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"""Gateway notebook for SVG Image Generation"""
import os
import tempfile
from pathlib import Path
from typing import Any
import pandas as pd
import polars as pl
from kaggle_evaluation.core.base_gateway import GatewayRuntimeError, GatewayRuntimeErrorType, IS_RERUN
import kaggle_evaluation.core.templates
from kaggle_evaluation.svg_constraints import SVGConstraints
class SVGGateway(kaggle_evaluation.core.templates.Gateway):
def __init__(self, data_path: str | Path | None = None):
super().__init__(target_column_name='svg')
self.set_response_timeout_seconds(60 * 5)
self.row_id_column_name = 'id'
self.data_path: Path = Path(data_path) if data_path else Path(__file__).parent
self.constraints: SVGConstraints = SVGConstraints()
def generate_data_batches(self):
test = pl.read_csv(self.data_path / 'test.csv')
for _, group in test.group_by('id'):
yield group.item(0, 0), group.item(0, 1) # id, description
def get_all_predictions(self):
row_ids, predictions = [], []
for id, description in self.generate_data_batches():
svg = self.predict(description)
self.validate(svg)
row_ids.append(id)
predictions.append(svg)
return predictions, row_ids
def validate(self, svg: str):
try:
self.constraints.validate_svg(svg)
except ValueError as err:
msg = f'SVG failed validation: {str(err)}'
raise GatewayRuntimeError(GatewayRuntimeErrorType.INVALID_SUBMISSION, msg)
def write_submission(self, predictions: list, row_ids: list) -> Path:
predictions = pl.DataFrame(
data={
self.row_id_column_name: row_ids,
self.target_column_name: predictions,
}
)
submission_path = Path('/kaggle/working/submission.csv')
if not IS_RERUN:
with tempfile.NamedTemporaryFile(prefix='kaggle-evaluation-submission-', suffix='.csv', delete=False, mode='w+') as f:
submission_path = Path(f.name)
predictions.write_csv(submission_path)
return submission_path
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