Fix JSON serialization for NumPy types

app.py

@@ -216,6 +216,10 @@ async def get_catalog():
         logger.error(traceback.format_exc())
         raise e
 
+from json_utils import convert_numpy_types, log_object_types
+
+# ... (imports remain the same)
+
 @app.get("/api/preview/{vertical}")
 async def get_preview(vertical: str):
     """Get preview data for a specific vertical"""
@@ -251,14 +255,17 @@ async def get_preview(vertical: str):
         # Get latest row for "Live Signals"
         latest = df.iloc[-1].to_dict()
         
-        return JSONResponse({
+        response_data = {
             "vertical": vertical,
             "latest": latest,
             "history": history,
             "total_rows": len(df)
-        })
-    except Exception as e:
-        logger.error(f"Error fetching preview: {e}")
+        }
+        
+        # Ensure all types are JSON serializable
+        response_data = convert_numpy_types(response_data)
+        
+        return JSONResponse(response_data)
     except Exception as e:
         logger.error(f"Error fetching preview: {e}")
         return JSONResponse({"error": str(e)}, status_code=500)
@@ -393,9 +400,24 @@ async def get_prediction(vertical: str):
         predictor = predictors[vertical]
         result = predictor.predict(latest_data)
         
+        # Convert NumPy types to Python types
+        result = convert_numpy_types(result)
+        
         return JSONResponse(result)
     except Exception as e:
         logger.error(f"Prediction failed: {e}")
+        # Detailed logging for debugging
+        logger.error("Dumping object types for debugging:")
+        try:
+            # We assume 'result' might be defined if the error happened during serialization
+            # If it happened before, this might fail, so we wrap it
+            if 'result' in locals():
+                log_object_types(result)
+            else:
+                logger.error("Result object was not created.")
+        except Exception as log_err:
+            logger.error(f"Failed to log object types: {log_err}")
+            
         return JSONResponse({"error": str(e)}, status_code=500)
 
 @app.get("/api/pnl")

json_utils.py

@@ -0,0 +1,44 @@
+import numpy as np
+import pandas as pd
+import logging
+
+logger = logging.getLogger(__name__)
+
+def convert_numpy_types(obj):
+    """
+    Recursively convert NumPy types to standard Python types for JSON serialization.
+    """
+    if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,
+                        np.int16, np.int32, np.int64, np.uint8,
+                        np.uint16, np.uint32, np.uint64)):
+        return int(obj)
+    elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):
+        return float(obj)
+    elif isinstance(obj, (np.ndarray,)):
+        return obj.tolist()
+    elif isinstance(obj, dict):
+        return {k: convert_numpy_types(v) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        return [convert_numpy_types(i) for i in obj]
+    elif isinstance(obj, (pd.Timestamp, pd.DatetimeIndex)):
+        return str(obj)
+    return obj
+
+def log_object_types(obj, prefix="", depth=0, max_depth=3):
+    """
+    Log the types of keys and values in a dictionary or list to help debug serialization issues.
+    """
+    if depth > max_depth:
+        return
+
+    indent = "  " * depth
+    if isinstance(obj, dict):
+        for k, v in obj.items():
+            logger.error(f"{indent}{prefix}Key: {k} ({type(k)}), Value Type: {type(v)}")
+            if isinstance(v, (dict, list)):
+                log_object_types(v, prefix=f"{k}.", depth=depth+1, max_depth=max_depth)
+    elif isinstance(obj, list):
+        for i, v in enumerate(obj[:5]): # Only check first 5 to avoid spam
+            logger.error(f"{indent}{prefix}Index: {i}, Value Type: {type(v)}")
+            if isinstance(v, (dict, list)):
+                log_object_types(v, prefix=f"[{i}].", depth=depth+1, max_depth=max_depth)