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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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73 lines
2.7 KiB
73 lines
2.7 KiB
from typing import List
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import filetype
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from celery import shared_task
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from django.conf import settings
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from django.contrib.auth import get_user_model
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from django.shortcuts import get_object_or_404
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from django_drf_filepond.api import store_upload
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from django_drf_filepond.models import TemporaryUpload
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from .datasets import load_dataset
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from .pipeline.catalog import Format, create_file_format
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from .pipeline.exceptions import (
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FileImportException,
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FileTypeException,
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MaximumFileSizeException,
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)
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from .pipeline.readers import FileName
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from projects.models import Project
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def check_file_type(filename, file_format: Format, filepath: str):
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if not settings.ENABLE_FILE_TYPE_CHECK:
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return
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kind = filetype.guess(filepath)
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if not file_format.validate_mime(kind.mime):
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raise FileTypeException(filename, kind.mime, file_format.accept_types)
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def check_uploaded_files(upload_ids: List[str], file_format: Format):
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errors: List[FileImportException] = []
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cleaned_ids = []
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temporary_uploads = TemporaryUpload.objects.filter(upload_id__in=upload_ids)
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for tu in temporary_uploads:
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if tu.file.size > settings.MAX_UPLOAD_SIZE:
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errors.append(MaximumFileSizeException(tu.upload_name, settings.MAX_UPLOAD_SIZE))
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tu.delete()
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continue
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try:
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check_file_type(tu.upload_name, file_format, tu.get_file_path())
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except FileTypeException as e:
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errors.append(e)
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tu.delete()
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continue
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cleaned_ids.append(tu.upload_id)
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return cleaned_ids, errors
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@shared_task(autoretry_for=(Exception,), retry_backoff=True, retry_jitter=True)
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def import_dataset(user_id, project_id, file_format: str, upload_ids: List[str], task: str, **kwargs):
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project = get_object_or_404(Project, pk=project_id)
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user = get_object_or_404(get_user_model(), pk=user_id)
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try:
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fmt = create_file_format(file_format)
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upload_ids, errors = check_uploaded_files(upload_ids, fmt)
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temporary_uploads = TemporaryUpload.objects.filter(upload_id__in=upload_ids)
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filenames = [
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FileName(full_path=tu.get_file_path(), generated_name=tu.file.name, upload_name=tu.upload_name)
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for tu in temporary_uploads
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]
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dataset = load_dataset(task, fmt, filenames, project, **kwargs)
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dataset.save(user, batch_size=settings.IMPORT_BATCH_SIZE)
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upload_to_store(temporary_uploads)
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errors.extend(dataset.errors)
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return {"error": [e.dict() for e in errors]}
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except FileImportException as e:
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return {"error": [e.dict()]}
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def upload_to_store(temporary_uploads):
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for tu in temporary_uploads:
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store_upload(tu.upload_id, destination_file_path=tu.file.name)
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