remove zipping

integration_tests/test_model_upload.py

@@ -102,6 +102,22 @@ def test_model_upload_directory(self) -> None:
             # Create Version
             model_upload(self.handle, temp_dir, LICENSE_NAME)
 
+    def test_model_upload_directory_structure(self) -> None:
+        nested_dir = Path(self.temp_dir) / "nested"
+        nested_dir.mkdir()
+
+        with open(Path(self.temp_dir) / "file1.txt", "w") as f:
+            f.write("dummy content in nested file")
+
+        # Create dummy files in the nested directory
+        nested_dummy_files = ["nested_model.h5", "nested_config.json", "nested_metadata.json"]
+        for file in nested_dummy_files:
+            with open(nested_dir / file, "w") as f:
+                f.write("dummy content in nested file")
+
+        # Call the model upload function with the base directory
+        model_upload(self.handle, self.temp_dir, LICENSE_NAME)
+
     def test_model_upload_nested_dir(self) -> None:
         # Create a nested directory within self.temp_dir
         nested_dir = Path(self.temp_dir) / "nested"

src/kagglehub/gcs_upload.py

@@ -1,13 +1,10 @@
 import logging
 import os
-import shutil
 import time
 import zipfile
 from datetime import datetime
-from multiprocessing import Pool
-from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import List, Tuple, Union
+from typing import Dict, List, Optional, Union
 
 import requests
 from requests.exceptions import ConnectionError, Timeout
@@ -25,6 +22,25 @@
 REQUEST_TIMEOUT = 600
 
 
+class UploadDirectoryInfo:
+    def __init__(
+        self,
+        name: str,
+        files: Optional[List[str]] = None,
+        directories: Optional[List["UploadDirectoryInfo"]] = None,
+    ):
+        self.name = name
+        self.files = files if files is not None else []
+        self.directories = directories if directories is not None else []
+
+    def serialize(self) -> Dict:
+        return {
+            "name": self.name,
+            "files": [{"token": file} for file in self.files],
+            "directories": [directory.serialize() for directory in self.directories],
+        }
+
+
 def parse_datetime_string(string: str) -> Union[datetime, str]:
     time_formats = ["%Y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ"]
     for t in time_formats:
@@ -138,51 +154,108 @@ def _upload_blob(file_path: str, model_type: str) -> str:
     return response["token"]
 
 
-def zip_file(args: Tuple[Path, Path, Path]) -> int:
-    file_path, zip_path, source_path_obj = args
-    arcname = file_path.relative_to(source_path_obj)
-    size = file_path.stat().st_size
-    with zipfile.ZipFile(zip_path, "a", zipfile.ZIP_STORED, allowZip64=True) as zipf:
-        zipf.write(file_path, arcname)
-    return size
-
-
-def zip_files(source_path_obj: Path, zip_path: Path) -> List[int]:
-    files = [file for file in source_path_obj.rglob("*") if file.is_file()]
-    args = [(file, zip_path, source_path_obj) for file in files]
-
-    with Pool() as pool:
-        sizes = pool.map(zip_file, args)
-    return sizes
-
-
-def upload_files(source_path: str, model_type: str) -> List[str]:
-    source_path_obj = Path(source_path)
-    with TemporaryDirectory() as temp_dir:
-        temp_dir_path = Path(temp_dir)
-        total_size = 0
-
-        if source_path_obj.is_dir():
-            for file_path in source_path_obj.rglob("*"):
-                if file_path.is_file():
-                    total_size += file_path.stat().st_size
-        elif source_path_obj.is_file():
-            total_size = source_path_obj.stat().st_size
-        else:
-            path_error_message = "The source path does not point to a valid file or directory."
-            raise ValueError(path_error_message)
-
-        with tqdm(total=total_size, desc="Zipping", unit="B", unit_scale=True, unit_divisor=1024) as pbar:
-            if source_path_obj.is_dir():
-                zip_path = temp_dir_path / "archive.zip"
-                sizes = zip_files(source_path_obj, zip_path)
-                for size in sizes:
-                    pbar.update(size)
-                upload_path = str(zip_path)
-            elif source_path_obj.is_file():
-                temp_file_path = temp_dir_path / source_path_obj.name
-                shutil.copy(source_path_obj, temp_file_path)
-                pbar.update(temp_file_path.stat().st_size)
-                upload_path = str(temp_file_path)
-
-        return [token for token in [_upload_blob(upload_path, model_type)] if token]
+def upload_files_and_directories(
+    folder: str, model_type: str, quiet: bool = False  # noqa: FBT002, FBT001
+) -> UploadDirectoryInfo:
+    # Count the total number of files
+    file_count = 0
+    for _, _, files in os.walk(folder):
+        file_count += len(files)
+
+    if file_count > MAX_FILES_TO_UPLOAD:
+        if not quiet:
+            logger.info(f"More than {MAX_FILES_TO_UPLOAD} files detected, creating a zip archive...")
+
+        with TemporaryDirectory() as temp_dir:
+            zip_path = os.path.join(temp_dir, TEMP_ARCHIVE_FILE)
+            with zipfile.ZipFile(zip_path, "w") as zipf:
+                for root, _, files in os.walk(folder):
+                    for file in files:
+                        file_path = os.path.join(root, file)
+                        zipf.write(file_path, os.path.relpath(file_path, folder))
+
+            tokens = [
+                token
+                for token in [_upload_file_or_folder(temp_dir, TEMP_ARCHIVE_FILE, model_type, quiet)]
+                if token is not None
+            ]
+            return UploadDirectoryInfo(name="archive", files=tokens)
+
+    root_dict = UploadDirectoryInfo(name="root")
+    if os.path.isfile(folder):
+        # Directly upload the file if the path is a file
+        file_name = os.path.basename(folder)
+        token = _upload_file_or_folder(os.path.dirname(folder), file_name, model_type, quiet)
+        if token:
+            root_dict.files.append(token)
+    else:
+        for root, _, files in os.walk(folder):
+            # Path of the current folder relative to the base folder
+            path = os.path.relpath(root, folder)
+
+            # Navigate or create the dictionary path to the current folder
+            current_dict = root_dict
+            if path != ".":
+                for part in path.split(os.sep):
+                    # Find or create the subdirectory in the current dictionary
+                    for subdir in current_dict.directories:
+                        if subdir.name == part:
+                            current_dict = subdir
+                            break
+                    else:
+                        # If the directory is not found, create a new one
+                        new_dir = UploadDirectoryInfo(name=part)
+                        current_dict.directories.append(new_dir)
+                        current_dict = new_dir
+
+            # Add file tokens to the current directory in the dictionary
+            for file in files:
+                token = _upload_file_or_folder(root, file, model_type, quiet)
+                if token:
+                    current_dict.files.append(token)
+
+    return root_dict
+
+
+def _upload_file_or_folder(
+    parent_path: str,
+    file_or_folder_name: str,
+    model_type: str,
+    quiet: bool = False,  # noqa: FBT002, FBT001
+) -> Optional[str]:
+    """
+    Uploads a file or each file inside a folder individually from a specified path to a remote service.
+    Parameters
+    ==========
+    parent_path: The parent directory path from where the file or folder is to be uploaded.
+    file_or_folder_name: The name of the file or folder to be uploaded.
+    dir_mode: The mode to handle directories. Accepts 'zip', 'tar', or other values for skipping.
+    model_type: Type of the model that is being uploaded.
+    quiet: suppress verbose output (default is False)
+    :return: A token if the upload is successful, or None if the file is skipped or the upload fails.
+    """
+    full_path = os.path.join(parent_path, file_or_folder_name)
+    if os.path.isfile(full_path):
+        return _upload_file(file_or_folder_name, full_path, quiet, model_type)
+    return None
+
+
+def _upload_file(file_name: str, full_path: str, quiet: bool, model_type: str) -> Optional[str]:  # noqa: FBT001
+    """Helper function to upload a single file
+    Parameters
+    ==========
+    file_name: name of the file to upload
+    full_path: path to the file to upload
+    quiet: suppress verbose output
+    model_type: Type of the model that is being uploaded.
+    :return: None - upload unsuccessful; instance of UploadFile - upload successful
+    """
+
+    if not quiet:
+        logger.info("Starting upload for file " + file_name)
+
+    content_length = os.path.getsize(full_path)
+    token = _upload_blob(full_path, model_type)
+    if not quiet:
+        logger.info("Upload successful: " + file_name + " (" + File.get_size(content_length) + ")")
+    return token

src/kagglehub/models.py

@@ -2,7 +2,7 @@
 from typing import Optional
 
 from kagglehub import registry
-from kagglehub.gcs_upload import upload_files
+from kagglehub.gcs_upload import upload_files_and_directories
 from kagglehub.handle import parse_model_handle
 from kagglehub.models_helpers import create_model_if_missing, create_model_instance_or_version
 
@@ -47,7 +47,7 @@ def model_upload(
     create_model_if_missing(h.owner, h.model)
 
     # Upload the model files to GCS
-    tokens = upload_files(local_model_dir, "model")
+    tokens = upload_files_and_directories(local_model_dir, "model")
 
     # Create a model instance if it doesn't exist, and create a new instance version if an instance exists
     create_model_instance_or_version(h, tokens, license_name, version_notes)

src/kagglehub/models_helpers.py

@@ -1,9 +1,10 @@
 import logging
 from http import HTTPStatus
-from typing import List, Optional
+from typing import Optional
 
-from kagglehub.clients import KaggleApiV1Client
-from kagglehub.exceptions import BackendError, KaggleApiHTTPError
+from kagglehub.clients import BackendError, KaggleApiV1Client
+from kagglehub.exceptions import KaggleApiHTTPError
+from kagglehub.gcs_upload import UploadDirectoryInfo
 from kagglehub.handle import ModelHandle
 
 logger = logging.getLogger(__name__)
@@ -16,11 +17,15 @@ def _create_model(owner_slug: str, model_slug: str) -> None:
     logger.info(f"Model '{model_slug}' Created.")
 
 
-def _create_model_instance(model_handle: ModelHandle, files: List[str], license_name: Optional[str] = None) -> None:
+def _create_model_instance(
+    model_handle: ModelHandle, files_and_directories: UploadDirectoryInfo, license_name: Optional[str] = None
+) -> None:
+    serialized_data = files_and_directories.serialize()
     data = {
         "instanceSlug": model_handle.variation,
         "framework": model_handle.framework,
-        "files": [{"token": file_token} for file_token in files],
+        "files": [{"token": file_token} for file_token in files_and_directories.files],
+        "directories": serialized_data["directories"],
     }
     if license_name is not None:
         data["licenseName"] = license_name
@@ -30,8 +35,15 @@ def _create_model_instance(model_handle: ModelHandle, files: List[str], license_
     logger.info(f"Your model instance has been created.\nFiles are being processed...\nSee at: {model_handle.to_url()}")
 
 
-def _create_model_instance_version(model_handle: ModelHandle, files: List[str], version_notes: str = "") -> None:
-    data = {"versionNotes": version_notes, "files": [{"token": file_token} for file_token in files]}
+def _create_model_instance_version(
+    model_handle: ModelHandle, files_and_directories: UploadDirectoryInfo, version_notes: str = ""
+) -> None:
+    serialized_data = files_and_directories.serialize()
+    data = {
+        "versionNotes": version_notes,
+        "files": [{"token": file_token} for file_token in files_and_directories.files],
+        "directories": serialized_data["directories"],
+    }
     api_client = KaggleApiV1Client()
     api_client.post(
         f"/models/{model_handle.owner}/{model_handle.model}/{model_handle.framework}/{model_handle.variation}/create/version",
@@ -43,7 +55,7 @@ def _create_model_instance_version(model_handle: ModelHandle, files: List[str],
 
 
 def create_model_instance_or_version(
-    model_handle: ModelHandle, files: List[str], license_name: Optional[str], version_notes: str = ""
+    model_handle: ModelHandle, files: UploadDirectoryInfo, license_name: Optional[str], version_notes: str = ""
 ) -> None:
     try:
         _create_model_instance(model_handle, files, license_name)

tests/test_model_upload.py

@@ -140,7 +140,7 @@ def test_model_upload_instance_with_valid_handle(self) -> None:
                     test_filepath.touch()  # Create a temporary file in the temporary directory
                     model_upload("metaresearch/new-model/pyTorch/new-variation", temp_dir, APACHE_LICENSE, "model_type")
                     self.assertEqual(len(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES), 1)
-                    self.assertIn(TEMP_ARCHIVE_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
+                    self.assertIn(TEMP_TEST_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
 
     def test_model_upload_instance_with_nested_directories(self) -> None:
         # execution path: get_model -> create_model -> get_instance -> create_version
@@ -156,7 +156,7 @@ def test_model_upload_instance_with_nested_directories(self) -> None:
                     test_filepath.touch()
                     model_upload("metaresearch/new-model/pyTorch/new-variation", temp_dir, APACHE_LICENSE, "model_type")
                     self.assertEqual(len(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES), 1)
-                    self.assertIn(TEMP_ARCHIVE_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
+                    self.assertIn(TEMP_TEST_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
 
     def test_model_upload_version_with_valid_handle(self) -> None:
         # execution path: get_model -> get_instance -> create_instance
@@ -168,7 +168,7 @@ def test_model_upload_version_with_valid_handle(self) -> None:
                     test_filepath.touch()  # Create a temporary file in the temporary directory
                     model_upload("metaresearch/llama-2/pyTorch/7b", temp_dir, APACHE_LICENSE, "model_type")
                     self.assertEqual(len(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES), 1)
-                    self.assertIn(TEMP_ARCHIVE_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
+                    self.assertIn(TEMP_TEST_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
 
     def test_model_upload_with_too_many_files(self) -> None:
         with create_test_http_server(KaggleAPIHandler):
@@ -199,7 +199,7 @@ def test_model_upload_resumable(self) -> None:
                     # Check that GcsAPIHandler received two PUT requests
                     self.assertEqual(GcsAPIHandler.put_requests_count, 2)
                     self.assertEqual(len(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES), 1)
-                    self.assertIn(TEMP_ARCHIVE_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
+                    self.assertIn(TEMP_TEST_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
 
     def test_model_upload_with_none_license(self) -> None:
         with create_test_http_server(KaggleAPIHandler):
@@ -209,7 +209,7 @@ def test_model_upload_with_none_license(self) -> None:
                     test_filepath.touch()  # Create a temporary file in the temporary directory
                     model_upload("metaresearch/new-model/pyTorch/new-variation", temp_dir, None, "model_type")
                     self.assertEqual(len(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES), 1)
-                    self.assertIn(TEMP_ARCHIVE_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
+                    self.assertIn(TEMP_TEST_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
 
     def test_model_upload_without_license(self) -> None:
         with create_test_http_server(KaggleAPIHandler):
@@ -219,7 +219,7 @@ def test_model_upload_without_license(self) -> None:
                     test_filepath.touch()  # Create a temporary file in the temporary directory
                     model_upload("metaresearch/new-model/pyTorch/new-variation", temp_dir, version_notes="model_type")
                     self.assertEqual(len(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES), 1)
-                    self.assertIn(TEMP_ARCHIVE_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
+                    self.assertIn(TEMP_TEST_FILE, KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
 
     def test_model_upload_with_invalid_license_fails(self) -> None:
         with create_test_http_server(KaggleAPIHandler):
@@ -244,3 +244,29 @@ def test_single_file_upload(self) -> None:
 
                     self.assertEqual(len(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES), 1)
                     self.assertIn("single_dummy_file.txt", KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES)
+
+    def test_model_upload_with_directory_structure(self) -> None:
+        with create_test_http_server(KaggleAPIHandler):
+            with create_test_http_server(GcsAPIHandler, "http://localhost:7778"):
+                with TemporaryDirectory() as temp_dir:
+                    base_path = Path(temp_dir)
+                    (base_path / "dir1").mkdir()
+                    (base_path / "dir2").mkdir()
+
+                    (base_path / "file1.txt").touch()
+
+                    (base_path / "dir1" / "file2.txt").touch()
+                    (base_path / "dir1" / "file3.txt").touch()
+
+                    (base_path / "dir1" / "subdir1").mkdir()
+                    (base_path / "dir1" / "subdir1" / "file4.txt").touch()
+
+                    model_upload("metaresearch/new-model/pyTorch/new-variation", temp_dir, APACHE_LICENSE, "model_type")
+
+                    self.assertEqual(len(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES), 4)
+                    expected_files = {"file1.txt", "file2.txt", "file3.txt", "file4.txt"}
+                    self.assertTrue(set(KaggleAPIHandler.UPLOAD_BLOB_FILE_NAMES).issubset(expected_files))
+
+                    # TODO: Add assertions on CreateModelInstanceRequest.Directories and
+                    # CreateModelInstanceRequest.Files to verify the expected structure
+                    # is sent.