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| import dataclasses | |
| from typing import Any | |
| import streamlit as st | |
| from core.state import Metadata | |
| import mlcroissant as mlc | |
| from utils import needed_field | |
| from views.metadata import handle_metadata_change | |
| from views.metadata import MetadataEvent | |
| _NON_RELEVANT_METADATA = ["name", "distribution", "record_sets", "rdf"] | |
| _INFO_TEXT = """Croissant files are composed of three layers: | |
| - **Metadata** about the dataset covering Responsible AI, licensing and attributes of | |
| [sc\:Dataset](https://schema.org/Dataset). | |
| - **Resources**: The contents of a dataset as the underlying files | |
| ([`FileObject`](https://github.com/mlcommons/croissant/blob/main/docs/croissant-spec.md#fileobject)) | |
| and/or sets of files ([`FileSet`](https://github.com/mlcommons/croissant/blob/main/docs/croissant-spec.md#fileset)). | |
| - **RecordSets**: the sets of structured records obtained from one or more resources | |
| (typically a file or set of files) and the structure of these records, | |
| expressed as a set of fields (e.g., the columns of a table). | |
| The next three tabs will guide you through filling those layers. The errors if any will | |
| be displayed on this page. Once you are ready, you can download the dataset by clicking | |
| the export button in the upper right corner.""" | |
| def render_overview(): | |
| metadata: Metadata = st.session_state[Metadata] | |
| col1, col2 = st.columns([1, 1], gap="medium") | |
| with col1: | |
| key = "metadata-name" | |
| name = st.text_input( | |
| label=needed_field("Name"), | |
| key=key, | |
| value=metadata.name, | |
| placeholder="Dataset", | |
| on_change=handle_metadata_change, | |
| args=(MetadataEvent.NAME, metadata, key), | |
| ) | |
| if not name: | |
| st.stop() | |
| key = "metadata-url" | |
| url = st.text_input( | |
| label=needed_field("URL"), | |
| key=key, | |
| value=metadata.url, | |
| placeholder="URL to the dataset.", | |
| on_change=handle_metadata_change, | |
| args=(MetadataEvent.URL, metadata, key), | |
| ) | |
| if not url: | |
| st.stop() | |
| key = "metadata-description" | |
| st.text_area( | |
| label="Description", | |
| key=key, | |
| value=metadata.description, | |
| placeholder="Provide a clear description of the dataset.", | |
| on_change=handle_metadata_change, | |
| args=(MetadataEvent.DESCRIPTION, metadata, key), | |
| ) | |
| st.divider() | |
| left, middle, right = st.columns([1, 1, 1]) | |
| fields = [ | |
| field | |
| for field, value in dataclasses.asdict(metadata).items() | |
| if value and field not in _NON_RELEVANT_METADATA | |
| ] | |
| left.metric("Number of metadata", len(fields)) | |
| middle.metric("Number of resources", len(metadata.distribution)) | |
| right.metric("Number of RecordSets", len(metadata.record_sets)) | |
| with col2: | |
| user_started_editing = metadata.record_sets or metadata.distribution | |
| if user_started_editing: | |
| warning = "" | |
| try: | |
| issues = metadata.to_canonical().issues | |
| if issues.errors: | |
| warning += "**Errors**\n" | |
| for error in issues.errors: | |
| warning += f"{error}\n" | |
| if issues.warnings: | |
| warning += "**Warnings**\n" | |
| for warning in issues.warnings: | |
| warning += f"{warning}\n" | |
| except mlc.ValidationError as exception: | |
| warning += "**Errors**\n" | |
| warning += f"{str(exception)}\n" | |
| if warning: | |
| st.warning(warning, icon="⚠️") | |
| else: | |
| st.success("No validation issues detected!", icon="✅") | |
| st.info(_INFO_TEXT, icon="💡") | |