Update visuals.py

visuals.py

@@ -10,22 +10,18 @@ def create_progress_chart(log_data):
     if not log_data or "trace" not in log_data:
         return None
     
-    # Filter for "attempt" steps to get the scores
     attempts = [step for step in log_data["trace"] if step["step_type"] == "attempt"]
     if not attempts:
         return None
 
-    # Define the standard criteria
     categories = ["Novelty", "Usefulness_Feasibility", "Flexibility", "Elaboration", "Cultural_Appropriateness"]
     
     fig = go.Figure()
 
     for i, attempt in enumerate(attempts):
         scores = attempt.get("scores", {})
-        # Handle cases where scores might be missing (default to 0)
         values = [scores.get(cat, 0) for cat in categories]
         
-        # Close the loop for radar chart (repeat first value at the end)
         values += [values[0]]
         radar_categories = categories + [categories[0]]
         
@@ -44,7 +40,7 @@ def create_progress_chart(log_data):
         polar=dict(
             radialaxis=dict(
                 visible=True,
-                range=[0, 5] # Scores are always 0-5
+                range=[0, 5] 
             )),
         showlegend=True,
         title="Evolution of Solution Quality"
@@ -58,24 +54,15 @@ def create_calibration_table(log_data):
     if not log_data or "trace" not in log_data:
         return None
     
-    # Find the calibration step in the log
     calibration_step = next((step for step in log_data["trace"] if step["step_type"] == "calibration"), None)
-    
-    # If no calibration happened (e.g. Single Agent mode), return None
     if not calibration_step or "details" not in calibration_step:
         return None
 
-    # details is a list of dicts: {'role': 'Plant', 'llm': 'Gemini', 'score': {...}}
     details = calibration_step["details"]
-    
     data = []
     for item in details:
         role = item["role"]
         model = item["llm"]
-        
-        # The 'score' field contains the full evaluation object.
-        # We need to extract the specific numeric score relevant to that Role.
-        # Plant -> Novelty, Implementer -> Feasibility, Monitor -> Cultural
         score_data = item.get("score", {})
         
         score = 0
@@ -89,12 +76,48 @@ def create_calibration_table(log_data):
         
         data.append({"Role": role, "Model": model, "Score": score})
 
-    if not data:
-        return None
+    if not data: return None
 
     df = pd.DataFrame(data)
-    
-    # Pivot the table: Rows = Roles, Columns = Models, Values = Scores
-    # This creates a nice comparison matrix
     pivot_df = df.pivot(index="Role", columns="Model", values="Score").reset_index()
-    return pivot_df
+    return pivot_df
+
+def create_cost_summary(log_data):
+    """
+    Creates a Markdown summary of costs based on usage data found in the log.
+    """
+    if not log_data or "financial_report" not in log_data:
+        return "**No Financial Data Available**"
+    
+    fin = log_data["financial_report"]
+    total = fin.get("total_cost", 0)
+    calib = fin.get("calibration_cost", 0)
+    gen = fin.get("generation_cost", 0)
+    
+    # Calculate Total Tokens
+    total_input = sum(u.get("input", 0) for u in fin.get("usage_breakdown", []))
+    total_output = sum(u.get("output", 0) for u in fin.get("usage_breakdown", []))
+    
+    # Model Usage Breakdown
+    models_used = {}
+    for u in fin.get("usage_breakdown", []):
+        m = u.get("model", "Unknown")
+        models_used[m] = models_used.get(m, 0) + 1
+    
+    model_str = ", ".join([f"{k} ({v} calls)" for k,v in models_used.items()])
+
+    md = f"""
+    ### 💰 Financial Intelligence Report
+    
+    | **Category** | **Cost (USD)** | **Details** |
+    | :--- | :--- | :--- |
+    | **Total Investment** | **${total:.6f}** | **Total execution cost** |
+    | Calibration Phase | ${calib:.6f} | Auditing models to pick the best team |
+    | Solution Phase | ${gen:.6f} | Drafting, refining, and judging |
+    
+    ---
+    **Operational Metrics:**
+    * **Total Tokens:** {total_input + total_output:,} ({total_input:,} in / {total_output:,} out)
+    * **Models Deployed:** {model_str}
+    """
+    return md