Update agent_logic.py

agent_logic.py

@@ -1,4 +1,4 @@
-# agent_logic.py (FINAL, Robust Version)
+# agent_logic.py (Milestone 5 - FINAL & ROBUST)
 import asyncio
 from typing import AsyncGenerator, Dict, Optional
 import json
@@ -6,14 +6,13 @@ import os
 import google.generativeai as genai
 from anthropic import AsyncAnthropic
 from openai import AsyncOpenAI
-import re # <-- Added: For score parsing fix
+import re 
 from personas import PERSONAS_DATA 
 import config
 from utils import load_prompt
 from mcp_servers import AgentCalibrator, BusinessSolutionEvaluator, get_llm_response
 from self_correction import SelfCorrector
 
-# (Configuration and Manager Prompts are loaded here)
 CLASSIFIER_SYSTEM_PROMPT = load_prompt(config.PROMPT_FILES["classifier"])
 HOMOGENEOUS_MANAGER_PROMPT = load_prompt(config.PROMPT_FILES["manager_homogeneous"])
 HETEROGENEOUS_MANAGER_PROMPT = load_prompt(config.PROMPT_FILES["manager_heterogeneous"])
@@ -170,17 +169,15 @@ class StrategicSelectorAgent:
                 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):
-                            # Standard format: {"score": 4, "justification": "..."}
                             score_value = v.get('score')
                             justification_value = v.get('justification', str(v))
                         elif isinstance(v, list) and len(v) > 0 and isinstance(v[0], dict):
-                            # Handles list structure: [{"score": 4, "justification": "..."}]
                             score_value = v[0].get('score')
                             justification_value = v[0].get('justification', str(v[0]))
                         else:
-                            # Fallback for unexpected structure
-                            score_value = 0
+                            score_value = v.get('score', 0) if isinstance(v, dict) else 0 # Fallback check
                             justification_value = str(v)
                         
                         # FIX: Extract the integer score from the string (e.g., "4/5" -> 4)
@@ -190,7 +187,6 @@ class StrategicSelectorAgent:
                             except:
                                 score_value = 0
                         
-                        # Ensure score is an integer
                         try:
                             score_value = int(score_value)
                         except (ValueError, TypeError):
@@ -199,11 +195,9 @@ class StrategicSelectorAgent:
                         normalized_fitness[k] = {'score': score_value, 'justification': justification_value}
 
                 else:
-                     # Fallback if the whole thing isn't a dict
                      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}"