fix: roi graph

app.py

@@ -1197,8 +1197,51 @@ def create_combined_roi_time_series_graph(df):
     for agent_id, data in agent_runtimes.items():
         logger.info(f"Agent {data['agent_name']} (ID: {agent_id}): Runtime = {data['runtime_days']:.2f} days, Last report: {data['last_report']}")
     
-    # IMPORTANT: Force data types to ensure consistency
-    df['roi'] = df['roi'].astype(float)  # Ensure ROI is float
+    # IMPORTANT: Clean and convert ROI data to ensure consistency
+    logger.info("Cleaning ROI data before conversion...")
+    
+    def clean_roi_value(value):
+        """Clean and convert ROI value to float"""
+        if pd.isna(value):
+            return None
+        
+        # If it's already a number, return it
+        if isinstance(value, (int, float)):
+            return float(value)
+        
+        # If it's a string, try to extract numeric value
+        if isinstance(value, str):
+            # Remove any non-numeric characters except decimal point and minus sign
+            import re
+            # Look for patterns like "value': 16.007665648354" and extract the number
+            match = re.search(r'[\d\.-]+', value)
+            if match:
+                try:
+                    return float(match.group())
+                except ValueError:
+                    logger.warning(f"Could not convert ROI value to float: {value}")
+                    return None
+            else:
+                logger.warning(f"No numeric value found in ROI string: {value}")
+                return None
+        
+        logger.warning(f"Unexpected ROI value type: {type(value)} - {value}")
+        return None
+    
+    # Apply cleaning function to ROI column
+    df['roi'] = df['roi'].apply(clean_roi_value)
+    
+    # Remove rows with invalid ROI values
+    initial_count = len(df)
+    df = df[df['roi'].notna()]
+    final_count = len(df)
+    removed_count = initial_count - final_count
+    
+    if removed_count > 0:
+        logger.warning(f"Removed {removed_count} rows with invalid ROI values")
+    
+    # Ensure ROI is float after cleaning
+    df['roi'] = df['roi'].astype(float)
     # ROI values are already in percentage format from initial_value_fixer.py
     df['metric_type'] = df['metric_type'].astype(str)  # Ensure metric_type is string