diff --git a/.gitattributes b/.gitattributes
index 1d50726..a6344aa 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,4 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-nnunet_results/Dataset903_Task903_DLMUSEV2/nnUNetTrainer__nnUNetPlans__3d_fullres/crossval_results_folds_0/summary.json filter=lfs diff=lfs merge=lfs -text
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
new file mode 100644
index 0000000..b61bf9e
--- /dev/null
+++ b/.github/workflows/python-publish.yml
@@ -0,0 +1,40 @@
+name: Upload Python Package
+
+on: workflow_dispatch
+
+permissions:
+  contents: read
+  id-token: write # Allow GitHub to mint tokens
+
+jobs:
+  pypi-publish:
+    name: Upload release to PyPI
+    runs-on: ubuntu-latest # Can use macos-latest if needed
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.x"
+
+      - name: Install dependencies
+        run: |
+          pip install setuptools
+          pip install twine
+          pip install wheel
+          sudo apt-get install jq
+
+      - name: Clean previous builds
+        run: rm -rf dist
+
+      - name: Build package
+        run: python setup.py sdist bdist_wheel
+
+      - name: Publish package to PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+        run: twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
diff --git a/README.md b/README.md
index bd1ffd6..e64e23a 100644
--- a/README.md
+++ b/README.md
@@ -20,40 +20,13 @@ cd DLMUSE
 pip install -e .
 ```
 
-### Using docker(OUTDATED)
-
-```bash
-docker pull aidinisg/dlmuse:0.0.1
-```
-
 ## Usage
 
-A pre-trained nnUNet model can be found in the [DLMUSEV2-1.0.0 release](https://github.com/CBICA/DLMUSE/releases/tag/v1.0.0). Feel free to use it under the package's [license](LICENSE).
+A pre-trained nnUNet model can be found at our [hugging face account](https://huggingface.co/nichart/DLMUSE/tree/main) or at the [DLMUSEV2-1.0.0 release](https://github.com/CBICA/DLMUSE/releases/tag/v1.0.0). Feel free to use it under the package's [license](LICENSE).
 
 ### From command line
 ```bash
-DLMUSE -i "image_folder" -o "path to output folder" -m "path to model weights" -f 0 -tr nnUNetTrainer -c 3d_fullres -p nnUNetPlans -d "id" -device cuda/cpu/mps
-```
-
-### Using the docker container(OUTDATED)
-
-In the [docker container](https://hub.docker.com/repository/docker/aidinisg/dlmuse/general), the default model is included, but you can also provide your own.
-
-Without providing a model:
-
-```bash
-docker run --gpus all -it --rm -v /path/to/local/input:/workspace/input \
-                               -v /path/to/local/output:/workspace/output \
-                               aidinisg/dlmuse:0.0.1  -i input/ -o output/
-```
-
-Providing a model:
-
-```bash
-docker run --gpus all -it --rm -v /path/to/local/model:/workspace/model \
-                               -v /path/to/local/input:/workspace/input \
-                               -v /path/to/local/output:/workspace/output \
-                               aidinisg/dlmuse:0.0.1  -i input/ -o output/  --model model/
+DLMUSE -i "image_folder" -o "path to output folder" -device cuda/mps/cpu
 ```
 
 ## Contact
diff --git a/nnunet_output/dataset.json b/nnunet_output/dataset.json
new file mode 100644
index 0000000..c39f8d0
--- /dev/null
+++ b/nnunet_output/dataset.json
@@ -0,0 +1,11 @@
+{
+    "channel_names": {
+        "0": "MRI"
+    },
+    "file_ending": ".nii.gz",
+    "labels": {
+        "background": 0,
+        "class1": 1
+    },
+    "numTraining": 1806
+}
\ No newline at end of file
diff --git a/nnunet_output/plans.json b/nnunet_output/plans.json
new file mode 100644
index 0000000..0849bf1
--- /dev/null
+++ b/nnunet_output/plans.json
@@ -0,0 +1,434 @@
+{
+    "dataset_name": "Dataset901_Task901_dlicv",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        1.0,
+        1.0,
+        1.0
+    ],
+    "original_median_shape_after_transp": [
+        255,
+        255,
+        176
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 66,
+            "patch_size": [
+                256,
+                192
+            ],
+            "median_image_size_in_voxels": [
+                240.0,
+                179.0
+            ],
+            "spacing": [
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
+                5
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        },
+        "3d_lowres": {
+            "data_identifier": "nnUNetPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                160,
+                128,
+                112
+            ],
+            "median_image_size_in_voxels": [
+                235,
+                226,
+                169
+            ],
+            "spacing": [
+                1.0609,
+                1.0609,
+                1.0609
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
+                5,
+                4
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    1
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 320,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": false,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                160,
+                128,
+                112
+            ],
+            "median_image_size_in_voxels": [
+                249.0,
+                240.0,
+                179.0
+            ],
+            "spacing": [
+                1.0,
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
+                5,
+                4
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    1
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 320,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        },
+        "3d_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 8952.697265625,
+            "mean": 528.2257080078125,
+            "median": 317.0,
+            "min": -24.0,
+            "percentile_00_5": 20.0,
+            "percentile_99_5": 3972.0,
+            "std": 636.9642333984375
+        }
+    }
+}
\ No newline at end of file
diff --git a/nnunet_output/predict_from_raw_data_args.json b/nnunet_output/predict_from_raw_data_args.json
new file mode 100644
index 0000000..7f7ed21
--- /dev/null
+++ b/nnunet_output/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
+    "folder_with_segs_from_prev_stage": null,
+    "list_of_lists_or_source_folder": "nnunet_output/renamed_image",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "nnunet_output/",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnunet_output/renamed_image/renaming.json b/nnunet_output/renamed_image/renaming.json
new file mode 100644
index 0000000..bbb9214
--- /dev/null
+++ b/nnunet_output/renamed_image/renaming.json
@@ -0,0 +1,6 @@
+{
+    "test4_t1.nii.gz": "case_ 000_0000.nii.gz",
+    "test2_t1.nii.gz": "case_ 001_0000.nii.gz",
+    "test3_t1.nii.gz": "case_ 002_0000.nii.gz",
+    "test1_t1.nii.gz": "case_ 003_0000.nii.gz"
+}
\ No newline at end of file
diff --git a/src/__main__.py b/src/__main__.py
index ccf083f..f46174e 100644
--- a/src/__main__.py
+++ b/src/__main__.py
@@ -1,8 +1,8 @@
 import argparse
 import json
 import os
-import sys
 import shutil
+import sys
 import warnings
 
 import torch
@@ -188,25 +188,17 @@ def main() -> None:
     )
 
     args = parser.parse_args()
-    #args.f = [i if i == "all" else int(i) for i in args.f]
     args.f = [args.f]
 
     # data conversion
     src_folder = args.i  # input folder
-    #des_folder = args.o
+
+    # des_folder = args.o
     if not os.path.exists(args.o):  # create output folder if it does not exist
         os.makedirs(args.o)
 
     des_folder = os.path.join(args.o, "renamed_image")
 
-    # prepare_data_folder(des_folder)
-    # rename_dic, rename_back_dict = rename_and_copy_files(src_folder, des_folder)
-
-    # datalist_file = os.path.join(des_folder, "renaming.json")
-    # with open(datalist_file, "w", encoding="utf-8") as f:
-    #     json.dump(rename_dic, f, ensure_ascii=False, indent=4)
-    # print(f"Renaming dic is saved to {datalist_file}")
-
     # check if -i argument is a folder, list (csv), or a single file (nii.gz)
     if os.path.isdir(args.i):  # if args.i is a directory
         src_folder = args.i
@@ -266,7 +258,6 @@ def main() -> None:
         device = torch.device("mps")
         print("Running in MPS mode.")
 
-
     # exports for nnunetv2 purposes
     os.environ["nnUNet_raw"] = "/nnunet_raw/"
     os.environ["nnUNet_preprocessed"] = "/nnunet_preprocessed"