# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------

"""
DESCRIPTION:
    Given an AIProjectClient, this sample demonstrates how to use the synchronous
    file operations using the OpenAI client: create, wait_for_processing, retrieve, content, list, and delete.

USAGE:
    python sample_files.py

    Before running the sample:

    pip install "azure-ai-projects>=2.0.0" python-dotenv

    Set these environment variables with your own values:
    1) FOUNDRY_PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
       Microsoft Foundry portal.
    2) FILE_PATH - Optional. Path to the file to upload. Defaults to the `data` folder.
"""

import os
from pathlib import Path

from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

load_dotenv()

endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
script_dir = Path(__file__).parent
file_path = os.environ.get("FILE_PATH", os.path.join(script_dir, "data", "test_file.jsonl"))

with (
    DefaultAzureCredential() as credential,
    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
    project_client.get_openai_client() as openai_client,
):
    # [START files_sample]
    print("Uploading file")
    with open(file_path, "rb") as f:
        uploaded_file = openai_client.files.create(file=f, purpose="fine-tune")
    print(uploaded_file)

    print("Waits for the given file to be processed, default timeout is 30 mins")
    processed_file = openai_client.files.wait_for_processing(uploaded_file.id)
    print(processed_file)

    print(f"Retrieving file metadata with ID: {processed_file.id}")
    retrieved_file = openai_client.files.retrieve(processed_file.id)
    print(retrieved_file)

    print(f"Retrieving file content with ID: {processed_file.id}")
    file_content = openai_client.files.content(processed_file.id)
    print(file_content.content)

    print("Listing all files:")
    for file in openai_client.files.list():
        print(file)

    print(f"Deleting file with ID: {processed_file.id}")
    deleted_file = openai_client.files.delete(processed_file.id)
    print(f"Successfully deleted file: {deleted_file.id}")
    # [END files_sample]
