98dc001123
- revert pipeline and serve logic to pre figure detection data for image extraction changes: figure detection data as input not supported for now
44 lines
1.8 KiB
Python
44 lines
1.8 KiB
Python
from funcy import juxt
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from image_prediction.classifier.classifier import Classifier
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from image_prediction.classifier.image_classifier import ImageClassifier
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from image_prediction.compositor.compositor import TransformerCompositor
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from image_prediction.encoder.encoders.hash_encoder import HashEncoder
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from image_prediction.estimator.adapter.adapter import EstimatorAdapter
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from image_prediction.formatter.formatters.camel_case import Snake2CamelCaseKeyFormatter
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from image_prediction.formatter.formatters.enum import EnumFormatter
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from image_prediction.image_extractor.extractors.parsable import ParsablePDFImageExtractor
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from image_prediction.label_mapper.mappers.probability import ProbabilityMapper
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from image_prediction.model_loader.loader import ModelLoader
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from image_prediction.model_loader.loaders.mlflow import MlflowConnector
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from image_prediction.redai_adapter.mlflow import MlflowModelReader
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from image_prediction.transformer.transformers.coordinate.pdfnet import PDFNetCoordinateTransformer
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from image_prediction.transformer.transformers.response import ResponseTransformer
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def get_mlflow_model_loader(mlruns_dir):
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model_loader = ModelLoader(MlflowConnector(MlflowModelReader(mlruns_dir)))
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return model_loader
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def get_image_classifier(model_loader, model_identifier):
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model, classes = juxt(model_loader.load_model, model_loader.load_classes)(model_identifier)
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return ImageClassifier(Classifier(EstimatorAdapter(model), ProbabilityMapper(classes)))
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def get_extractor(**kwargs):
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image_extractor = ParsablePDFImageExtractor(**kwargs)
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return image_extractor
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def get_formatter():
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formatter = TransformerCompositor(
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PDFNetCoordinateTransformer(), EnumFormatter(), ResponseTransformer(), Snake2CamelCaseKeyFormatter()
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)
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return formatter
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def get_encoder():
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return HashEncoder()
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