fix: model calling method

core/detection/branches.py

@@ -438,11 +438,22 @@ class BranchProcessor:
                        f"({input_frame.shape[1]}x{input_frame.shape[0]}) with confidence={min_confidence}")
 
 
-            # Use .predict() method for both detection and classification models
+            # Determine model type and use appropriate calling method (like ML engineer's approach)
             inference_start = time.time()
-            detection_results = model.model.predict(input_frame, conf=min_confidence, verbose=False)
+
+            # Check if this is a classification model based on filename or model structure
+            is_classification = 'cls' in branch_id.lower() or 'classify' in branch_id.lower()
+
+            if is_classification:
+                # Use .predict() method for classification models (like ML engineer's classification_test.py)
+                detection_results = model.model.predict(source=input_frame, verbose=False)
+                logger.info(f"[INFERENCE DONE] {branch_id}: Classification completed in {time.time() - inference_start:.3f}s using .predict()")
+            else:
+                # Use direct model call for detection models (like ML engineer's detection_test.py)
+                detection_results = model.model(input_frame, conf=min_confidence, verbose=False)
+                logger.info(f"[INFERENCE DONE] {branch_id}: Detection completed in {time.time() - inference_start:.3f}s using direct call")
+
             inference_time = time.time() - inference_start
-            logger.info(f"[INFERENCE DONE] {branch_id}: Predict completed in {inference_time:.3f}s using .predict() method")
 
             # Initialize branch_detections outside the conditional
             branch_detections = []
@@ -648,17 +659,11 @@ class BranchProcessor:
             # Format key with context
             key = action.params['key'].format(**context)
 
-            # Convert image to bytes
+            # Get image format parameters
             import cv2
             image_format = action.params.get('format', 'jpeg')
             quality = action.params.get('quality', 90)
 
-            if image_format.lower() == 'jpeg':
-                encode_param = [cv2.IMWRITE_JPEG_QUALITY, quality]
-                _, image_bytes = cv2.imencode('.jpg', image_to_save, encode_param)
-            else:
-                _, image_bytes = cv2.imencode('.png', image_to_save)
-
             # Save to Redis synchronously using a sync Redis client
             try:
                 import redis