Update visuals.py

visuals.py

@@ -2,6 +2,7 @@
 # Handles all data visualization logic for MudabbirAI
 import pandas as pd
 import plotly.graph_objects as go
+import config # Import config to access specific model names
 
 def create_progress_chart(log_data):
     """
@@ -20,7 +21,6 @@ def create_progress_chart(log_data):
 
     for i, attempt in enumerate(attempts):
         scores = attempt.get("scores", {})
-        # Handle case where scores might be missing
         values = [scores.get(cat, 0) for cat in categories]
         
         # Close the loop for radar chart
@@ -64,12 +64,11 @@ def create_calibration_table(log_data):
     data = []
     for item in details:
         role = item["role"]
-        model = item["llm"]
+        model_provider = item["llm"] # e.g., "Gemini", "Anthropic"
         score_data = item.get("score", {})
         
         score = 0
         if isinstance(score_data, dict):
-             # Extract the specific metric score relevant to that Role
              if role == "Plant": 
                  score = score_data.get("Novelty", {}).get("score", 0)
              elif role == "Implementer": 
@@ -77,7 +76,15 @@ def create_calibration_table(log_data):
              elif role == "Monitor": 
                  score = score_data.get("Cultural_Appropriateness", {}).get("score", 0)
         
-        data.append({"Role": role, "Model": model, "Score": score})
+        # --- NEW: Retrieve specific model name from config for transparency ---
+        specific_model_name = config.MODELS.get(model_provider, {}).get("default", "")
+        if specific_model_name:
+            # Format: "Anthropic\n(claude-3-5-haiku...)"
+            display_name = f"{model_provider}\n({specific_model_name})"
+        else:
+            display_name = model_provider
+
+        data.append({"Role": role, "Model": display_name, "Score": score})
 
     if not data: return None
 
@@ -89,7 +96,6 @@ def create_cost_summary(log_data):
     """
     Creates a Markdown summary of costs based on usage data found in the log.
     """
-    # Robust check in case financial_report is missing or empty
     if not log_data or "financial_report" not in log_data:
         return "### ⚠️ Financial Data Unavailable\n*Could not retrieve usage statistics.*"
     
@@ -103,7 +109,7 @@ def create_cost_summary(log_data):
     total_input = sum(u.get("input", 0) for u in breakdown)
     total_output = sum(u.get("output", 0) for u in breakdown)
     
-    # Model Usage Breakdown (Count how many calls per model)
+    # Model Usage Breakdown
     models_used = {}
     for u in breakdown:
         m = u.get("model", "Unknown")