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CreatedDataText

trigger_created_data_text(sender, **kwargs)

This function will trigger the emotion detection model with the latest data

It will first look for the latest data_text, and then get the audio and image data based on the time range of the audio data

Parameters:

Name Type Description Default
sender

The sender of the signal

 required
kwargs

The data passed to the signal

 {}

Returns:

Source code in API/orchestrator/chain/created_data_text.py 
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@receiver(created_data_text)
def trigger_created_data_text(sender, **kwargs):
    """
    This function will trigger the emotion detection model with the latest data

    It will first look for the latest data_text,
    and then get the audio and image data based on the time range of the audio data

    Args:
        sender: The sender of the signal
        kwargs: The data passed to the signal
    Returns:

    """
    data = kwargs.get("data", {})
    track_id = kwargs.get("track_id", None)
    data_text_id = data.get("id", None)
    logger.debug(track_id)
    # get the audio data, which have not been process and have the text information
    if data_text_id:
        data_text = DataText.objects.get(id=data_text_id)
    else:
        logger.error("No data_text_id found")
        return None, None, None, None

    text = data_text.text
    data_audio = data_text.audio

    audio_file = f"audio/{data_audio.uid}/{data_audio.audio_file}"

    # get the image data based on the audio data time range
    # TODO: this will be changed rapidly
    start_time = data_audio.start_time
    end_time = data_audio.end_time
    # round the start to the minute level down
    start_time = start_time.replace(second=0, microsecond=0)
    # round the end to the minute level up
    end_time = end_time.replace(second=0, microsecond=0) + timedelta(minutes=1)
    logger.info(f"Start time: {start_time}, End time: {end_time}")
    logger.info(data_audio)
    # we will assume it comes from the same device
    # list all videos has overlap with [start_time, end_time]
    # get start_time and end_time has overlap with [start_time, end_time]
    videos_data = DataVideo.objects.filter(
        Q(start_time__lt=end_time, end_time__gt=start_time)
    )

    data_audio.multi_modal_conversation.video.add(*videos_data)
    data_audio.multi_modal_conversation.save()
    images_path = []
    for video_data in videos_data:
        image_folder_name = video_data.video_file.split(".")[0].rsplit("-", 1)[0]
        images_path.append(f"{video_data.uid}/frames/{image_folder_name}")

    # I need to read image files into List[np.ndarray]
    images_path_list = []
    for image_path in images_path:
        # loop the path, get all images
        folder = f"videos/{image_path}"
        images_path_list.append(folder)

    # trigger the model
    logger.info(f"Text: {text}, Audio: {audio_file}, Images: {len(images_path_list)}")

    task_params = {
        "text": text,
        "audio_file": audio_file,
        "images_path_list": images_path_list,
        "data_text_id": data_text.id,
    }

    user = kwargs.get("user", None)
    ClusterManager.chain_next(
        track_id=track_id,
        current_component="created_data_text",
        next_component_params=task_params,
        user=user,
    )

    return text, [audio_file], images_path_list, data_text