From f47cf52ae1cfc8e14e6853ab4f140a7a0ed025fc Mon Sep 17 00:00:00 2001
From: Dobromir Popov <dobromir.popov@gateway.one>
Date: Tue, 24 Jun 2025 21:25:20 +0300
Subject: [PATCH] fixes

---
 core/enhanced_orchestrator.py | 24 +++++++++++++++---------
 1 file changed, 15 insertions(+), 9 deletions(-)

diff --git a/core/enhanced_orchestrator.py b/core/enhanced_orchestrator.py
index 6bb6462..9539cff 100644
--- a/core/enhanced_orchestrator.py
+++ b/core/enhanced_orchestrator.py
@@ -1395,13 +1395,14 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
                     predictions = prediction_result.get('probabilities', [0.33, 0.33, 0.34])
                     confidence = prediction_result.get('confidence', 0.7)
                     
-                    # Convert predictions to numpy array first
+                    # Convert predictions to numpy array first using safe conversion
                     if isinstance(predictions, np.ndarray):
                         predictions_array = predictions.flatten()
                     elif isinstance(predictions, (list, tuple)):
                         predictions_array = np.array(predictions, dtype=np.float32).flatten()
                     else:
-                        predictions_array = np.array([float(predictions)], dtype=np.float32)
+                        # Use safe tensor conversion for single values
+                        predictions_array = np.array([self._safe_tensor_to_scalar(predictions, 0.5)], dtype=np.float32)
                     
                     # Create final predictions array with confidence
                     # Use safe tensor conversion to avoid scalar conversion errors
@@ -1416,7 +1417,7 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
                     # Handle (pred_class, pred_proba) tuple from CNN models
                     pred_class, pred_proba = prediction_result
                     
-                    # Flatten and process the probability array
+                    # Flatten and process the probability array using safe conversion
                     if isinstance(pred_proba, np.ndarray):
                         if pred_proba.ndim > 1:
                             # Handle 2D arrays like [[0.1, 0.2, 0.7]]
@@ -1428,16 +1429,17 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
                         # Use the probability values as the predictions array
                         predictions = pred_proba_flat.astype(np.float32)
                     else:
-                        # Fallback: use class prediction only
-                        predictions = np.array([float(pred_class)], dtype=np.float32)
+                        # Fallback: use class prediction with safe conversion
+                        predictions = np.array([self._safe_tensor_to_scalar(pred_class, 0.5)], dtype=np.float32)
                 else:
-                    # Handle direct prediction result
+                    # Handle direct prediction result using safe conversion
                     if isinstance(prediction_result, np.ndarray):
                         predictions = prediction_result.flatten()
                     elif isinstance(prediction_result, (list, tuple)):
                         predictions = np.array(prediction_result, dtype=np.float32).flatten()
                     else:
-                        predictions = np.array([float(prediction_result)], dtype=np.float32)
+                        # Use safe tensor conversion for single tensor/scalar values
+                        predictions = np.array([self._safe_tensor_to_scalar(prediction_result, 0.5)], dtype=np.float32)
                 
                 # Extract hidden features if model supports it
                 hidden_features = None
@@ -4740,7 +4742,8 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
             Python float scalar value
         """
         try:
-            if hasattr(tensor_value, 'item'):
+            # Handle PyTorch tensors first
+            if hasattr(tensor_value, 'numel') and hasattr(tensor_value, 'item'):
                 # PyTorch tensor - handle different shapes
                 if tensor_value.numel() == 1:
                     return float(tensor_value.item())
@@ -4754,9 +4757,12 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
                     return float(tensor_value.flatten()[0])
                 else:
                     return float(tensor_value.flat[0])
+            elif hasattr(tensor_value, 'item') and not isinstance(tensor_value, np.ndarray):
+                # Other tensor types that have .item() method
+                return float(tensor_value.item())
             else:
                 # Already a scalar value
                 return float(tensor_value)
         except Exception as e:
-            logger.warning(f"Error converting tensor to scalar, using default {default_value}: {e}")
+            logger.debug(f"Error converting tensor to scalar, using default {default_value}: {e}")
             return default_value
\ No newline at end of file