diff --git a/app.py b/app.py
index aace69c..ce80974 100644
--- a/app.py
+++ b/app.py
@@ -18,6 +18,9 @@ from fastapi.websockets import WebSocketDisconnect
 from websockets.exceptions import ConnectionClosedError
 from ultralytics import YOLO
 
+# Import shared pipeline functions
+from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
+
 app = FastAPI()
 
 # Global dictionaries to keep track of models and streams
@@ -57,161 +60,6 @@ WORKER_TIMEOUT_MS = 10000
 streams_lock = threading.Lock()
 models_lock = threading.Lock()
 
-####################################################
-# Pipeline (Model)-loading helper functions
-####################################################
-def load_pipeline_node(node_config: dict, models_dir: str) -> dict:
-    """
-    Recursively load a model node.
-    Expects node_config to have:
-      - modelId: a unique identifier
-      - modelFile: the .pt file in models_dir
-      - triggerClasses: list of class names that activate child branches
-      - crop: boolean; if True, we crop to the bounding box for the next model
-      - minConfidence: (optional) minimum confidence required to enter this branch
-      - branches: list of child node configurations
-    """
-    model_path = os.path.join(models_dir, node_config["modelFile"])
-    if not os.path.exists(model_path):
-        logging.error(f"Model file {model_path} not found.")
-        raise FileNotFoundError(f"Model file {model_path} not found.")
-    
-    logging.info(f"Loading model for node {node_config['modelId']} from {model_path}")
-    model = YOLO(model_path)
-    if torch.cuda.is_available():
-        model.to("cuda")
-    
-    node = {
-        "modelId": node_config["modelId"],
-        "modelFile": node_config["modelFile"],
-        "triggerClasses": node_config.get("triggerClasses", []),
-        "crop": node_config.get("crop", False),
-        "minConfidence": node_config.get("minConfidence", None),  # NEW FIELD
-        "model": model,
-        "branches": []
-    }
-    for child_config in node_config.get("branches", []):
-        child_node = load_pipeline_node(child_config, models_dir)
-        node["branches"].append(child_node)
-    return node
-
-def load_pipeline_from_zip(zip_url: str, target_dir: str) -> dict:
-    """
-    Download the .mpta file from zip_url, extract it to target_dir,
-    and load the pipeline configuration (pipeline.json).
-    Returns the model tree (root node) loaded with YOLO models.
-    """
-    os.makedirs(target_dir, exist_ok=True)
-    zip_path = os.path.join(target_dir, "pipeline.mpta")
-    
-    try:
-        response = requests.get(zip_url, stream=True)
-        if response.status_code == 200:
-            with open(zip_path, "wb") as f:
-                for chunk in response.iter_content(chunk_size=8192):
-                    f.write(chunk)
-            logging.info(f"Downloaded .mpta file from {zip_url} to {zip_path}")
-        else:
-            logging.error(f"Failed to download .mpta file (status {response.status_code})")
-            return None
-    except Exception as e:
-        logging.error(f"Exception downloading .mpta file from {zip_url}: {e}")
-        return None
-
-    # Extract the .mpta file
-    try:
-        with zipfile.ZipFile(zip_path, "r") as zip_ref:
-            zip_ref.extractall(target_dir)
-        logging.info(f"Extracted .mpta file to {target_dir}")
-    except Exception as e:
-        logging.error(f"Failed to extract .mpta file: {e}")
-        return None
-    finally:
-        if os.path.exists(zip_path):
-            os.remove(zip_path)
-
-    # Load pipeline.json
-    pipeline_json_path = os.path.join(target_dir, "pipeline.json")
-    if not os.path.exists(pipeline_json_path):
-        logging.error("pipeline.json not found in the .mpta file")
-        return None
-
-    try:
-        with open(pipeline_json_path, "r") as f:
-            pipeline_config = json.load(f)
-        # Build the model tree recursively
-        model_tree = load_pipeline_node(pipeline_config["pipeline"], target_dir)
-        return model_tree
-    except Exception as e:
-        logging.error(f"Error loading pipeline.json: {e}")
-        return None
-
-####################################################
-# Model execution function
-####################################################
-def run_pipeline(frame, node: dict):
-    """
-    Run the model at the current node.
-    - Select the highest-confidence detection (if any).
-    - If 'crop' is True, crop to the bounding box for the next stage.
-    - If the detected class matches a branch's triggerClasses, check the confidence.
-      If the detection's confidence is below branch["minConfidence"] (if specified),
-      do not enter the branch and return the current detection.
-    Returns the final detection result (dict) or None.
-    """
-    try:
-        results = node["model"].track(frame, stream=False, persist=True)
-        detection = None
-        max_conf = -1
-        best_box = None
-        
-        for r in results:
-            for box in r.boxes:
-                box_cpu = box.cpu()
-                conf = float(box_cpu.conf[0])
-                if conf > max_conf and hasattr(box, "id") and box.id is not None:
-                    max_conf = conf
-                    detection = {
-                        "class": node["model"].names[int(box_cpu.cls[0])],
-                        "confidence": conf,
-                        "id": box.id.item(),
-                    }
-                    best_box = box_cpu
-
-        # If there's a detection and crop is True, crop frame to bounding box
-        if detection and node.get("crop", False) and best_box is not None:
-            coords = best_box.xyxy[0]  # [x1, y1, x2, y2]
-            x1, y1, x2, y2 = map(int, coords)
-            h, w = frame.shape[:2]
-            x1 = max(0, x1)
-            y1 = max(0, y1)
-            x2 = min(w, x2)
-            y2 = min(h, y2)
-            
-            if x2 > x1 and y2 > y1:
-                frame = frame[y1:y2, x1:x2]  # crop the frame
-
-        if detection is not None:
-            # Check if any branch should be entered based on trigger classes
-            for branch in node["branches"]:
-                if detection["class"] in branch.get("triggerClasses", []):
-                    # Check for a minimum confidence threshold for this branch
-                    min_conf = branch.get("minConfidence")
-                    if min_conf is not None and detection["confidence"] < min_conf:
-                        logging.debug(
-                            f"Detection confidence {detection['confidence']} below threshold "
-                            f"{min_conf} for branch {branch['modelId']}. Ending pipeline at current node."
-                        )
-                        return detection
-                    branch_detection = run_pipeline(frame, branch)
-                    if branch_detection is not None:
-                        return branch_detection
-            return detection
-        return None
-    except Exception as e:
-        logging.error(f"Error running pipeline on node {node.get('modelId')}: {e}")
-        return None
-
 ####################################################
 # Detection and frame processing functions
 ####################################################
diff --git a/pipeline_webcam.py b/pipeline_webcam.py
new file mode 100644
index 0000000..d13af2a
--- /dev/null
+++ b/pipeline_webcam.py
@@ -0,0 +1,52 @@
+import argparse
+import os
+import cv2
+import time
+import logging
+
+from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
+
+logging.basicConfig(level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(message)s")
+
+def main(mpta_url: str, video_source: str):
+    extraction_dir = os.path.join("models", "webcam_pipeline")
+    logging.info(f"Loading pipeline from {mpta_url}")
+    model_tree = load_pipeline_from_zip(mpta_url, extraction_dir)
+    if model_tree is None:
+        logging.error("Failed to load pipeline.")
+        return
+
+    cap = cv2.VideoCapture(video_source)
+    if not cap.isOpened():
+        logging.error(f"Cannot open video source {video_source}")
+        return
+
+    logging.info("Press 'q' to exit.")
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            logging.error("Failed to capture frame.")
+            break
+
+        detection, bbox = run_pipeline(frame, model_tree, return_bbox=True)
+        if bbox:
+            x1, y1, x2, y2 = bbox
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            label = detection["class"] if detection else "Detection"
+            cv2.putText(frame, label, (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+        cv2.imshow("Pipeline Webcam", frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run pipeline webcam utility.")
+    parser.add_argument("--mpta-url", type=str, required=True, help="URL to the pipeline mpta (ZIP) file.")
+    parser.add_argument("--video", type=str, default="0", help="Video source (default webcam index 0).")
+    args = parser.parse_args()
+    video_source = int(args.video) if args.video.isdigit() else args.video
+    main(args.mpta_url, video_source)
diff --git a/siwatsystem/pympta.py b/siwatsystem/pympta.py
new file mode 100644
index 0000000..b5f05d1
--- /dev/null
+++ b/siwatsystem/pympta.py
@@ -0,0 +1,135 @@
+import os
+import json
+import logging
+import torch
+import cv2
+import requests
+import zipfile
+from ultralytics import YOLO
+
+def load_pipeline_node(node_config: dict, models_dir: str) -> dict:
+    # Recursively load a model node from configuration.
+    model_path = os.path.join(models_dir, node_config["modelFile"])
+    if not os.path.exists(model_path):
+        logging.error(f"Model file {model_path} not found.")
+        raise FileNotFoundError(f"Model file {model_path} not found.")
+    logging.info(f"Loading model for node {node_config['modelId']} from {model_path}")
+    model = YOLO(model_path)
+    if torch.cuda.is_available():
+        model.to("cuda")
+    node = {
+        "modelId": node_config["modelId"],
+        "modelFile": node_config["modelFile"],
+        "triggerClasses": node_config.get("triggerClasses", []),
+        "crop": node_config.get("crop", False),
+        "minConfidence": node_config.get("minConfidence", None),
+        "model": model,
+        "branches": []
+    }
+    for child in node_config.get("branches", []):
+        node["branches"].append(load_pipeline_node(child, models_dir))
+    return node
+
+def load_pipeline_from_zip(zip_url: str, target_dir: str) -> dict:
+    # Download, extract, and load a pipeline configuration from a zip (.mpta) file.
+    os.makedirs(target_dir, exist_ok=True)
+    zip_path = os.path.join(target_dir, "pipeline.mpta")
+    try:
+        response = requests.get(zip_url, stream=True)
+        if response.status_code == 200:
+            with open(zip_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    f.write(chunk)
+            logging.info(f"Downloaded .mpta file from {zip_url} to {zip_path}")
+        else:
+            logging.error(f"Failed to download .mpta file (status {response.status_code})")
+            return None
+    except Exception as e:
+        logging.error(f"Exception downloading .mpta file from {zip_url}: {e}")
+        return None
+
+    try:
+        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            zip_ref.extractall(target_dir)
+        logging.info(f"Extracted .mpta file to {target_dir}")
+    except Exception as e:
+        logging.error(f"Failed to extract .mpta file: {e}")
+        return None
+    finally:
+        if os.path.exists(zip_path):
+            os.remove(zip_path)
+
+    pipeline_json_path = os.path.join(target_dir, "pipeline.json")
+    if not os.path.exists(pipeline_json_path):
+        logging.error("pipeline.json not found in the .mpta file")
+        return None
+
+    try:
+        with open(pipeline_json_path, "r") as f:
+            pipeline_config = json.load(f)
+        return load_pipeline_node(pipeline_config["pipeline"], target_dir)
+    except Exception as e:
+        logging.error(f"Error loading pipeline.json: {e}")
+        return None
+
+def run_pipeline(frame, node: dict, return_bbox: bool = False):
+    """
+    Processes the frame with the given pipeline node. When return_bbox is True,
+    the function returns a tuple (detection, bbox) where bbox is (x1,y1,x2,y2)
+    for drawing. Otherwise, returns only the detection.
+    """
+    try:
+        results = node["model"].track(frame, stream=False, persist=True)
+        detection = None
+        best_box = None
+        max_conf = -1
+
+        for r in results:
+            for box in r.boxes:
+                box_cpu = box.cpu()
+                conf = float(box_cpu.conf[0])
+                if conf > max_conf and hasattr(box, "id") and box.id is not None:
+                    max_conf = conf
+                    detection = {
+                        "class": node["model"].names[int(box_cpu.cls[0])],
+                        "confidence": conf,
+                        "id": box.id.item()
+                    }
+                    best_box = box_cpu
+
+        bbox = None
+        if detection and node.get("crop", False) and best_box is not None:
+            coords = best_box.xyxy[0]
+            x1, y1, x2, y2 = map(int, coords)
+            h, w = frame.shape[:2]
+            x1, y1 = max(0, x1), max(0, y1)
+            x2, y2 = min(w, x2), min(h, y2)
+            if x2 > x1 and y2 > y1:
+                bbox = (x1, y1, x2, y2)
+                frame = frame[y1:y2, x1:x2]
+
+        if detection is not None:
+            for branch in node["branches"]:
+                if detection["class"] in branch.get("triggerClasses", []):
+                    min_conf = branch.get("minConfidence")
+                    if min_conf is not None and detection["confidence"] < min_conf:
+                        logging.debug(f"Confidence {detection['confidence']} below threshold {min_conf} for branch {branch['modelId']}.")
+                        if return_bbox:
+                            return detection, bbox
+                        return detection
+                    res = run_pipeline(frame, branch, return_bbox)
+                    if res is not None:
+                        if return_bbox:
+                            return res
+                        return res
+            if return_bbox:
+                return detection, bbox
+            return detection
+        if return_bbox:
+            return None, None
+        return None
+    except Exception as e:
+        logging.error(f"Error running pipeline on node {node.get('modelId')}: {e}")
+        if return_bbox:
+            return None, None
+        return None