diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index cac0187..f470e46 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -32,7 +32,7 @@ repos:
     rev: v1.11.2
     hooks:
       - id: mypy
-        args: ["--config=pyproject.toml"]
+        args: ["--config=pyproject.toml", "--python-version", "3.12"]
         additional_dependencies:
           - types-requests
           - types-aiofiles
diff --git a/labellerr/core/datasets/video_dataset.py b/labellerr/core/datasets/video_dataset.py
index 6f38301..44a9dcd 100644
--- a/labellerr/core/datasets/video_dataset.py
+++ b/labellerr/core/datasets/video_dataset.py
@@ -41,9 +41,13 @@ def fetch_files(self, page_size: int = 1000):
 
                 # print(params)
 
-                response = self.client.make_request(url, params, unique_id)
+                # Fixed: Pass method as first arg, url as second, params as kwarg
 
-                # pprint.pprint(response)
+                response = self.client.make_request(
+                    method="GET", url=url, request_id=unique_id, params=params
+                )
+                # from pprint import pprint
+                # pprint(response)
 
                 # Extract files from the response
                 files = response.get("response", {}).get("files", [])
@@ -63,12 +67,15 @@ def fetch_files(self, page_size: int = 1000):
                 if not next_search_after or not files:
                     break
 
-                print(f"Fetched total: {len(all_file_ids)}")
-
             print(f"Total file IDs extracted: {len(all_file_ids)}")
-            # return all_file_ids
+            return all_file_ids
+
+        except Exception as e:
+            raise LabellerrError(f"Failed to fetch dataset files: {str(e)}")
 
-            # Create LabellerrVideoFile instances for each file_id
+    def _create_labellerrfile_instances(self, all_file_ids: list, project_id: str):
+        # Create LabellerrVideoFile instances for each file_id
+        try:
             video_files = []
             print(
                 f"\nCreating LabellerrFile instances for {len(all_file_ids)} files..."
@@ -79,7 +86,7 @@ def fetch_files(self, page_size: int = 1000):
                     video_file = LabellerrFile(
                         client=self.client,
                         file_id=file_id,
-                        project_id="self.project_id",  # noqa: # todo: ximi we don't have project id here
+                        project_id=project_id,  # noqa: # todo: ximi we don't have project id here
                         dataset_id=self.dataset_id,
                     )
                     video_files.append(video_file)
@@ -94,7 +101,7 @@ def fetch_files(self, page_size: int = 1000):
         except Exception as e:
             raise LabellerrError(f"Failed to fetch dataset files: {str(e)}")
 
-    def download(self):
+    def download(self, project_id: str):
         """
         Process all video files in the dataset: download frames, create videos,
         and automatically clean up temporary files.
@@ -107,8 +114,13 @@ def download(self):
             print(f"# Starting batch video processing for dataset: {self.dataset_id}")
             print(f"{'#'*70}\n")
 
+            # Fetch all files ids
+            all_files_ids = self.fetch_files()
+
             # Fetch all video files
-            video_files = self.fetch_files()
+            video_files = self._create_labellerrfile_instances(
+                all_files_ids, project_id
+            )
 
             if not video_files:
                 print("No video files found in dataset")
diff --git a/labellerr/core/files/video_file.py b/labellerr/core/files/video_file.py
index 7e544d5..1a59234 100644
--- a/labellerr/core/files/video_file.py
+++ b/labellerr/core/files/video_file.py
@@ -61,9 +61,12 @@ def get_frames(self, frame_start: int = 0, frame_end: int | None = None):
                 "frame_end": frame_end,
                 "project_id": self.project_id,
                 "uuid": unique_id,
+                "client_id": self.client.client_id,
             }
 
-            response = self.client.make_request(url, params, unique_id)
+            response = self.client.make_request(
+                "GET", url, request_id=unique_id, params=params
+            )
 
             return response
 
@@ -233,7 +236,7 @@ def create_video(
             raise LabellerrError(f"Error while joining frames: {str(e)}")
 
     def download_create_video_auto_cleanup(
-        self, output_folder: str = "./Labellerr_datastets"
+        self, output_folder: str = "./Labellerr_datasets"
     ):
         """
         Download frames, create video, and automatically clean up temporary frames.
diff --git a/labellerr/notebooks/SDK.ipynb b/labellerr/notebooks/SDK.ipynb
index 1efd28f..20cba96 100644
--- a/labellerr/notebooks/SDK.ipynb
+++ b/labellerr/notebooks/SDK.ipynb
@@ -22,6 +22,7 @@
    "source": [
     "from labellerr.client import LabellerrClient\n",
     "from labellerr.core.datasets import LabellerrDataset\n",
+    "from labellerr.core.exceptions import LabellerrError\n",
     "import os\n",
     "from tqdm.notebook import tqdm\n"
    ]
@@ -61,7 +62,83 @@
     "\n",
     "api_key = config[\"API_KEY\"]\n",
     "api_secret = config[\"API_SECRET\"]\n",
-    "client_id = config[\"CLIENT_ID\"]"
+    "client_id = config[\"CLIENT_ID\"]\n",
+    "email = config[\"EMAIL\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2646549",
+   "metadata": {},
+   "source": [
+    "## Kaggle Dataset Download and Project creation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2d2a744",
+   "metadata": {},
+   "source": [
+    "Before downloading the dataset from Kaggle, you need to:\n",
+    "\n",
+    "1. Install kagglehub package using pip\n",
+    "2. Authenticate with Kaggle\n",
+    "3. Download the CCTV footage dataset\n",
+    "\n",
+    "The kagglehub package provides a simple interface to download datasets directly from Kaggle. Make sure you have a Kaggle account and API credentials set up before proceeding.\n",
+    "\n",
+    "Note: If you haven't set up Kaggle authentication before, you'll need to:\n",
+    "1. Create a Kaggle account at https://www.kaggle.com\n",
+    "2. Go to \"Account\" settings\n",
+    "3. Scroll to API section and click \"Create New API Token\"\n",
+    "4. This will download a kaggle.json file with your credentials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "be12bf3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install kagglehub ipywidgets"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "e05889d7",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "import kagglehub\n",
+    "\n",
+    "kagglehub.login()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "c5b93e97",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "# Download 1000 videos(~1 min) dataset\n",
+    "\n",
+    "# large video dataset(1000 videos)\n",
+    "# path_to_dataset = kagglehub.dataset_download(\"yashsuman/cctv-footage\")\n",
+    "\n",
+    "# medium video dataset(1000 videos)\n",
+    "# path_to_dataset = kagglehub.dataset_download(\"yashsuman/cctv-footage\")\n",
+    "\n",
+    "# small video dataset(5 videos)\n",
+    "path_to_dataset = kagglehub.dataset_download(\"mistag/short-videos\")\n",
+    "\n",
+    "print(\"Path to dataset files:\", path_to_dataset)"
    ]
   },
   {
@@ -71,28 +148,212 @@
    "source": [
     "## 2. Project Configuration\n",
     "\n",
-    "### Dataset and Project IDs\n",
-    "To work with specific datasets and projects in Labellerr, you need their respective IDs. These IDs are unique identifiers that link your code to the correct resources on the platform.\n",
+    "### Create Project with kaggle dataset\n",
+    "Create a project with sample annotation template with kaggle dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1e2e2f3",
+   "metadata": {},
+   "source": [
+    "### project_payload structure (keys and purpose)\n",
+    "- client_id: str — client identifier.\n",
+    "- dataset_name: str — human-readable dataset name.\n",
+    "- dataset_description: str — short description of the dataset.\n",
+    "- data_type: str — \"video\" (or \"image\") indicating dataset type.\n",
+    "- created_by: str — email of the creator/owner.\n",
+    "- project_name: str — name for the new Labellerr project.\n",
+    "- annotation_guide: list — annotation questions; each question includes:\n",
+    "    - question_number (int), question (str), question_id (str), option_type (str),\n",
+    "    - required (bool), options (list of option objects with option_name, etc.)\n",
+    "- rotation_config: dict — rotation counts for annotation, review, and client review:\n",
+    "    - annotation_rotation_count, review_rotation_count, client_review_rotation_count\n",
+    "- autolabel: bool — whether to enable autolabeling.\n",
+    "- folder_to_upload: str — local folder path containing files to upload to the project.\n",
+    "\n",
+    "### Typical usage steps\n",
+    "1. Ensure .env has API_KEY, API_SECRET, CLIENT_ID, EMAIL and `config` is loaded.\n",
+    "2. Ensure `path_to_dataset` points to the correct local dataset folder.\n",
+    "3. Instantiate client if not already done:\n",
+    "   client = LabellerrClient(api_key, api_secret, client_id)\n",
+    "4. Review or adjust `project_payload` (annotation guide, rotation, folder_to_upload).\n",
+    "5. Create the project:\n",
+    "   try:\n",
+    "       result = client.initiate_create_project(project_payload)\n",
+    "       project_id = result['project_id']['response']['project_id']\n",
+    "   except LabellerrError as e:\n",
+    "       handle or log the exception\n",
+    "\n",
+    "## Notes & best practices\n",
+    "- Do not commit API credentials to source control.\n",
+    "- Verify `folder_to_upload` contains the expected video files before initiating the create-project call.\n",
+    "- Customize `annotation_guide` to match your annotation schema and color/options.\n",
+    "- Use rotation_config to control annotation/review distribution and workload."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "55b42671",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = LabellerrClient(api_key, api_secret, client_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7be23d9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_dataset = r\"D:\\Professional\\tensormatics_SDKPython\\.cache\\kagglehub\\datasets\\mistag\\short-videos\\versions\\4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "94adb94a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset created with ID: 5b0c7653-3adf-4ee8-ad3c-dfd9654b182c\n",
+      "Total files: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from labellerr.core.datasets import create_dataset\n",
+    "from labellerr.core.schemas import DatasetConfig\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "if not Path(path_to_dataset).exists():\n",
+    "    raise FileNotFoundError(f\"Path does not exist: {path_to_dataset}\")\n",
     "\n",
-    "How to obtain the IDs:\n",
-    "1. Go to the Labellerr platform\n",
-    "2. Create or select an existing dataset\n",
-    "3. Create or select an existing project\n",
-    "4. Copy the dataset_id and project_id from their respective pages\n",
+    "dataset = create_dataset(\n",
+    "    client=client,\n",
+    "    dataset_config=DatasetConfig(\n",
+    "        dataset_name=\"SDK-Test\",\n",
+    "        dataset_description=\"a sample dataset of video\",\n",
+    "        data_type=\"video\",\n",
+    "        connector_type=\"local\"\n",
+    "    ),\n",
+    "    folder_to_upload=path_to_dataset,\n",
+    "    path=\"local\"\n",
+    "    \n",
+    ")\n",
     "\n",
-    "Note: The dataset_id is a UUID format string, while the project_id is typically a human-readable string."
+    "print(f\"Dataset created with ID: {dataset.dataset_id}\")\n",
+    "print(f\"Total files: {dataset.files_count}\") # only after video processing, right no is shown else 0 is shown always"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "07dcfae9",
+   "execution_count": null,
+   "id": "5b7e6f39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"Dataset ID: {dataset.dataset_id}\")\n",
+    "print(f\"Data Type: {dataset.data_type}\")\n",
+    "print(f\"Files Count: {dataset.files_count}\")\n",
+    "print(f\"Status Code: {dataset.status_code}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9f6eff1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from labellerr.core.projects import create_annotation_guideline\n",
+    "import uuid\n",
+    "\n",
+    "questions = [\n",
+    "  { \"question_number\": 1,\n",
+    "    \"question\": \"Test_12345\",\n",
+    "    \"option_type\": \"polygon\",\n",
+    "    \"required\": True,\n",
+    "    \"question_id\": str(uuid.uuid4()),\n",
+    "    \"options\": [\n",
+    "      { \"option_name\": \"#fe1236\" }\n",
+    "    ]\n",
+    "  }\n",
+    "]\n",
+    "\n",
+    "template_id = create_annotation_guideline(\n",
+    "    client=client, \n",
+    "    questions=questions, \n",
+    "    template_name='SDK_template', \n",
+    "    data_type='video'\n",
+    ")\n",
+    "print(f\"Annotation template created with ID: {template_id}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6027577d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_payload = {\n",
+    "    'data_type': \"video\",\n",
+    "    'created_by': email,\n",
+    "    'project_name': \"SDK workflow\",\n",
+    "    # \"datasets\": [dataset.dataset_id],\n",
+    "    \"datasets\": [\"d49f5cfb-cc72-420d-a3d1-68eaadf69f5a\"],\n",
+    "    'annotation_template_id': template_id,\n",
+    "    'rotation_config': {\n",
+    "        'annotation_rotation_count': 1,\n",
+    "        'review_rotation_count': 1,\n",
+    "        'client_review_rotation_count': 1\n",
+    "    },\n",
+    "    'autolabel': False,\n",
+    "    # 'folder_to_upload': 'path/to/your/dataset'\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bad0ec0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from labellerr.core.projects import create_project\n",
+    "try:\n",
+    "    result = create_project(client, project_payload)\n",
+    "    print(f\"Project created successfully. Project ID: {result.project_id}\")\n",
+    "except LabellerrError as e:\n",
+    "    print(f\"Project creation failed: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69c5e5ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.attached_datasets\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1ca38e6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# go to our platform to create dataset and project then get their ids\n",
-    "dataset_id = \"16257fd6-b91b-4d00-a680-9ece9f3f241c\"\n",
-    "project_id = \"gabrila_artificial_duck_74237\""
+    "result.project_data"
    ]
   },
   {
@@ -113,78 +374,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "9eaec7e1",
+   "execution_count": null,
+   "id": "9d91db16",
    "metadata": {},
    "outputs": [],
    "source": [
-    "client = LabellerrClient(api_key, api_secret, client_id) \n",
-    "dataset = LabellerrDataset(client, dataset_id, project_id)"
+    "# dataset.dataset_id\n",
+    "\n",
+    "# dataset_id = 'd49f5cfb-cc72-420d-a3d1-68eaadf69f5a'\n",
+    "# project_id = 'aurora_pretty_camel_16090'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "7b6a7052",
+   "id": "15c901ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from labellerr.core.datasets import LabellerrDataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9eaec7e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from labellerr.core.datasets import LabellerrDataset\n",
+    "files = LabellerrDataset(client, dataset_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cf0aa76f",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "######################################################################\n",
-      "# Starting batch video processing for dataset: 16257fd6-b91b-4d00-a680-9ece9f3f241c\n",
-      "######################################################################\n",
-      "\n",
-      "Total file IDs extracted: 1\n",
-      "\n",
-      "Creating LabellerrFile instances for 1 files...\n",
-      "Successfully created 1 LabellerrFile instances\n",
-      "\n",
-      "Processing 1 video files...\n",
-      "\n",
-      "\n",
-      "Starting download of 1 files...\n",
-      "\n",
-      "============================================================\n",
-      "Processing file: c44f38f6-0186-436f-8c2d-ffb50a539c76\n",
-      "============================================================\n",
-      "\n",
-      "[1/4] Fetching frame data from API (0 to 1440)...\n",
-      "Retrieved 1440 frames\n",
-      "\n",
-      "[2/4] Setting up output folders...\n",
-      "\n",
-      "[3/4] Downloading frames...\n",
-      "Starting download of 1440 frames...\n",
-      "Frames downloaded: 1440/1440 (1440 successful, 0 failed)\n",
-      "\n",
-      "[4/4] Creating video from frames...\n",
-      "Running command: ffmpeg -y -start_number 0 -framerate 30 -i ./Labellerr_datastets\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\%d.jpg -c:v libx264 -pix_fmt yuv420p ./Labellerr_datastets\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\c44f38f6-0186-436f-8c2d-ffb50a539c76.mp4\n",
-      "Video saved as ./Labellerr_datastets\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\c44f38f6-0186-436f-8c2d-ffb50a539c76.mp4\n",
-      "\n",
-      "Cleaning up temporary frames...\n",
-      "Removed temporary frames folder: ./Labellerr_datastets\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\c44f38f6-0186-436f-8c2d-ffb50a539c76\n",
-      "\n",
-      "============================================================\n",
-      "✓ Processing complete!\n",
-      "Video saved to: ./Labellerr_datastets\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\c44f38f6-0186-436f-8c2d-ffb50a539c76.mp4\n",
-      "============================================================\n",
-      "\n",
-      "Files processed: 1/1 (1 successful, 0 failed)\n",
-      "######################################################################\n",
-      "# Batch Processing Complete\n",
-      "# Total files: 1\n",
-      "# Successful: 1\n",
-      "# Failed: 0\n",
-      "######################################################################\n",
-      "\n"
+      "Total file IDs extracted: 3\n"
      ]
     }
    ],
    "source": [
-    "results = dataset.download()"
+    "labellerrfile_list = files.fetch_files()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "995fc600",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import logging\n",
+    "# logging.basicConfig(level=logging.DEBUG)\n",
+    "\n",
+    "labellerrfile = files.download(project_id)"
    ]
   },
   {
@@ -231,14 +481,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "f5c41073",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from labellerr.services.video_sampling.pyscene_detect import PySceneDetect\n",
-    "from labellerr.services.video_sampling.ssim import SSIMSceneDetect\n",
-    "from labellerr.services.video_sampling.ffmpeg import FFMPEGSceneDetect"
+    "from labellerr.services.video_sampling import PySceneDetect\n",
+    "from labellerr.services.video_sampling import SSIMSceneDetect\n",
+    "from labellerr.services.video_sampling import FFMPEGSceneDetect"
    ]
   },
   {
@@ -260,17 +510,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "49a6f89d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path exists ✅\n"
+     ]
+    }
+   ],
    "source": [
-    "dataset_dir = f\".\\Labellerr_datasets\\{dataset_id}\""
+    "from pathlib import Path\n",
+    "\n",
+    "dataset_dir = Path(f\".\\\\Labellerr_datasets\\\\{dataset_id}\")\n",
+    "\n",
+    "if dataset_dir.exists():\n",
+    "    print(\"Path exists ✅\")\n",
+    "else:\n",
+    "    print(\"Path does not exist ❌\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "dd96be8c",
    "metadata": {},
    "outputs": [],
@@ -289,19 +554,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "a3052f25",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Keyframes extracted to FFMPEG_detects\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\frames\n",
-      "JSON mapping saved to: FFMPEG_detects\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\c44f38f6-0186-436f-8c2d-ffb50a539c76_mapping.json\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for filename in os.listdir(dataset_dir):\n",
     "    file_path = os.path.join(dataset_dir, filename)\n",
@@ -350,18 +606,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "1b364362",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 52 image files\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -384,79 +632,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "f39153ab",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\0.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1008.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1016.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1028.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1060.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1082.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1106.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1119.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1137.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1157.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1175.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1189.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\119.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1201.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1218.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1233.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1246.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1257.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1278.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1312.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\1319.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\141.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\233.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\263.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\37.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\381.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\408.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\437.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\457.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\484.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\508.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\552.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\575.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\590.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\619.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\63.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\647.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\683.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\706.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\721.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\758.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\776.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\805.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\823.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\83.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\836.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\858.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\876.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\893.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\915.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\949.jpg',\n",
-       " 'FFMPEG_detects\\\\16257fd6-b91b-4d00-a680-9ece9f3f241c\\\\c44f38f6-0186-436f-8c2d-ffb50a539c76\\\\frames\\\\99.jpg']"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "images_files"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "40c70986",
    "metadata": {},
    "outputs": [],
@@ -489,36 +675,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "a1d96b25",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset created successfully!\n",
-      "Dataset ID: 6a680901-fe81-49f0-9120-bb754d63a341\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'6a680901-fe81-49f0-9120-bb754d63a341'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "upload_images_from_files(images_files, client, client_id)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "958fc75e",
    "metadata": {},
    "outputs": [],
@@ -601,19 +768,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "9f682f4f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Project created successfully!\n",
-      "Project ID: sherri_puny_rattlesnake_84247\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if response['response']['project_id']:\n",
     "    print(f\"Project created successfully!\")\n",
@@ -697,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "df6b3ac7",
    "metadata": {},
    "outputs": [],
@@ -708,7 +866,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "SDk",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -722,7 +880,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.18"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,