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64 lines
2.4 KiB
Python
64 lines
2.4 KiB
Python
"""Add spacy_nlp_pipeline_models table
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Revision ID: 31dd42e5ea6f
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Revises: a3b727e3ff71
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Create Date: 2022-10-13 12:47:50.870474
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"""
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from alembic import op
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import shutil
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import sqlalchemy as sa
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import os
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from app.models import User
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# revision identifiers, used by Alembic.
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revision = '31dd42e5ea6f'
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down_revision = 'a3b727e3ff71'
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branch_labels = None
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depends_on = None
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def upgrade():
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for user in User.query.all():
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spacy_nlp_pipeline_models_dir = os.path.join(user.path, 'spacy_nlp_pipeline_models')
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if os.path.exists(spacy_nlp_pipeline_models_dir):
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if not os.path.isdir(spacy_nlp_pipeline_models_dir):
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raise OSError(f'Not a directory: {spacy_nlp_pipeline_models_dir}')
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if not os.listdir(spacy_nlp_pipeline_models_dir):
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raise OSError(f'Directory not empty: {spacy_nlp_pipeline_models_dir}')
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else:
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os.mkdir(spacy_nlp_pipeline_models_dir)
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op.create_table(
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'spacy_nlp_pipeline_models',
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sa.Column('creation_date', sa.DateTime(), nullable=True),
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sa.Column('filename', sa.String(length=255), nullable=True),
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sa.Column('last_edited_date', sa.DateTime(), nullable=True),
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sa.Column('mimetype', sa.String(length=255), nullable=True),
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('user_id', sa.Integer(), nullable=True),
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sa.Column('title', sa.String(length=64), nullable=True),
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sa.Column('description', sa.String(length=255), nullable=True),
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sa.Column('version', sa.String(length=16), nullable=True),
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sa.Column('compatible_service_versions', sa.String(length=255), nullable=True),
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sa.Column('publisher', sa.String(length=128), nullable=True),
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sa.Column('publisher_url', sa.String(length=512), nullable=True),
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sa.Column('publishing_url', sa.String(length=512), nullable=True),
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sa.Column('publishing_year', sa.Integer(), nullable=True),
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sa.Column('shared', sa.Boolean(), nullable=True),
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sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
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sa.PrimaryKeyConstraint('id')
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)
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def downgrade():
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for user in User.query.all():
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spacy_nlp_pipeline_models_dir = os.path.join(user.path, 'spacy_nlp_pipeline_models')
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if os.path.exists(spacy_nlp_pipeline_models_dir):
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shutil.rmtree(spacy_nlp_pipeline_models_dir)
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op.drop_table('spacy_nlp_pipeline_models')
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