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# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
Given an AIProjectClient, this sample demonstrates how to use the synchronous
`.datasets` methods to upload all files in a folder (including sub-folders),
create Dataset that reference those files, and then download the files using
an Azure storage ContainerClient.
USAGE:
python sample_datasets_download.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" azure-identity python-dotenv
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
Microsoft Foundry project.
2) CONNECTION_NAME - Required. The name of the Azure Storage Account connection to use for uploading files.
3) DATASET_NAME - Optional. The name of the Dataset to create and use in this sample.
4) DATASET_VERSION - Optional. The version of the Dataset to create and use in this sample.
6) DATA_FOLDER - Optional. The folder path where the data files for upload are located.
7) DOWNLOAD_FOLDER - Optional. The folder path where the downloaded files will be saved.
"""
import os
import re
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.storage.blob import ContainerClient
load_dotenv()
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
connection_name = os.environ["CONNECTION_NAME"]
dataset_name = os.environ.get("DATASET_NAME", "dataset-test")
dataset_version = os.environ.get("DATASET_VERSION", "1.0")
download_folder = os.environ.get("DOWNLOAD_FOLDER", "downloaded_blobs")
# Construct the paths to the data folder and data file used in this sample
script_dir = os.path.dirname(os.path.abspath(__file__))
data_folder = os.environ.get("DATA_FOLDER", os.path.join(script_dir, "data_folder"))
with (
DefaultAzureCredential() as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
):
print(
f"Upload files in a folder (including sub-folders) and create a dataset named `{dataset_name}` version `{dataset_version}`, to reference the files."
)
dataset = project_client.datasets.upload_folder(
name=dataset_name,
version=dataset_version,
folder=data_folder,
connection_name=connection_name,
file_pattern=re.compile(r"\.(txt|csv|md)$", re.IGNORECASE),
)
print(dataset)
print(f"Get credentials of an existing Dataset version `{dataset_version}`:")
dataset_credential = project_client.datasets.get_credentials(name=dataset_name, version=dataset_version)
print(dataset_credential)
print(f"Creating a folder `{download_folder}` for the downloaded blobs:")
os.makedirs(download_folder, exist_ok=True)
container_client = ContainerClient.from_container_url(
container_url=dataset_credential.blob_reference.credential.sas_uri
)
print("Looping over all blobs in the container:")
for blob_name in container_client.list_blob_names():
blob_client = container_client.get_blob_client(blob_name)
file_path = os.path.join(download_folder, blob_name)
os.makedirs(os.path.dirname(file_path), exist_ok=True) # Handle sub-folders if needed
with open(file_path, "wb") as f:
f.write(blob_client.download_blob().readall())
print(f"Downloaded: {blob_name}")
print("Delete the dataset created above:")
project_client.datasets.delete(name=dataset_name, version=dataset_version)
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