I am not able to get the Assistant API to find an uploaded file in the uploaded files. Could you please provide specific code to correct my code as I am quite poor coder?

I have tried to read API documentation but I cannot understand what the entire code should look lik and I do not fully understand how different element are linked to each other. Hence, a full code would be appreciated.

def initialize_openai_resources(file_path, model, analysis_type, user_prompt):
    """
    Initializes OpenAI client, uploads a file, creates an assistant, and initializes a thread based on predefined settings.

    Parameters:
    - file_path: Path to the file to be uploaded.
    - model: Model type for the assistant (e.g., 'gpt-3.5-turbo').

    Returns:
    - A dictionary containing the assistant and thread objects.
    """
    client = get_openai_client()
    print("OpenAI API key loaded successfully.n")

    # Upload a file
    with open(file_path, 'rb') as file_data:
        my_file = client.files.create(
            file=file_data,
            purpose="assistants"
        )
    print(f"File uploaded successfully with ID: {my_file.id}n")

    # Create Vector Store and upload a file there
    vector_store = client.beta.vector_stores.create(file_ids=[my_file.id])
    print(f"Vector store created successfully with ID: {vector_store.id}n")
    print(f"File with ID {my_file.id} has been successfully attached to Vector store with ID {vector_store.id}n")

    if analysis_type == 'thematic':
        instructions = ta_instruction.format(user_prompt=user_prompt)
    elif analysis_type == 'content':
        instructions = ca_instruction.format(user_prompt=user_prompt)
    elif analysis_type == 'grounded':
        instructions = gt_instruction.format(user_prompt=user_prompt)
    else:
        raise ValueError("Unsupported analysis type")

    # Create an Assistant
    my_assistant = client.beta.assistants.create(
        instructions=instructions,
        name="QDA-GPT",
        tools=[{"type": "file_search"}],
        model=model,
        tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}}
    )
    print(f"Assistant created successfully with ID: {my_assistant.id}n")

    # Create a Thread
    my_thread = client.beta.threads.create()

    # Validate that everything has been initialized successfully.
    print(f"Thread created successfully with ID: {my_thread.id}n")

    return {'assistant': my_assistant, 'file':my_file, 'thread': my_thread, 'vector_store': vector_store,}





def get_openai_response(content, assistant_id, thread_id):
    """
    Sends content to ChatGPT using an existing assistant and thread, and retrieves the response.

    Parameters:
    - content: The user's input to be sent to ChatGPT.
    - assistant_id: The ID of the initialized assistant.
    - thread_id: The ID of the initialized thread.

    Returns:
    - The response from ChatGPT as a string, or "No response." if no response is retrieved.
    """
    client = get_openai_client()
    print("OpenAI API key loaded successfully. Sending content to OpenAi Assistant.n")


    try:

        # Send message to the thread
        my_thread_message = client.beta.threads.messages.create(
            thread_id=thread_id,
            role="user",
            content=content
        )
        if not my_thread_message or not my_thread_message.content:
            return "Message creation failed.", "Failure"
        print(f"Message sent to thread. Message ID: {my_thread_message.id}n")


        # Run the assistant
        my_run = client.beta.threads.runs.create(
            thread_id=thread_id,
            assistant_id=assistant_id,
        )
        print(f"Assistant run initiated. Run ID: {my_run.id}n")

        # Retrieve the Run status
        # Periodically retrieve the Run to check on its status to see if it has moved to completed
        print("Run status: in_progress", end="")
        sys.stdout.flush()  # Ensure "in_progress" is displayed immediately
        while my_run.status in ["queued", "in_progress"]:
            keep_retrieving_run = client.beta.threads.runs.retrieve(
                thread_id=thread_id,
                run_id=my_run.id
            )

            if keep_retrieving_run.status == "in_progress":
                print(".", end="")
                sys.stdout.flush()  # Print each dot immediately
                time.sleep(0.5)    # Increase/reduce this if necessary
            elif keep_retrieving_run.status == "completed":
                print("nRun status: completedn")

                # Retrieve the Messages added by the Assistant to the Thread
                all_messages = client.beta.threads.messages.list(
                    thread_id=thread_id
                )
                if not all_messages or not all_messages.data or not all_messages.data[0].content:
                    return "Response retrieval failed.", "Failure"

                response = all_messages.data[0].content[0].text.value

                print("------------------------------------------------------------n")
                print("Response retrieved successfully.n")
                print("Assistant response processed successfully.n")
                return response
            else:
                print(f"nRun status: {keep_retrieving_run.status}n")
                break

        return "Failed to retrieve a valid response from OpenAI.n"

    except Exception as e:
        print(f"An error occurred: {str(e)} n")
        return "Failed to retrieve a valid response from OpenAI.n"