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use onnx models, new deepdoc (#68)
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@ -1,11 +1,22 @@
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import copy
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import re
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from io import BytesIO
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from pptx import Presentation
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from rag.parser import tokenize, is_english
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from deepdoc.parser import tokenize, is_english
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from rag.nlp import huqie
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from rag.parser.pdf_parser import HuParser
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from deepdoc.parser import PdfParser
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class Ppt(object):
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@ -58,7 +69,7 @@ class Ppt(object):
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return [(txts[i], imgs[i]) for i in range(len(txts))]
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class Pdf(HuParser):
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class Pdf(PdfParser):
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def __init__(self):
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super().__init__()
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@ -74,7 +85,7 @@ class Pdf(HuParser):
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assert len(self.boxes) == len(self.page_images), "{} vs. {}".format(len(self.boxes), len(self.page_images))
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res = []
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#################### More precisely ###################
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# self._layouts_paddle(zoomin)
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# self._layouts_rec(zoomin)
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# self._text_merge()
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# pages = {}
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# for b in self.boxes:
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