diff --git a/README.md b/README.md
index d99e27bc8e9ba7f2f3af159ab36244ef2dc83162..ea61c1574773ab44ad1e358596e9bb152cde0163 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@ emoji: 🌍
 colorFrom: red
 colorTo: pink
 sdk: gradio
-sdk_version: 5.11.0
+sdk_version: 5.12.0
 app_file: app.py
 pinned: false
 license: apache-2.0
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..91e6532db6632467bdc806d325a3f243b7b43a5a
--- /dev/null
+++ b/app.py
@@ -0,0 +1,212 @@
+# from io import BytesIO
+import gradio as gr
+import pandas as pd
+import numpy as np
+import torch
+import os
+from aitiautils.model_loader import ModelLoader
+import tempfile
+import matplotlib.pyplot as plt
+import traceback as tb
+
+# True 이면, tmp directory 에 파일 존재 유무와 상관없이 항상 새로운 이미지 생성
+ALWAYS_RECREATE_IMAGE = os.getenv("ALWAYS_RECREATE_IMAGE", "False").lower() == "true"
+selected_columns = ["subject_id", "no_p", "Rhythm", "Electric axis of the heart", "Etc"]
+train_df = pd.read_csv("./res/ludb/dataset/train_for_public.csv").drop_duplicates(
+    subset=["subject_id"]
+)[selected_columns]
+valid_df = pd.read_csv("./res/ludb/dataset/valid_for_public.csv").drop_duplicates(
+    subset=["subject_id"]
+)[selected_columns]
+test_df = pd.read_csv("./res/ludb/dataset/test_for_public.csv").drop_duplicates(
+    subset=["subject_id"]
+)[selected_columns]
+cutoffs = [0.001163482666015625, 0.15087890625, -0.587890625]
+lead_names = ["I", "II", "III", "aVR", "aVL", "aVF", "V1", "V2", "V3", "V4", "V5", "V6"]
+
+
+def gen_seg(subject_id):
+    input = np.load(f"./res/ludb/ecg_np/{subject_id}.npy")
+    network = ModelLoader("./res/models/hrnetv2/checkpoint.pth").get_network()
+    output: torch.Tensor = network(torch.from_numpy(input)).detach().numpy()
+    seg = [(output[:, i, :] >= cutoffs[i]).astype(int) for i in range(len(cutoffs))]
+    return input, np.stack(seg, axis=1)
+
+
+def concat_short_interval(seg, th):
+    """seg에서 구간(1)과 구간(1) 사이에 th 보다 짧은 부분(0)을 이어 붙인다. (0 -> 1)"""
+    # seg 에서 같은 구간끼리 그룹을 만듦. ex: seg = [0, 0, 1, 1, 0, 1, 1, 1, 1] -> seg_groups=[[0, 0], [1, 1], [0], [1, 1, 1, 1]]]
+    seg_groups = np.split(seg, np.where(np.diff(seg) != 0)[0] + 1)
+    for i in range(1, len(seg_groups) - 1):  # 첫 번째와 마지막 그룹 제외
+        group = seg_groups[i]
+        if len(group) <= th and np.all(group == 0):
+            seg_groups[i] = np.ones_like(group)  # 0 -> 1
+
+    return np.concatenate(seg_groups)
+
+
+def remove_short_duration(seg, th):
+    """seg의 구간(1)중에 th 보다 짧은 구간은 제거 (1 -> 0)"""
+    seg_groups = np.split(seg, np.where(np.diff(seg) != 0)[0] + 1)
+    for i, group in enumerate(seg_groups):
+        if len(group) <= th and np.all(group == 1):
+            seg_groups[i] = np.zeros_like(group)  # 1 -> 0
+
+    return np.concatenate(seg_groups)
+
+
+def gen_each_image(input, seg, image_path, ths, pp=False):
+    fig = plt.figure(figsize=(15, 18))
+    plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02, hspace=0.2)
+    for idx, (in_by_lead, seg_by_lead) in enumerate(zip(input, seg)):
+        sub_fig = fig.add_subplot(12, 1, idx + 1)
+        sub_fig.text(
+            0.02,
+            0.5,
+            f"{lead_names[idx]}",
+            fontsize=9,
+            fontweight="bold",
+            ha="center",
+            va="center",
+            rotation=90,
+            transform=sub_fig.transAxes,
+        )
+        sub_fig.set_xticks([])
+        sub_fig.set_yticks([])
+        sub_fig.plot(
+            range(len(in_by_lead[0])), in_by_lead[0], color="black", linewidth=1.0
+        )
+        p_seg = seg_by_lead[0]
+        qrs_seg = seg_by_lead[1]
+        t_seg = seg_by_lead[2]
+        if pp:
+            p_seg = remove_short_duration(concat_short_interval(p_seg, ths[0]), ths[1])
+            qrs_seg = remove_short_duration(
+                concat_short_interval(qrs_seg, ths[2]), ths[3]
+            )
+            t_seg = remove_short_duration(concat_short_interval(t_seg, ths[4]), ths[5])
+        sub_fig.plot(
+            range(len(p_seg)), p_seg / 2, label="P", color="red", linewidth=0.7
+        )
+        sub_fig.plot(
+            range(len(qrs_seg)), qrs_seg, label="QRS", color="green", linewidth=0.7
+        )
+        sub_fig.plot(
+            range(len(t_seg)), t_seg / 2, label="T", color="blue", linewidth=0.7
+        )
+    plt.savefig(image_path, dpi=150)
+    plt.close()
+
+
+def gen_image(subject_id, image_path, pp_image_path, ths):
+    try:
+        input, seg = gen_seg(subject_id)
+        gen_each_image(input, seg, image_path, ths)
+        gen_each_image(input, seg, pp_image_path, ths, True)
+        return True
+    except Exception:
+        print(tb.format_exc())
+        return False
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("App"):
+        with gr.Row():
+            gr.Textbox(
+                "Welcome to visit ECG-Delineation space",
+                label="Information",
+                lines=1,
+            )
+        gr_dfs = []
+        with gr.Row():
+            gr_dfs.append(
+                gr.Dataframe(
+                    value=train_df,
+                    interactive=False,
+                    max_height=250,
+                    label="our train dataset. (source: ./res/ludb/dataset/train_for_public.csv)",
+                )
+            )
+
+        with gr.Row():
+            gr_dfs.append(
+                gr.Dataframe(
+                    value=valid_df,
+                    interactive=False,
+                    max_height=250,
+                    label="our valid dataset. (source: ./res/ludb/dataset/valid_for_public.csv)",
+                )
+            )
+
+        with gr.Row():
+            gr_dfs.append(
+                gr.Dataframe(
+                    value=test_df,
+                    interactive=False,
+                    max_height=250,
+                    label="our test dataset. (source: ./res/ludb/dataset/test_for_public.csv)",
+                )
+            )
+
+        with gr.Row():
+            gr_ths = [
+                gr.Textbox(
+                    label="Interval Threshold of P (ms)",
+                    lines=1,
+                    value="10",
+                ),
+                gr.Textbox(
+                    label="Duration Threshold of P (ms)",
+                    lines=1,
+                    value="50",
+                ),
+                gr.Textbox(
+                    label="Interval Threshold of QRS (ms)",
+                    lines=1,
+                    value="50",
+                ),
+                gr.Textbox(
+                    label="Duration Threshold of QRS (ms)",
+                    lines=1,
+                    value="50",
+                ),
+                gr.Textbox(
+                    label="Interval Threshold of T (ms)",
+                    lines=1,
+                    value="30",
+                ),
+                gr.Textbox(
+                    label="Duration Threshold of T (ms)",
+                    lines=1,
+                    value="50",
+                ),
+            ]
+
+        with gr.Row():
+            gr_image = gr.Image(type="filepath", label="Output")
+            gr_pp_image = gr.Image(type="filepath", label="PostProcessed Output")
+
+        def show_image(df: pd.DataFrame, evt: gr.SelectData, *ths):
+            subject_id = evt.row_value[0]
+
+            image_path = f"{tempfile.gettempdir()}/ludb_{subject_id}.png"
+            pp_image_path = f"{tempfile.gettempdir()}/ludb_{subject_id}_pp.png"
+            if not ALWAYS_RECREATE_IMAGE and (
+                os.path.exists(image_path) and os.path.exists(pp_image_path)
+            ):
+                return [image_path, pp_image_path]
+
+            gen_image(
+                subject_id, image_path, pp_image_path, [int(th) / 2 for th in ths]
+            )
+
+            return [image_path, pp_image_path]
+
+        for gr_df in gr_dfs:
+            gr_df.select(
+                fn=show_image,
+                inputs=[gr_df, *gr_ths],
+                outputs=[gr_image, gr_pp_image],
+            )
+
+demo.launch()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..55c9d16278d9a4bf66dad7535a121ca6226d575b
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+./res/whl/aitiautils-1.1.59-py3-none-any.whl[torch]
+gradio==5.12.0
\ No newline at end of file
diff --git a/res/ludb/test_for_public.csv b/res/ludb/dataset/test_for_public.csv
similarity index 100%
rename from res/ludb/test_for_public.csv
rename to res/ludb/dataset/test_for_public.csv
diff --git a/res/ludb/train_for_public.csv b/res/ludb/dataset/train_for_public.csv
similarity index 100%
rename from res/ludb/train_for_public.csv
rename to res/ludb/dataset/train_for_public.csv
diff --git a/res/ludb/valid_for_public.csv b/res/ludb/dataset/valid_for_public.csv
similarity index 100%
rename from res/ludb/valid_for_public.csv
rename to res/ludb/dataset/valid_for_public.csv
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+size 240128
diff --git a/res/ludb/ecg_np/98.npy b/res/ludb/ecg_np/98.npy
new file mode 100644
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--- /dev/null
+++ b/res/ludb/ecg_np/98.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df68e24d653fa81beedea4f8eda93a3555c7ab14e0c63c7a6441dc5d2accd9a4
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diff --git a/res/ludb/ecg_np/99.npy b/res/ludb/ecg_np/99.npy
new file mode 100644
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+++ b/res/ludb/ecg_np/99.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77ef9baa7f380bee1729475307f9b4835a22e3abb70b65af405c42802a08b70a
+size 240128
diff --git a/res/models/hrnetv2/best_config.json b/res/models/hrnetv2/best_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..10dcaab8aed932b15741442c6d8e8f047a2e4d5c
--- /dev/null
+++ b/res/models/hrnetv2/best_config.json
@@ -0,0 +1,151 @@
+{
+    "train": {
+        "progress": true,
+        "random_seed": 2407041220,
+        "resume_dir": [],
+        "checkpoint_dir": "/bfai/nfs_export/workspace/share/result/wogh/hrnet/train-240704_123013",
+        "checkpoint_save_freq": 1,
+        "working_dir": "",
+        "user": "wogh",
+        "name": "hrnet",
+        "exp_name": "wogh:hrnet",
+        "type": "supervised",
+        "task": "segmentation",
+        "epochs": 501,
+        "batch_size": 64,
+        "hpo": {
+            "num_samples": 256,
+            "criteria": {
+                "jaccard_avg": 1
+            },
+            "scheduler": {
+                "ASHAScheduler": {
+                    "grace_period": 200,
+                    "max_t": 501
+                }
+            }
+        },
+        "label": {
+            "num_labels": 3,
+            "path": [
+                "/bfai/nfs_export/workspace/share/labels/pqrst/ludb/train.csv",
+                "/bfai/nfs_export/workspace/share/labels/pqrst/ludb/valid.csv",
+                "/bfai/nfs_export/workspace/share/labels/pqrst/ludb/test.csv"
+            ],
+            "target": [
+                "p_onoffs",
+                "qrs_onoffs",
+                "t_onoffs"
+            ],
+            "split_ratio": [
+                1,
+                1,
+                1
+            ]
+        },
+        "resource_per_trial": {
+            "num_workers": 1,
+            "num_gpus_per_worker": 1,
+            "num_cpus_per_worker": 16
+        },
+        "comment": "",
+        "tracking": true,
+        "available_resources": {
+            "available_gpus": 16.0
+        }
+    },
+    "solver": {
+        "SolverPQRST": {
+            "mixed_precision": true,
+            "gradient_clip": 0.1
+        }
+    },
+    "datasets": [
+        {
+            "ECGPQRSTDataset": {
+                "lead_type": [
+                    "I",
+                    "II",
+                    "III",
+                    "aVR",
+                    "aVL",
+                    "aVF",
+                    "V1",
+                    "V2",
+                    "V3",
+                    "V4",
+                    "V5",
+                    "V6"
+                ],
+                "aux_data": [],
+                "normalization": "z_norm",
+                "second": 10,
+                "hz": 500
+            }
+        }
+    ],
+    "models": [
+        {
+            "network": {
+                "HRNetV2": {
+                    "data_len": 5000,
+                    "kernel_size": 5,
+                    "dilation": 1,
+                    "num_stages": 3,
+                    "num_blocks": 6,
+                    "num_modules": [
+                        1,
+                        1,
+                        1,
+                        4,
+                        3
+                    ],
+                    "use_bottleneck": [
+                        1,
+                        0,
+                        0,
+                        0,
+                        0
+                    ],
+                    "stage1_channels": 128,
+                    "num_channels_init": 48,
+                    "interpolate_mode": "linear",
+                    "task": "segmentation",
+                    "num_leads": 12,
+                    "num_aux": 0,
+                    "output_size": 3,
+                    "aux_output_size": 0
+                }
+            },
+            "optimizer": [
+                {
+                    "SGD": {
+                        "lr": 0.0983058839402403,
+                        "momentum": 0.9,
+                        "weight_decay": 0.0003850652731758502,
+                        "sharpness_min": false
+                    }
+                }
+            ],
+            "scheduler": [
+                {
+                    "PolynomialLR": {
+                        "total_iters": 501,
+                        "power": 0.0
+                    }
+                }
+            ]
+        }
+    ],
+    "loss_fns": [
+        {
+            "BCEWithLogitsLoss": {}
+        }
+    ],
+    "cur_epoch": 358,
+    "cutoff": [
+        0.001163482666015625,
+        0.15087890625,
+        -0.587890625
+    ]
+}
\ No newline at end of file
diff --git a/res/models/hrnetv2/checkpoint.pth b/res/models/hrnetv2/checkpoint.pth
new file mode 100644
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--- /dev/null
+++ b/res/models/hrnetv2/checkpoint.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fd6e031d55efa48257225ebb2dce0e861fc02080830e655c61a618a227e1537
+size 44641506
diff --git a/res/whl/aitiautils-1.1.59-py3-none-any.whl b/res/whl/aitiautils-1.1.59-py3-none-any.whl
new file mode 100644
index 0000000000000000000000000000000000000000..dd77de4349e48d241c752cc1c914da9691671435
--- /dev/null
+++ b/res/whl/aitiautils-1.1.59-py3-none-any.whl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99bb506c65d15b19d19691156efaa2fd15dcc78ea31913f817417c31d008100b
+size 14134456
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2c6e97a4975dcc8514085aac729931cabc9f3a4
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,124 @@
+def show_segmentation(ti=0, target_dir: str = "./seg"):
+    fig = plt.figure(figsize=(15, 2 * len(Alg) * 2))
+    fig.subplots_adjust(hspace=0, wspace=0.1)
+
+    # for lead_cnt, ti in enumerate(range(12 * target_idx, 12 * (target_idx + 1))):
+    test_df = pd.read_csv(test_path)
+    lead_type = ast.literal_eval(test_df.iloc[ti]["lead_type"])[0]
+    seq = test_df.iloc[ti]["seq"]
+    object_id: str = test_df.iloc[ti]["objectid"]
+    ecg_data_path = f"/bfai/data/ecg_data/{object_id[18:22]}/{object_id}.json"
+    # ecg_data_path = test_df.iloc[ti]["file_path"]
+    ecg_data = []
+    with open(ecg_data_path) as ecg_data_file:
+        ecg_json = json.load(ecg_data_file)
+        ecg_data = (
+            np.array(ecg_json["waveform"]["data"][lead_type])
+            * ecg_json["study"]["mv_unit"]
+        )
+        if seq != "1/1":
+            seq_idx, seq_range = [int(str_seq) for str_seq in seq.split("/")]
+            use_lead_length = 5000  # (500 * 10)
+            total_use_length = use_lead_length * seq_range
+            front_idx = int((len(ecg_data) - total_use_length) / 2)
+            ecg_data = ecg_data[front_idx:]
+
+    for alg, alg_idx in ALG_ORDER.items():
+        for pp_type_idx, pp_type in enumerate(["ori", "pp"]):
+            sub_fig = fig.add_subplot(len(Alg) * 2, 1, 2 * alg_idx + pp_type_idx + 1)
+            # sub_fig.set_title(f"{alg} - Lead {lead_type}")
+            sub_fig.text(
+                0.02,
+                0.5,
+                # f"{alg} {pp_type} - {lead_type}",
+                f"{alg} {pp_type}\n{lead_type}",
+                fontsize=9,
+                fontweight="bold",
+                ha="center",
+                va="center",
+                rotation=90,
+                transform=sub_fig.transAxes,
+            )
+            sub_fig.set_xticks([])
+            sub_fig.set_yticks([])
+
+            sub_fig.plot(range(len(ecg_data)), ecg_data, color="black", linewidth=1.0)
+            sub_fig.plot(
+                range(len(output[alg][ti][0])),
+                (output[alg][ti][0] >= cutoff[alg][0]).astype(int) / 2
+                if pp_type_idx == 0
+                else (pp_output[alg][ti][0]).astype(int) / 2,
+                label="P",
+                color="red",
+                linewidth=0.7,
+            )
+            sub_fig.plot(
+                range(len(output[alg][ti][1])),
+                (output[alg][ti][1] >= cutoff[alg][1]).astype(int)
+                if pp_type_idx == 0
+                else (pp_output[alg][ti][1]).astype(int),
+                label="QRS",
+                color="green",
+                linewidth=0.7,
+            )
+            sub_fig.plot(
+                (output[alg][ti][2] >= cutoff[alg][2]).astype(int) / 2
+                if pp_type_idx == 0
+                else (pp_output[alg][ti][2]).astype(int) / 2,
+                label="T",
+                color="blue",
+                linewidth=0.7,
+            )
+
+            sub_fig.plot(
+                range(len(origin_seg[ti][0])),
+                ((origin_seg[ti][0] > 0).astype(int) * (-1)) / 2,
+                label="P Label",
+                color="salmon",
+                linewidth=0.7,
+            )
+            sub_fig.plot(
+                range(len(origin_seg[ti][1])),
+                ((origin_seg[ti][1] > 0).astype(int) * (-1)),
+                label="QRS Label",
+                color="seagreen",
+                linewidth=0.7,
+            )
+            sub_fig.plot(
+                range(len(origin_seg[ti][2])),
+                ((origin_seg[ti][2] > 0).astype(int) * (-1)) / 2,
+                label="T Label",
+                color="darkslateblue",
+                linewidth=0.7,
+            )
+
+            sub_fig.plot(
+                range(len(origin_seg[ti][0])),
+                ((origin_seg[ti][0] < 0).astype(int) * (-1)),
+                label="P UnLabeled",
+                linestyle=":",
+                color="salmon",
+                linewidth=0.5,
+            )
+            sub_fig.plot(
+                range(len(origin_seg[ti][1])),
+                ((origin_seg[ti][1] < 0).astype(int) * (-1)),
+                label="QRS UnLabeled",
+                linestyle=":",
+                color="seagreen",
+                linewidth=0.5,
+            )
+            sub_fig.plot(
+                range(len(origin_seg[ti][2])),
+                ((origin_seg[ti][2] < 0).astype(int) * (-1)),
+                label="T UnLabeled",
+                linestyle=":",
+                color="darkslateblue",
+                linewidth=0.5,
+            )
+            # sub_fig.legend()
+    plt.savefig(
+        f"./{target_dir}/{test_df.iloc[ti]['lead_cnt']}_{object_id}_{lead_type}_{test_df.iloc[ti]['seq'].replace('/', '_')}.png",
+        dpi=150,
+    )
+    plt.close()
\ No newline at end of file