diff --git a/README.md b/README.md
index 58bafbfe65b945f58ea22a14cb9b16c9b390d7c5..2f98a2e45786432b109da21a6395346909f8c83d 100644
--- a/README.md
+++ b/README.md
@@ -9,9 +9,11 @@ tags:
 - code
 ---
 # Anti-Spoofing dataset: live
-The dataset consists of 40,000 videos and selfies with unique people. 15,000 attack replays from 4,000 unique devices. 
+
+The dataset consists of 40,000 videos and selfies with unique people. 15,000 attack replays from 4,000 unique devices.
 
 # File with the extension .csv
+
 includes the following information for each media file:
 
 - **phone**: the device used to capture the media files,
@@ -25,13 +27,14 @@ includes the following information for each media file:
 - **video_file_type**: the type of the video
 
 # Folder "img" with media files
+
 - containg all the photos and videos
 - which correspond to the data in the .csv file
 
 **How it works**: *go to the first folder and you will make sure that it contains media files taken by a person whose parameters are specified in the first line of the .csv file.*
 
-In order to get access to more than 80,000 media files from 4,000 people or to learn more about our data, please contact our sales team by submitting a request on our website https://trainingdata.pro/data-market?utm_source=huggingface or emaling us at sales@trainingdata.pro
+In order to get access to more than 80,000 media files from 4,000 people or to learn more about our data, please contact our sales team by submitting a request on our website <https://trainingdata.pro/data-market?utm_source=huggingface> or emaling us at <sales@trainingdata.pro>
 
-More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
+More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>**
 
-TrainingData's GitHub: **https://github.com/trainingdata-pro**
\ No newline at end of file
+TrainingData's GitHub: **<https://github.com/trainingdata-pro>**
diff --git a/anti-spoofing_Live.py b/anti-spoofing_Live.py
new file mode 100644
index 0000000000000000000000000000000000000000..e10360fd592f752c9ee69323e0c5c268a3b8d262
--- /dev/null
+++ b/anti-spoofing_Live.py
@@ -0,0 +1,91 @@
+import io
+
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {anti-spoofing_Live},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of 40,000 videos and selfies with unique people.
+15,000 attack replays from 4,000 unique devices.
+"""
+_NAME = 'anti-spoofing_Live'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class PortraitAnd26Photos(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'phone': datasets.Value('string'),
+                'selfie': datasets.Image(),
+                'video': datasets.Value('string'),
+                'worker_id': datasets.Value('string'),
+                'age': datasets.Value('int8'),
+                'country': datasets.Value('string'),
+                'gender': datasets.Value('string')
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data = dl_manager.download(f"{_DATA}data.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        data = dl_manager.iter_archive(data)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={
+                                        "data": data,
+                                        'annotations': annotations
+                                    }),
+        ]
+
+    def _generate_examples(self, data, annotations):
+        annotations_df = pd.read_csv(annotations, sep=',')
+        for idx, (image_path, image) in enumerate(data):
+            if image_path.endswith('.jpg'):
+                yield idx, {
+                    'phone':
+                        annotations_df.loc[
+                            annotations_df['selfie_link'] == image_path]
+                        ['phone'].values[0],
+                    'selfie': {
+                        'path': image_path,
+                        'bytes': image.read()
+                    },
+                    'video':
+                        annotations_df.loc[
+                            annotations_df['selfie_link'] == image_path]
+                        ['video_link'].values[0],
+                    'worker_id':
+                        annotations_df.loc[
+                            annotations_df['selfie_link'] == image_path]
+                        ['worker_id'].values[0],
+                    'age':
+                        annotations_df.loc[
+                            annotations_df['selfie_link'] == image_path]
+                        ['age'].values[0],
+                    'country':
+                        annotations_df.loc[
+                            annotations_df['selfie_link'] == image_path]
+                        ['country'].values[0],
+                    'gender':
+                        annotations_df.loc[
+                            annotations_df['selfie_link'] == image_path]
+                        ['gender'].values[0],
+                }
diff --git a/live_30.csv b/data/anti-spoofing_Live.csv
similarity index 86%
rename from live_30.csv
rename to data/anti-spoofing_Live.csv
index b040d2cb1adb97f0827e211d62f56cc9943fb71d..4e6e459d20a9e71a5e415d2db759ea60ac9e242d 100644
--- a/live_30.csv
+++ b/data/anti-spoofing_Live.csv
@@ -1,9 +1,9 @@
 phone;selfie_link;video_link;worker_id;age;country;gender;selfie_file_type;video_file_type
-Iphone11 ;0001e96803--6238f1fa0478c01e4272b19c/live_selfie.jpg;0001e96803--6238f1fa0478c01e4272b19c/live_video.MOV;1692c0cc5e15f4d0b4eedd278b76fae6;22;IN;MALE;jpg;MOV
-Motorola e5 ;0001e96803--621ea6adb419e86592d408c5/live_selfie.jpg;0001e96803--621ea6adb419e86592d408c5/live_video.mp4;2770f7f62b3bc8ce2f43401d7ee8b673;43;BR;FEMALE;jpg;mp4
+Iphone11;0001e96803--6238f1fa0478c01e4272b19c/live_selfie.jpg;0001e96803--6238f1fa0478c01e4272b19c/live_video.MOV;1692c0cc5e15f4d0b4eedd278b76fae6;22;IN;MALE;jpg;MOV
+Motorola e5;0001e96803--621ea6adb419e86592d408c5/live_selfie.jpg;0001e96803--621ea6adb419e86592d408c5/live_video.mp4;2770f7f62b3bc8ce2f43401d7ee8b673;43;BR;FEMALE;jpg;mp4
 Xiaomi note 10s;0001e96803--62294efac72a824e5bb369f3/live_selfie.jpg;0001e96803--62294efac72a824e5bb369f3/live_video.mp4;4d493939d821bf3ae2eafd8b41389354;26;PH;FEMALE;jpg;mp4
 iPhone 6;0001e96803--621ea6b1e2c5d221d5131267/live_selfie.jpg;0001e96803--621ea6b1e2c5d221d5131267/live_video.MOV;d549a76a2deee40ae5a0ae285c0a0062;25;TR;MALE;jpg;MOV
-iPhone ;0001e96803--62421a6b74c10315675b9eb3/live_selfie.jpg;0001e96803--62421a6b74c10315675b9eb3/live_video.MOV;24b759a13087ac0ffabad77b7e6e29d6;27;BR;FEMALE;jpg;MOV
+iPhone;0001e96803--62421a6b74c10315675b9eb3/live_selfie.jpg;0001e96803--62421a6b74c10315675b9eb3/live_video.MOV;24b759a13087ac0ffabad77b7e6e29d6;27;BR;FEMALE;jpg;MOV
 Infinix smart HD;0001e96803--621ea6ba1185323cad2532b0/live_selfie.jpg;0001e96803--621ea6ba1185323cad2532b0/live_video.mp4;4baa6c174404f49e04dd832675dd8cb0;27;KE;FEMALE;jpg;mp4
 Android;0001e96803--624c537a6c319c28fbd30808/live_selfie.jpg;0001e96803--624c537a6c319c28fbd30808/live_video.mp4;db02b6db0f45ae5d94ccb5c3e76d2057;26;BR;FEMALE;jpg;mp4
 iPhone 11;0001e96803--621ea6c1b99da7739f2be705/live_selfie.jpg;0001e96803--621ea6c1b99da7739f2be705/live_video.MOV;f4d768d2381d90bf11a4651e9f19bd1f;27;RU;MALE;jpg;MOV
@@ -26,6 +26,6 @@ Samsung A30s;0001e96803--621f75c97610eb59dffc7030/live_selfie.jpg;0001e96803--62
 Hisense F27;0001e96803--6221e1a899d6fe4e24ba05ec/live_selfie.jpg;0001e96803--6221e1a899d6fe4e24ba05ec/live_video.3gp;f9cb91a299a9243a3bbb709a3d09ef6d;33;ZA;FEMALE;jpg;3gp
 Redmi note 10 pro;0001e96803--621f762b779c5d6618c34920/live_selfie.jpg;0001e96803--621f762b779c5d6618c34920/live_video.mp4;7a5e08bbffdfa8c796f61309fb57fc73;31;TR;MALE;jpg;mp4
 Oppo;0001e96803--622e5d6b21965b1ff1380c0e/live_selfie.jpg;0001e96803--622e5d6b21965b1ff1380c0e/live_video.mp4;98916f030157fb4c7ba078bbf6e32c49;37;TR;MALE;jpg;mp4
-Xiaomi Redmi Note 8 ;0001e96803--621f7640ae84134bae50c875/live_selfie.jpg;0001e96803--621f7640ae84134bae50c875/live_video.mp4;356f65da912e3e4cd6a60dd2624581ab;22;CO;FEMALE;jpg;mp4
+Xiaomi Redmi Note 8;0001e96803--621f7640ae84134bae50c875/live_selfie.jpg;0001e96803--621f7640ae84134bae50c875/live_video.mp4;356f65da912e3e4cd6a60dd2624581ab;22;CO;FEMALE;jpg;mp4
 Vivo Y20s;0001e96803--6266a9012065600057a7394e/live_selfie.jpg;0001e96803--6266a9012065600057a7394e/live_video.mp4;31141a7a2cbf6cba167c3a17b748df8b;19;TR;FEMALE;jpg;mp4
 Samsung Galaxy M21;0001e96803--621f7647009671488f39cca2/live_selfie.jpg;0001e96803--621f7647009671488f39cca2/live_video.mp4;5ddf989381ec382749c8543381045e44;28;BD;MALE;jpg;mp4
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