Create preprocessing.py

utils/preprocessing.py

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+from haystack.nodes.base import BaseComponent
+from haystack.schema import Document
+from haystack.nodes import PDFToTextOCRConverter, PDFToTextConverter
+from haystack.nodes import TextConverter, DocxToTextConverter, PreProcessor
+from typing import Callable, Dict, List, Optional, Text, Tuple, Union
+from typing_extensions import Literal
+import pandas as pd
+import logging
+import re
+import string
+from haystack.pipelines import Pipeline
+
+def useOCR(file_path: str)-> Text:
+    """
+    Converts image pdfs into text, Using the Farm-haystack[OCR]
+    
+    Params
+    ----------
+    file_path: file_path of uploade file, returned by add_upload function in 
+    uploadAndExample.py
+    
+    Returns the text file as string.
+    """
+
+    
+    converter = PDFToTextOCRConverter(remove_numeric_tables=True, 
+                                      valid_languages=["eng"])
+    docs = converter.convert(file_path=file_path, meta=None)
+    return docs[0].content
+
+
+
+
+class FileConverter(BaseComponent):
+    """
+    Wrapper class to convert uploaded document into text by calling appropriate 
+    Converter class, will use internally haystack PDFToTextOCR in case of image 
+    pdf. Cannot use the FileClassifier from haystack as its doesnt has any 
+    label/output class for image.
+    1. https://haystack.deepset.ai/pipeline_nodes/custom-nodes
+    2. https://docs.haystack.deepset.ai/docs/file_converters
+    3. https://github.com/deepset-ai/haystack/tree/main/haystack/nodes/file_converter
+    4. https://docs.haystack.deepset.ai/reference/file-converters-api
+    """
+
+    outgoing_edges = 1
+
+    def run(self, file_name: str , file_path: str, encoding: Optional[str]=None,
+            id_hash_keys: Optional[List[str]] = None,
+            ) -> Tuple[dict,str]:
+        """ this is required method to invoke the component in 
+            the pipeline implementation. 
+            
+        Params
+        ----------
+        file_name: name of file
+        file_path: file_path of uploade file, returned by add_upload function in 
+                    uploadAndExample.py
+        
+        See the links provided in Class docstring/description to see other params
+        
+        Return
+        ---------
+        output: dictionary, with key as identifier and value could be anything 
+                we need to return. In this case its the List of Hasyatck Document
+        
+        output_1: As there is only one outgoing edge, we pass 'output_1' string
+        """
+        try:
+            if file_name.endswith('.pdf'):
+                converter = PDFToTextConverter(remove_numeric_tables=True)
+            if file_name.endswith('.txt'):
+                converter = TextConverter(remove_numeric_tables=True)
+            if file_name.endswith('.docx'):
+                converter = DocxToTextConverter() 
+        except Exception as e:
+            logging.error(e)
+            return 
+
+
+
+        documents = []
+
+
+# encoding is empty, probably should be utf-8
+        document = converter.convert(
+                      file_path=file_path, meta=None, 
+                      encoding=encoding, id_hash_keys=id_hash_keys
+                      )[0]
+
+        text = document.content
+
+        # in case of scanned/images only PDF the content might contain only
+        # the page separator (\f or \x0c). We check if is so and use
+        # use the OCR to get the text.
+        filtered = re.sub(r'\x0c', '', text)
+
+        if filtered == "":
+            logging.info("Using OCR")
+            text = useOCR(file_path)
+
+        documents.append(Document(content=text, 
+                              meta={"name": file_name}, 
+                              id_hash_keys=id_hash_keys))
+        
+        logging.info('file conversion succesful')
+        output = {'documents': documents}
+        return output, 'output_1'
+
+    def run_batch():
+        """
+        we dont have requirement to process the multiple files in one go
+        therefore nothing here, however to use the custom node we need to have
+        this method for the class.
+        """
+        
+        return
+
+
+def basic(s:str, remove_punc:bool = False):
+
+    """
+    Performs basic cleaning of text.
+    Params
+    ----------
+    s: string to be processed
+    removePunc: to remove all Punctuation including ',' and '.' or not
+    
+    Returns: processed string: see comments in the source code for more info
+    """
+    
+    # Remove URLs
+    s = re.sub(r'^https?:\/\/.*[\r\n]*', ' ', s, flags=re.MULTILINE)
+    s = re.sub(r"http\S+", " ", s)
+
+    # Remove new line characters
+    s = re.sub('\n', ' ', s) 
+
+    # Remove punctuations
+    if remove_punc == True:
+      translator = str.maketrans(' ', ' ', string.punctuation) 
+      s = s.translate(translator)
+    # Remove distracting single quotes and dotted pattern
+    s = re.sub("\'", " ", s)
+    s = s.replace("..","") 
+    
+    return s.strip()
+
+def paraLengthCheck(paraList, max_len = 100):
+    """
+    There are cases where preprocessor cannot respect word limit, when using 
+    respect sentence boundary flag due to missing sentence boundaries.
+    Therefore we run one more round of split here for those paragraphs
+    
+    Params
+    ---------------
+    paraList : list of paragraphs/text
+    max_len : max length to be respected by sentences which bypassed 
+              preprocessor strategy
+              
+    """
+    new_para_list = []
+    for passage in paraList:
+        # check if para exceeds words limit
+        if len(passage.content.split()) > max_len:
+          # we might need few iterations example if para = 512 tokens
+          # we need to iterate 5 times to reduce para to size limit of '100'
+            iterations = int(len(passage.content.split())/max_len)
+            for i in range(iterations):
+                temp  = " ".join(passage.content.split()[max_len*i:max_len*(i+1)])
+                new_para_list.append((temp,passage.meta['page']))
+            temp  = " ".join(passage.content.split()[max_len*(i+1):])
+            new_para_list.append((temp,passage.meta['page']))
+        else:
+            # paragraphs which dont need any splitting
+            new_para_list.append((passage.content, passage.meta['page']))
+    
+    logging.info("New paragraphs length {}".format(len(new_para_list)))
+    return new_para_list
+
+class UdfPreProcessor(BaseComponent):
+    """
+    class to preprocess the document returned by FileConverter. It will check
+    for splitting strategy and splits the document by word or sentences and then
+    synthetically create the paragraphs. 
+    1. https://docs.haystack.deepset.ai/docs/preprocessor
+    2. https://docs.haystack.deepset.ai/reference/preprocessor-api
+    3. https://github.com/deepset-ai/haystack/tree/main/haystack/nodes/preprocessor
+    """
+    outgoing_edges = 1
+
+    def run(self, documents:List[Document], remove_punc:bool=False, 
+            split_by: Literal["sentence", "word"] = 'sentence',
+            split_length:int = 2, split_respect_sentence_boundary:bool = False,
+            split_overlap:int = 0):
+
+        """ this is required method to invoke the component in 
+        the pipeline implementation. 
+            
+        Params
+        ----------
+        documents: documents from the output dictionary returned by Fileconverter
+        remove_punc: to remove all Punctuation including ',' and '.' or not
+        split_by: document splitting strategy either as word or sentence
+        split_length: when synthetically creating the paragrpahs from document,
+                      it defines the length of paragraph.
+        split_respect_sentence_boundary: Used when using 'word' strategy for 
+        splititng of text.
+        split_overlap: Number of words or sentences that overlap when creating
+        the paragraphs. This is done as one sentence or 'some words' make sense
+        when  read in together with others. Therefore the overlap is used.
+        
+        Return
+        ---------
+        output: dictionary, with key as identifier and value could be anything 
+                we need to return. In this case the output will contain 4 objects
+                the paragraphs text list as List, Haystack document, Dataframe and 
+                one raw text file.
+        
+        output_1: As there is only one outgoing edge, we pass 'output_1' string
+      
+        """
+        
+        if split_by == 'sentence':
+            split_respect_sentence_boundary = False
+    
+        else:
+            split_respect_sentence_boundary = split_respect_sentence_boundary
+      
+        preprocessor = PreProcessor(
+            clean_empty_lines=True,
+            clean_whitespace=True,
+            clean_header_footer=True,
+            split_by=split_by,
+            split_length=split_length,
+            split_respect_sentence_boundary= split_respect_sentence_boundary,
+            split_overlap=split_overlap,
+
+            # will add page number only in case of PDF not for text/docx file.
+            add_page_number=True
+            )
+        
+        for i in documents:
+            # # basic cleaning before passing it to preprocessor.
+            # i = basic(i)
+            docs_processed = preprocessor.process([i])
+            for item in docs_processed:
+                item.content = basic(item.content, remove_punc= remove_punc)
+
+        df = pd.DataFrame(docs_processed)
+        all_text = " ".join(df.content.to_list())
+        para_list = df.content.to_list()
+        logging.info('document split into {} paragraphs'.format(len(para_list)))
+        output = {'documents': docs_processed,
+                  'dataframe': df,
+                  'text': all_text,
+                  'paraList': para_list
+                 }
+        return output, "output_1"
+    def run_batch():
+        """
+            we dont have requirement to process the multiple files in one go
+            therefore nothing here, however to use the custom node we need to have
+            this method for the class.
+        """
+        return
+
+def processingpipeline():
+    """
+    Returns the preprocessing pipeline. Will use FileConverter and UdfPreProcesor 
+    from utils.preprocessing
+    """
+
+    preprocessing_pipeline = Pipeline()
+    file_converter = FileConverter()
+    custom_preprocessor = UdfPreProcessor()
+
+    preprocessing_pipeline.add_node(component=file_converter, 
+                                    name="FileConverter", inputs=["File"])
+    preprocessing_pipeline.add_node(component = custom_preprocessor, 
+                            name ='UdfPreProcessor', inputs=["FileConverter"])
+
+    return preprocessing_pipeline