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| import json | |
| from collections import OrderedDict | |
| from pathlib import Path | |
| from typing import Any | |
| import gradio as gr | |
| from gradio.flagging import HuggingFaceDatasetSaver, client_utils | |
| import huggingface_hub | |
| class myHuggingFaceDatasetSaver(HuggingFaceDatasetSaver): | |
| """ | |
| Custom HuggingFaceDatasetSaver to save images/audio to disk. | |
| Gradio's implementation seems to have a bug. | |
| """ | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| def _deserialize_components( | |
| self, | |
| data_dir: Path, | |
| flag_data: list[Any], | |
| flag_option: str = "", | |
| username: str = "", | |
| ) -> tuple[dict[Any, Any], list[Any]]: | |
| """Deserialize components and return the corresponding row for the flagged sample. | |
| Images/audio are saved to disk as individual files. | |
| """ | |
| # Components that can have a preview on dataset repos | |
| file_preview_types = {gr.Audio: "Audio", gr.Image: "Image"} | |
| # Generate the row corresponding to the flagged sample | |
| features = OrderedDict() | |
| row = [] | |
| for component, sample in zip(self.components, flag_data): | |
| # Get deserialized object (will save sample to disk if applicable -file, audio, image,...-) | |
| label = component.label or "" | |
| save_dir = data_dir / client_utils.strip_invalid_filename_characters(label) | |
| save_dir.mkdir(exist_ok=True, parents=True) | |
| deserialized = component.flag(sample, save_dir) | |
| if isinstance(component, gr.Image) and isinstance(sample, dict): | |
| deserialized = json.loads(deserialized)['path'] # dirty hack | |
| # Add deserialized object to row | |
| features[label] = {"dtype": "string", "_type": "Value"} | |
| try: | |
| assert Path(deserialized).exists() | |
| row.append(str(Path(deserialized).relative_to(self.dataset_dir))) | |
| except (AssertionError, TypeError, ValueError): | |
| deserialized = "" if deserialized is None else str(deserialized) | |
| row.append(deserialized) | |
| # If component is eligible for a preview, add the URL of the file | |
| # Be mindful that images and audio can be None | |
| if isinstance(component, tuple(file_preview_types)): # type: ignore | |
| for _component, _type in file_preview_types.items(): | |
| if isinstance(component, _component): | |
| features[label + " file"] = {"_type": _type} | |
| break | |
| if deserialized: | |
| path_in_repo = str( # returned filepath is absolute, we want it relative to compute URL | |
| Path(deserialized).relative_to(self.dataset_dir) | |
| ).replace("\\", "/") | |
| row.append( | |
| huggingface_hub.hf_hub_url( | |
| repo_id=self.dataset_id, | |
| filename=path_in_repo, | |
| repo_type="dataset", | |
| ) | |
| ) | |
| else: | |
| row.append("") | |
| features["flag"] = {"dtype": "string", "_type": "Value"} | |
| features["username"] = {"dtype": "string", "_type": "Value"} | |
| row.append(flag_option) | |
| row.append(username) | |
| return features, row |