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.. _sphx_glr__examples_00_basic_simai_ex_00-create_project_upload_data.py:

.. _ref_basic_create_project_upload_data:

Creating a SimAI Project and Uploading Training Data
=======================================================

This example demonstrates how to connect to SimAI, create a new project, and upload training data folders.

Before you begin
-------------------

Make sure you have:

- Valid SimAI credentials and organization access.
- A dataset folder containing subdirectories with your training data.
- The ``ansys-simai-core`` library installed.

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Import necessary libraries
----------------------------------

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.. code-block:: Python


    import os

    import ansys.simai.core as asc
    from ansys.simai.core.data.training_data import TrainingData
    from ansys.simai.core.errors import NotFoundError


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Configure your settings
----------------------------------
Update these variables with your specific settings:

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.. code-block:: Python


    ORGANIZATION_NAME = "<your_organization>"  # Replace with your organization name
    PROJECT_NAME = "<your_project_name>"  # Your project name
    DATASET_PATH = "<PATH_TO_YOUR_DATASET>"  # Directory containing subdirectories with training data


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Initialize the SimAI client
----------------------------------
Create a client to connect to the SimAI platform:

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.. code-block:: Python


    simai_client = asc.SimAIClient(organization=ORGANIZATION_NAME)


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Set up the project
----------------------------------
Try to get an existing project by name, or create it if it does not exist:

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.. code-block:: Python


    try:
        project = simai_client.projects.get(name=PROJECT_NAME)
        print(f"Using existing project: {PROJECT_NAME}")
    except NotFoundError:
        project = simai_client.projects.create(PROJECT_NAME)
        print(f"Created new project: {PROJECT_NAME}")


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Set as the current working project
----------------------------------
Setting the current project allows subsequent operations to use this project by default:

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.. code-block:: Python


    simai_client.set_current_project(PROJECT_NAME)
    print(f"Current project: {simai_client.current_project}")


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Upload training data
----------------------------------
Upload all directories from the dataset path as training data.
Each subdirectory should contain the files for one training data sample.

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.. code-block:: Python



    available_tds = simai_client.training_data.list()

    print("\nUploading training data files:")
    successful_uploads = 0
    failed_uploads = 0

    for dir in os.listdir(DATASET_PATH):
        complete_path = os.path.join(DATASET_PATH, dir)
        print(f"Uploading {dir}")

        existing_tds = [td for td in available_tds if td.name == dir]
        if existing_tds:
            print(f"Training data '{dir}' already exists in the datalake. Skipping upload.")
            td = existing_tds[0]
            try:
                td.add_to_project(project)
                print(f"✓ Added existing '{dir}' to project '{project.name}'")
                successful_uploads += 1
            except Exception as e:
                print(f"✗ Failed to add existing '{dir}' to project: {e}")
                failed_uploads += 1
            continue
        else:
            try:
                td: TrainingData = simai_client.training_data.create(dir)
                td.upload_folder(complete_path)
                print(f"Uploaded {dir} successfully.")
            except Exception as e:
                print(f"Failed to upload {dir}: {e}")
                failed_uploads += 1
                continue


    print(f"\nUpload summary: {successful_uploads} successful, {failed_uploads} failed")


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Check and wait for data processing
-------------------------------------
After uploading, SimAI needs to process the training data.
Get all data in the current project and wait for them to be ready.

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.. code-block:: Python


    project_data = project.data

    print("\nWaiting for data processing to complete...")
    for data in project_data:
        print(f"Processing {data.name}...")
        data.wait()
        print(f"Data '{data.name}' is ready")


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Display project status summary
-------------------------------------
Show a summary of the project's data processing status:

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.. code-block:: Python


    print("\nProject Summary")
    print("=" * 50)

    ready_data = [data for data in project_data if data.is_ready]
    not_ready_data = [data for data in project_data if not data.is_ready]

    print(f"Total data in project: {len(project_data)}")
    print(f"Ready data: {len(ready_data)} of {len(project_data)}")
    print(f"Not ready data: {len(not_ready_data)} of {len(project_data)}")

    if not_ready_data:
        print("\nFailed data processing details:")
        for data in not_ready_data:
            print(f"- {data.name}: {data.failure_reason}")


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Next steps
----------------------------------
Once all data are ready, you can proceed to configure and build a model.
See the next tutorial: :ref:`ref_basic_build_model`


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