too low. Initiating Self-Correction... ---"
                     correction_prompt_text = self.corrector.get_correction_plan(v_fitness_json)
                     yield f"Diagnosis: {correction_prompt_text.splitlines()[3].strip()}"
                     current_problem = f"{problem}\n\n{correction_prompt_text}"
+                    
+                    debug_log["trace"].append({
+                        "step_type": "correction_plan",
+                        "loop_index": i,
+                        "prompt": correction_prompt_text
+                    })
                 
                 # --- DEPLOY ---
                 default_persona = PERSONAS_DATA[config.DEFAULT_PERSONA_KEY]["description"]
@@ -142,7 +161,18 @@ class StrategicSelectorAgent:
                 elif classification == "Cognitive_Labyrinth":
                     if i == 0:
                         yield "Deploying: Static Heterogeneous Team (Cognitive Diversity)..."
-                        team_plan, calibration_errors = await self.calibrator.calibrate_team(current_problem)
+                        
+                        # --- UPDATED: Unpack 3 values now ---
+                        team_plan, calibration_errors, calib_details = await self.calibrator.calibrate_team(current_problem)
+                        
+                        # LOG CALIBRATION
+                        debug_log["trace"].append({
+                            "step_type": "calibration",
+                            "details": calib_details,
+                            "errors": calibration_errors,
+                            "selected_plan": team_plan
+                        })
+
                         if calibration_errors:
                             yield "--- CALIBRATION WARNINGS ---"
                             for err in calibration_errors: yield err
@@ -165,11 +195,10 @@ class StrategicSelectorAgent:
                 yield "Evaluating draft (live)..."
                 v_fitness_json = await self.evaluator.evaluate(current_problem, solution_draft)
                 
-                # --- Robust Normalization of Evaluation Data ---
+                # --- Robust Normalization ---
                 normalized_fitness = {}
                 if isinstance(v_fitness_json, dict):
                     for k, v in v_fitness_json.items():
-                        # Determine score value (safe check for list wrapping, which causes the crash)
                         if isinstance(v, dict):
                             score_value = v.get('score')
                             justification_value = v.get('justification', str(v))
@@ -177,10 +206,9 @@ class StrategicSelectorAgent:
                             score_value = v[0].get('score')
                             justification_value = v[0].get('justification', str(v[0]))
                         else:
-                            score_value = v.get('score', 0) if isinstance(v, dict) else 0 # Fallback check
+                            score_value = v.get('score', 0) if isinstance(v, dict) else 0 
                             justification_value = str(v)
                         
-                        # FIX: Extract the integer score from the string (e.g., "4/5" -> 4)
                         if isinstance(score_value, str):
                             try:
                                 score_value = int(re.search(r'\d+', score_value).group())
@@ -193,19 +221,25 @@ class StrategicSelectorAgent:
                             score_value = 0
 
                         normalized_fitness[k] = {'score': score_value, 'justification': justification_value}
-
                 else:
                      normalized_fitness = {k: {'score': 0, 'justification': "Invalid JSON structure"} for k in ["Novelty", "Usefulness_Feasibility", "Flexibility", "Elaboration", "Cultural_Appropriateness"]}
                 
                 v_fitness_json = normalized_fitness
-
                 scores = {k: v.get('score', 0) for k, v in v_fitness_json.items()}
                 yield f"Evaluation Score: {scores}"
                 
+                # LOG ATTEMPT
+                debug_log["trace"].append({
+                    "step_type": "attempt",
+                    "loop_index": i,
+                    "draft": solution_draft,
+                    "scores": scores,
+                    "full_evaluation": v_fitness_json
+                })
+
                 if scores.get('Novelty', 0) <= 1:
                      yield f"⚠️ Low Score Detected. Reason: {v_fitness_json.get('Novelty', {}).get('justification', 'Unknown')}"
 
-                # Check if we passed
                 if self.corrector.is_good_enough(scores):
                     yield "--- Solution approved by self-corrector. ---"
                     break
@@ -221,4 +255,16 @@ class StrategicSelectorAgent:
         except Exception as e:
             error_msg = f"An error occurred in the agent's solve loop: {e}"
             print(error_msg)
-            yield error_msg
+            debug_log["error"] = str(e)
+            yield error_msg
+        
+        finally:
+            # --- SAVE LOG ---
+            try:
+                os.makedirs("logs", exist_ok=True)
+                log_path = f"logs/run_{run_id}.json"
+                with open(log_path, "w", encoding="utf-8") as f:
+                    json.dump(debug_log, f, indent=2)
+                print(f"Detailed execution log saved to {log_path}")
+            except Exception as log_err:
+                print(f"Failed to save log: {log_err}")