Delete utils

utils/checkconfig.py

@@ -1,15 +0,0 @@
-import configparser
-import logging
-
-def getconfig(configfile_path:str):
-    """
-    configfile_path: file path of .cfg file
-    """
-
-    config = configparser.ConfigParser()
-
-    try:
-        config.read_file(open(configfile_path))
-        return config
-    except:
-        logging.warning("config file not found")

utils/preprocessing.py

@@ -1,260 +0,0 @@
-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 = []
-
-        document = converter.convert(
-                      file_path=file_path, meta=None, 
-                      encoding=encoding, id_hash_keys=id_hash_keys
-                      )[0]
-
-        text = document.content
-
-        # if file is image pdf then it will have {'content': "\x0c\x0c\x0c\x0c"}
-        # subsitute this substring with '',and check if content is empty string
-
-        text = re.sub(r'\x0c', '', text)
-        documents.append(Document(content=text, 
-                              meta={"name": file_name}, 
-                              id_hash_keys=id_hash_keys))
-
-        
-        # check if text is empty and apply pdfOCR converter.
-        for i in documents:
-            if i.content == "":
-                logging.info("Using OCR")
-                i.content = useOCR(file_path)
-        
-        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()
-
-
-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
-

utils/streamlitcheck.py

@@ -1,42 +0,0 @@
-import logging
-try:
-    import streamlit as st
-except ImportError:
-    logging.info("Streamlit not installed")
-
-
-def check_streamlit():
-    """
-    Function to check whether python code is run within streamlit
-
-    Returns
-    -------
-    use_streamlit : boolean
-        True if code is run within streamlit, else False
-    """
-    try:
-        from streamlit.scriptrunner.script_run_context import get_script_run_ctx
-        if not get_script_run_ctx():
-            use_streamlit = False
-        else:
-            use_streamlit = True
-    except ModuleNotFoundError:
-        use_streamlit = False
-    return use_streamlit
-
-def disable_other_checkboxes(*other_checkboxes_keys):
-    for checkbox_key in other_checkboxes_keys:
-        st.session_state[checkbox_key] = False
-
-def checkbox_without_preselect(keylist):
-    dict_ = {}
-    for i,key_val in enumerate(keylist):
-        dict_[i] = st.checkbox(key_val,key = key_val,
-        on_change = disable_other_checkboxes,
-        args=tuple(list(filter(lambda x: x!= key_val, keylist))),)
-    
-    for key,val in dict_.items():
-        if val == True:
-            return keylist[int(key)]
-    
-    return None

utils/uploadAndExample.py

@@ -1,33 +0,0 @@
-import streamlit as st
-import tempfile
-import json
-
-def add_upload(choice):
-    """
-    Provdies the user with choice to either 'Upload Document' or 'Try Example'.
-    Based on user choice runs streamlit processes and save the path and name of
-    the 'file' to streamlit session_state which then can be fetched later.
-
-    """
-    
-    if choice == 'Upload Document':
-        uploaded_file = st.sidebar.file_uploader('Upload the File',
-                            type=['pdf', 'docx', 'txt'])
-        if uploaded_file is not None:
-            with tempfile.NamedTemporaryFile(mode="wb", delete = False) as temp:
-                bytes_data = uploaded_file.getvalue()
-                temp.write(bytes_data)
-                st.session_state['filename'] = uploaded_file.name
-                st.session_state['filepath'] = temp.name
-
-                
-    else:
-        # listing the options
-        with open('docStore/sample/files.json','r') as json_file:
-            files = json.load(json_file)
-
-        option = st.sidebar.selectbox('Select the example document',
-                              list(files.keys()))
-        file_name = file_path  = files[option]
-        st.session_state['filename'] = file_name
-        st.session_state['filepath'] = file_path

utils/vulnerability_classifier.py

@@ -1,177 +0,0 @@
-from haystack.nodes import TransformersDocumentClassifier
-from haystack.schema import Document
-from typing import List, Tuple
-from typing_extensions import Literal
-import logging
-import pandas as pd
-from pandas import DataFrame, Series
-from utils.checkconfig import getconfig
-from utils.streamlitcheck import check_streamlit
-from utils.preprocessing import processingpipeline
-try:
-    import streamlit as st
-except ImportError:
-    logging.info("Streamlit not installed")
-
-## Labels dictionary ###
-_lab_dict = {0: 'Agricultural communities',
-             1: 'Children',
-             2: 'Coastal communities',
-             3: 'Ethnic, racial or other minorities',
-             4: 'Fishery communities',
-             5: 'Informal sector workers',
-             6: 'Members of indigenous and local communities',
-             7: 'Migrants and displaced persons',
-             8: 'Older persons',
-             9: 'Other',
-             10: 'Persons living in poverty',
-             11: 'Persons with disabilities',
-             12: 'Persons with pre-existing health conditions',
-             13: 'Residents of drought-prone regions',
-             14: 'Rural populations',
-             15: 'Sexual minorities (LGBTQI+)',
-             16: 'Urban populations',
-             17: 'Women and other genders'}
-
-@st.cache(allow_output_mutation=True)
-def load_Classifier(config_file:str = None, classifier_name:str = None):
-    """
-    loads the document classifier using haystack, where the name/path of model
-    in HF-hub as string is used to fetch the model object.Either configfile or 
-    model should be passed.
-    1. https://docs.haystack.deepset.ai/reference/document-classifier-api
-    2. https://docs.haystack.deepset.ai/docs/document_classifier
-
-    Params
-    --------
-    config_file: config file path from which to read the model name
-    classifier_name: if modelname is passed, it takes a priority if not \
-    found then will look for configfile, else raise error.
-
-
-    Return: document classifier model
-    """
-    if not classifier_name:
-        if not config_file:
-            logging.warning("Pass either model name or config file")
-            return
-        else:
-            config = getconfig(config_file)
-            classifier_name = config.get('vulnerability','MODEL')
-    
-    logging.info("Loading classifier")    
-    doc_classifier = TransformersDocumentClassifier(
-                        model_name_or_path=classifier_name,
-                        task="text-classification")
-
-    return doc_classifier
-        
-
-@st.cache(allow_output_mutation=True)
-def vulnerability_classification(haystack_doc:List[Document],
-                        threshold:float = 0.8, 
-                        classifier_model:TransformersDocumentClassifier= None
-                        )->Tuple[DataFrame,Series]:
-    """
-    Text-Classification on the list of texts provided. Classifier provides the 
-    most appropriate label for each text. these labels are in terms of if text 
-    belongs to which particular Sustainable Devleopment Goal (SDG).
-
-    Params
-    ---------
-    haystack_doc: List of haystack Documents. The output of Preprocessing Pipeline 
-    contains the list of paragraphs in different format,here the list of 
-    Haystack Documents is used.
-    threshold: threshold value for the model to keep the results from classifier
-    classifiermodel: you can pass the classifier model directly,which takes priority
-    however if not then looks for model in streamlit session.
-    In case of streamlit avoid passing the model directly.
-
-
-    Returns
-    ----------
-    df: Dataframe with two columns['SDG:int', 'text']
-    x: Series object with the unique SDG covered in the document uploaded and 
-    the number of times it is covered/discussed/count_of_paragraphs. 
-
-    """
-    logging.info("Working on vulnerability Classification")
-    if not classifier_model:
-        if check_streamlit():
-            classifier_model = st.session_state['vulnerability_classifier']
-        else:
-            logging.warning("No streamlit envinornment found, Pass the classifier")
-            return
-    
-    results = classifier_model.predict(haystack_doc)
-
-
-    labels_= [(l.meta['classification']['label'],
-            l.meta['classification']['score'],l.content,) for l in results]
-
-    df = DataFrame(labels_, columns=["vulnerability","Relevancy","text"])
-    
-    df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True)  
-    df.index += 1
-    df =df[df['Relevancy']>threshold]
-
-    # creating the dataframe for value counts of SDG, along with 'title' of SDGs
-    x = df['vulnerability'].value_counts()
-    x = x.rename('count')
-    x = x.rename_axis('vulnerability').reset_index()
-    x["Vulnerability"] = pd.to_numeric(x["vulnerability"])
-    x = x.sort_values(by=['count'], ascending=False)
-    x['vulnerability_name'] = x['vulnerability'].apply(lambda x: _lab_dict[x])
-    x['vulnerability_Num'] = x['vulnerability'].apply(lambda x: "vulnerability "+str(x))
-
-    df['vulnerability'] = pd.to_numeric(df['vulnerability'])
-    df = df.sort_values('vulnerability')
-    
-    return df, x
-
-def runPreprocessingPipeline(file_name:str, file_path:str, 
-            split_by: Literal["sentence", "word"] = 'sentence',
-            split_length:int = 2, split_respect_sentence_boundary:bool = False,
-            split_overlap:int = 0,remove_punc:bool = False)->List[Document]:
-    """
-    creates the pipeline and runs the preprocessing pipeline, 
-    the params for pipeline are fetched from paramconfig
-
-    Params
-    ------------
-
-    file_name: filename, in case of streamlit application use 
-    st.session_state['filename']
-    file_path: filepath, in case of streamlit application use st.session_state['filepath']
-    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.
-    remove_punc: to remove all Punctuation including ',' and '.' or not
-
-
-    Return
-    --------------
-    List[Document]: When preprocessing pipeline is run, the output dictionary 
-    has four objects. For the Haysatck implementation of SDG classification we, 
-    need to use the List of Haystack Document, which can be fetched by 
-    key = 'documents' on output.
-
-    """
-
-    processing_pipeline = processingpipeline()
-
-    output_pre = processing_pipeline.run(file_paths = file_path, 
-                            params= {"FileConverter": {"file_path": file_path, \
-                                        "file_name": file_name}, 
-                                     "UdfPreProcessor": {"remove_punc": remove_punc, \
-                                            "split_by": split_by, \
-                                            "split_length":split_length,\
-                                            "split_overlap": split_overlap, \
-        "split_respect_sentence_boundary":split_respect_sentence_boundary}})
-    
-    return output_pre