diff --git a/scripts/demo.py b/scripts/demo.py
new file mode 100644
index 0000000..9f9eb00
--- /dev/null
+++ b/scripts/demo.py
@@ -0,0 +1,120 @@
+import argparse
+import os
+import os.path as osp
+import numpy as np
+import cv2
+import torch
+import gc
+import sys
+sys.path.append("./sam2")
+from sam2.build_sam import build_sam2_video_predictor
+
+color = [(0, 0, 255)]
+
+def load_txt(gt_path):
+    with open(gt_path, 'r') as f:
+        gt = f.readlines()
+    prompts = {}
+    for fid, line in enumerate(gt):
+        x, y, w, h = map(float, line.split(','))
+        x, y, w, h = int(x), int(y), int(w), int(h)
+        prompts[fid] = ((x, y, x + w, y + h), 0)
+    return prompts
+
+def determine_model_cfg(model_path):
+    if "large" in model_path:
+        return "configs/sam2.1/sam2.1_hiera_l.yaml"
+    elif "base_plus" in model_path:
+        return "configs/sam2.1/sam2.1_hiera_b+.yaml"
+    elif "small" in model_path:
+        return "configs/sam2.1/sam2.1_hiera_s.yaml"
+    elif "tiny" in model_path:
+        return "configs/sam2.1/sam2.1_hiera_t.yaml"
+    else:
+        raise ValueError("Unknown model size in path!")
+
+def prepare_frames_or_path(video_path):
+    if video_path.endswith(".mp4") or osp.isdir(video_path):
+        return video_path
+    else:
+        raise ValueError("Invalid video_path format. Should be .mp4 or a directory of jpg frames.")
+
+def main(args):
+    model_cfg = determine_model_cfg(args.model_path)
+    predictor = build_sam2_video_predictor(model_cfg, args.model_path, device="cuda:0")
+    frames_or_path = prepare_frames_or_path(args.video_path)
+    prompts = load_txt(args.txt_path)
+
+    if args.save_to_video:
+        if osp.isdir(args.video_path):
+            frames = sorted([osp.join(args.video_path, f) for f in os.listdir(args.video_path) if f.endswith(".jpg")])
+            loaded_frames = [cv2.imread(frame_path) for frame_path in frames]
+            height, width = loaded_frames[0].shape[:2]
+        else:
+            cap = cv2.VideoCapture(args.video_path)
+            loaded_frames = []
+            while True:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                loaded_frames.append(frame)
+            cap.release()
+            height, width = loaded_frames[0].shape[:2]
+
+            if len(loaded_frames) == 0:
+                raise ValueError("No frames were loaded from the video.")
+
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(args.video_output_path, fourcc, 30, (width, height))
+
+    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16):
+        state = predictor.init_state(frames_or_path, offload_video_to_cpu=True)
+        bbox, track_label = prompts[0]
+        _, _, masks = predictor.add_new_points_or_box(state, box=bbox, frame_idx=0, obj_id=0)
+
+        for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
+            mask_to_vis = {}
+            bbox_to_vis = {}
+
+            for obj_id, mask in zip(object_ids, masks):
+                mask = mask[0].cpu().numpy()
+                mask = mask > 0.0
+                non_zero_indices = np.argwhere(mask)
+                if len(non_zero_indices) == 0:
+                    bbox = [0, 0, 0, 0]
+                else:
+                    y_min, x_min = non_zero_indices.min(axis=0).tolist()
+                    y_max, x_max = non_zero_indices.max(axis=0).tolist()
+                    bbox = [x_min, y_min, x_max - x_min, y_max - y_min]
+                bbox_to_vis[obj_id] = bbox
+                mask_to_vis[obj_id] = mask
+
+            if args.save_to_video:
+                img = loaded_frames[frame_idx]
+                for obj_id, mask in mask_to_vis.items():
+                    mask_img = np.zeros((height, width, 3), np.uint8)
+                    mask_img[mask] = color[(obj_id + 1) % len(color)]
+                    img = cv2.addWeighted(img, 1, mask_img, 0.2, 0)
+
+                for obj_id, bbox in bbox_to_vis.items():
+                    cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[0] + bbox[2], bbox[1] + bbox[3]), color[obj_id % len(color)], 2)
+
+                out.write(img)
+
+        if args.save_to_video:
+            out.release()
+
+    del predictor, state
+    gc.collect()
+    torch.clear_autocast_cache()
+    torch.cuda.empty_cache()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--video_path", required=True, help="Input video path or directory of frames.")
+    parser.add_argument("--txt_path", required=True, help="Path to ground truth text file.")
+    parser.add_argument("--model_path", default="sam2/checkpoints/sam2.1_hiera_base_plus.pt", help="Path to the model checkpoint.")
+    parser.add_argument("--video_output_path", default="demo.mp4", help="Path to save the output video.")
+    parser.add_argument("--save_to_video", default=True, help="Save results to a video.")
+    args = parser.parse_args()
+    main(args)