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How to use _cleanup_images method in tempest

Best Python code snippet using tempest_python

utils.py

Source:utils.py

...51    global cleanup_counter52    if cleanup_counter < 50:53        cleanup_counter += 154    else:55        _cleanup_images(os.path.dirname(item.path))56        cleanup_counter = 057    rimg_dir = os.path.join(os.path.dirname(item.path), img_dir)58    rimg_path = os.path.join(rimg_dir, item.name)59    rimg_url = os.path.join(os.path.dirname(item.url), img_dir, item.name)60    item.resized_img_url = rimg_url61    item.resized_img_path = rimg_path62    if os.path.exists(rimg_path):63        return64    ext = os.path.splitext(item.name)[1]65    if ext.lower()[1:] not in ['jpg', 'jpeg',  'gif', 'png']:66        item.resized_img_url = item.url67        item.resized_img_path = item.path68        return69    if not os.path.exists(rimg_dir):70        try:71            os.mkdir(rimg_dir)72        except:73            item.resized_img_url = item.url74            item.resized_img_path = item.path75    try:76        with Image.open(item.path) as im:77            im.thumbnail([rwidth, rheight], Image.ANTIALIAS)78            im.save(rimg_path)79    except:80        item.resized_img_url = item.url81        item.resized_img_path = item.path82def _list_dir(path):83    items = []84    if not os.path.exists(path):85        return items86    for item in os.listdir(path):87        item_path = os.path.join(path, item)88        if os.path.isfile(item_path):89            items.append(item)90    return items91def _cleanup_images(path):92    images = _list_dir(path)93    thumbs = _list_dir(os.path.join(path, img_thumbs))94    display = _list_dir(os.path.join(path, img_display))95    for image in images:96        if image in thumbs:97            del thumbs[thumbs.index(image)]98        if image in display:99            del display[display.index(image)]100    for t in thumbs:101        tpath = os.path.join(path, img_thumbs, t)102        if os.path.exists(tpath):103            os.remove(tpath)104    for d in display:105        dpath = os.path.join(path, img_display, d)...

image_serve.py

Source:image_serve.py

...5from PIL import Image6import os7import pandas as pd8import numpy as np9def _cleanup_images():10    files = os.listdir('.')11    for file in files:12        if file.endswith('.png'):13            os.remove(file)14def model_fn(model_dir):15    """loads model from previously saved artifact"""16    model = ImagePredictor.load(model_dir)17    return model18def transform_fn(model, request_body, input_content_type, output_content_type="application/json"):19    if input_content_type == "application/x-npy":20        buf = BytesIO(request_body)21        data = np.load(buf, allow_pickle=True)22        image_paths = []23        for i, image in enumerate(data):24            im = Image.fromarray(image)25            im_name = f'{i}.png'26            im.save(im_name)27            image_paths.append(im_name)28    elif input_content_type == "application/x-image":29        buf = BytesIO(request_body)30        im = Image.open(buf)31        image_paths = []32        im_name = 'test.png'33        im.save(im_name)34        image_paths.append(im_name)35    else:36        raise ValueError(37            f'{input_content_type} input content type not supported.'38        )39    if model._problem_type != REGRESSION:40        pred_proba = model.predict_proba(image_paths, as_pandas=True)41        pred = get_pred_from_proba_df(pred_proba, problem_type=model._problem_type)42        pred_proba.columns = [str(c) + '_proba' for c in pred_proba.columns]43        pred.name = str(pred.name) + '_pred' if pred.name is not None else 'pred'44        prediction = pd.concat([pred, pred_proba], axis=1)45    else:46        prediction = model.predict(image_paths, as_pandas=True)47    if isinstance(prediction, pd.Series):48        prediction = prediction.to_frame()49    if output_content_type == "application/json":50        output = prediction.to_json()51    elif output_content_type == "application/x-parquet":52        prediction.columns = prediction.columns.astype(str)53        output = prediction.to_parquet()54    elif output_content_type == "text/csv":55        output = prediction.to_csv(index=None)56    else:57        raise ValueError(f"{output_content_type} content type not supported")58    _cleanup_images()...

cleanup_media_files.py

Source:cleanup_media_files.py

...5class CleanupMediaFilesService(ICleanupMediaFilesService):6    """Cleanup media files service."""7    def cleanup_media_files(self, instance: models.Model) -> None:8        """Cleanup media files."""9        self._cleanup_images(instance)10    def _cleanup_images(self, instance: models.Model) -> None:11        for image_field in self._get_media_fields(instance, Image):12            media_instance = getattr(instance, image_field.name)13            if media_instance:14                self._delete_image(media_instance)15    def _delete_file(self, file_instance: models.Model) -> None:16        self._delete_media_from_storage(file_instance.storage_file)17        file_instance.delete()18    def _delete_image(self, image_instance: models.Model) -> None:19        self._delete_media_from_storage(image_instance.storage_image)20        image_instance.delete()21    def _delete_media_from_storage(22        self,23        media_field: models.FileField,24    ) -> None:...

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