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utils/adapmit_classifier.py

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+from typing import List, Tuple
+from typing_extensions import Literal
+import logging
+import pandas as pd
+from pandas import DataFrame, Series
+from utils.config import getconfig
+from utils.preprocessing import processingpipeline
+import streamlit as st
+from transformers import pipeline
+
+@st.cache_resource
+def load_adapmitClassifier(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('adapmit','MODEL')
+    
+    logging.info("Loading Adaptation Mitigation classifier")    
+    doc_classifier = pipeline("text-classification", 
+                            model=classifier_name, 
+                            return_all_scores=True, 
+                            function_to_apply= "sigmoid")
+
+
+    return doc_classifier
+
+
+@st.cache_data
+def adapmit_classification(haystack_doc:pd.DataFrame,
+                        threshold:float = 0.5, 
+                        classifier_model:pipeline= 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 Adaptation-Mitigation Identification")
+    haystack_doc['Adapt-Mitig Label'] = 'NA'
+   # df1 = haystack_doc[haystack_doc['Target Label'] == 'TARGET']
+   # df = haystack_doc[haystack_doc['Target Label'] == 'NEGATIVE']
+
+    if not classifier_model:
+        classifier_model = st.session_state['adapmit_classifier']
+    
+    predictions = classifier_model(list(haystack_doc.text))
+     # converting the predictions to desired format
+    list_ = []
+    for i in range(len(predictions)):
+
+      temp = predictions[i]
+      placeholder = {}
+      for j in range(len(temp)):
+        placeholder[temp[j]['label']] = temp[j]['score']
+      list_.append(placeholder)
+    labels_ = [{**list_[l]} for l in range(len(predictions))]
+    truth_df = DataFrame.from_dict(labels_)
+    truth_df = truth_df.round(2)
+    truth_df = truth_df.astype(float) >= threshold
+    truth_df = truth_df.astype(str)
+    categories = list(truth_df.columns)
+    truth_df['Adapt-Mitig Label'] = truth_df.apply(lambda x: {i if x[i]=='True' 
+                                        else None for i in categories}, axis=1)
+    truth_df['Adapt-Mitig Label'] = truth_df.apply(lambda x: 
+                                  list(x['Adapt-Mitig Label'] -{None}),axis=1)
+    haystack_doc['Adapt-Mitig Label'] = list(truth_df['Adapt-Mitig Label'])
+    #df = pd.concat([df,df1])
+
+    return haystack_doc

utils/conditional_classifier.py

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+from typing import List, Tuple
+from typing_extensions import Literal
+import logging
+import pandas as pd
+from pandas import DataFrame, Series
+from utils.config import getconfig
+from utils.preprocessing import processingpipeline
+import streamlit as st
+from transformers import pipeline
+
+
+@st.cache_resource
+def load_conditionalClassifier(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('conditional','MODEL')
+    
+    logging.info("Loading conditional classifier")    
+    doc_classifier = pipeline("text-classification", 
+                            model=classifier_name, 
+                            top_k =1)
+
+    return doc_classifier
+
+
+@st.cache_data
+def conditional_classification(haystack_doc:pd.DataFrame,
+                        threshold:float = 0.8, 
+                        classifier_model:pipeline= None
+                        )->Tuple[DataFrame,Series]:
+    """
+    Text-Classification on the list of texts provided. Classifier provides the 
+    most appropriate label for each text. It informs if paragraph contains any 
+    netzero information or not.
+    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 
+    """
+    logging.info("Working on Conditionality Identification")
+    haystack_doc['Conditional Label'] = 'NA'
+    haystack_doc['Conditional Score'] = 0.0
+    haystack_doc['cond_check'] = False
+    haystack_doc['PA_check'] = haystack_doc['Policy-Action Label'].apply(lambda x: True if len(x) != 0 else False)
+                            
+    #df1 = haystack_doc[haystack_doc['PA_check'] == True]
+    #df = haystack_doc[haystack_doc['PA_check'] == False]                        
+    haystack_doc['cond_check'] = haystack_doc.apply(lambda x: True if (
+                (x['Target Label'] == 'TARGET') | (x['PA_check'] == True)) else
+                False, axis=1)
+    # we apply Netzero to only paragraphs which are classified as 'Target' related
+    temp = haystack_doc[haystack_doc['cond_check'] == True]
+    temp = temp.reset_index(drop=True)
+    df = haystack_doc[haystack_doc['cond_check'] == False]
+    df = df.reset_index(drop=True)
+
+    if not classifier_model:
+        classifier_model = st.session_state['conditional_classifier']
+    
+    results = classifier_model(list(temp.text))
+    labels_= [(l[0]['label'],l[0]['score']) for l in results]
+    temp['Conditional Label'],temp['Conditional Score'] = zip(*labels_)
+    # temp[' Label'] = temp['Netzero Label'].apply(lambda x: _lab_dict[x])
+    # merging Target with Non Target dataframe
+    df = pd.concat([df,temp])
+    df = df.drop(columns = ['cond_check','PA_check'])
+    df = df.reset_index(drop =True)
+    df.index += 1
+
+    return df

utils/config.py

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+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")
+
+
+# Declare all the necessary variables
+def get_classifier_params(model_name):
+    config = getconfig('paramconfig.cfg')
+    params = {}
+    params['model_name'] = config.get(model_name,'MODEL')
+    params['split_by'] = config.get(model_name,'SPLIT_BY')
+    params['split_length'] = int(config.get(model_name,'SPLIT_LENGTH'))
+    params['split_overlap'] = int(config.get(model_name,'SPLIT_OVERLAP'))
+    params['remove_punc'] = bool(int(config.get(model_name,'REMOVE_PUNC')))
+    params['split_respect_sentence_boundary'] = bool(int(config.get(model_name,'RESPECT_SENTENCE_BOUNDARY')))
+    params['threshold'] = float(config.get(model_name,'THRESHOLD'))
+    params['top_n'] = int(config.get(model_name,'TOP_KEY'))
+
+    return params

utils/ghg_classifier.py

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+from typing import List, Tuple
+from typing_extensions import Literal
+import logging
+import pandas as pd
+from pandas import DataFrame, Series
+from utils.config import getconfig
+from utils.preprocessing import processingpipeline
+import streamlit as st
+from transformers import pipeline
+
+# Labels dictionary ###
+_lab_dict = {
+                      'GHG':'GHG',
+                      'NOT_GHG':'NON GHG TRANSPORT TARGET',
+                      'NEGATIVE':'OTHERS',
+                      }
+        
+
+@st.cache_resource
+def load_ghgClassifier(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('ghg','MODEL')
+    
+    logging.info("Loading ghg classifier")    
+    doc_classifier = pipeline("text-classification", 
+                            model=classifier_name, 
+                            top_k =1)
+
+    return doc_classifier
+
+
+@st.cache_data
+def ghg_classification(haystack_doc:pd.DataFrame,
+                        threshold:float = 0.5, 
+                        classifier_model:pipeline= 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 GHG Extraction")
+    haystack_doc['GHG Label'] = 'NA'
+    haystack_doc['GHG Score'] = 0.0
+    # applying GHG Identifier to only 'Target' paragraphs.
+    temp = haystack_doc[haystack_doc['Target Label'] == 'TARGET']
+    temp = temp.reset_index(drop=True)
+    df = haystack_doc[haystack_doc['Target Label'] == 'NEGATIVE']
+    df = df.reset_index(drop=True)
+
+    if not classifier_model:
+        classifier_model = st.session_state['ghg_classifier']
+    
+    results = classifier_model(list(temp.text))
+    labels_= [(l[0]['label'],l[0]['score']) for l in results]
+    temp['GHG Label'],temp['GHG Score'] = zip(*labels_)
+    temp['GHG Label'] = temp['GHG Label'].apply(lambda x: _lab_dict[x])
+    # merge back Target and non-Target dataframe
+    df = pd.concat([df,temp])
+    df = df.reset_index(drop =True)
+    df['GHG Score'] = df['GHG Score'].round(2)
+    df.index += 1
+
+    return df

utils/indicator_classifier.py

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+from typing import List, Tuple
+from typing_extensions import Literal
+import logging
+import pandas as pd
+from pandas import DataFrame, Series
+from utils.config import getconfig
+from utils.preprocessing import processingpipeline
+import streamlit as st
+from transformers import pipeline
+
+
+@st.cache_resource
+def load_indicatorClassifier(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('indicator','MODEL')
+    
+    logging.info("Loading indicator classifier")
+    # we are using the pipeline as the model is multilabel and DocumentClassifier 
+    # from Haystack doesnt support multilabel
+    # in pipeline we use 'sigmoid' to explicitly tell pipeline to make it multilabel
+    # if not then it will automatically use softmax, which is not a desired thing.
+    # doc_classifier = TransformersDocumentClassifier(
+    #                     model_name_or_path=classifier_name,
+    #                     task="text-classification",
+    #                     top_k = None)
+
+    doc_classifier = pipeline("text-classification", 
+                            model=classifier_name, 
+                            return_all_scores=True, 
+                            function_to_apply= "sigmoid")
+
+    return doc_classifier
+
+
+@st.cache_data
+def indicator_classification(haystack_doc:pd.DataFrame,
+                        threshold:float = 0.5, 
+                        classifier_model:pipeline= 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 Indicator Identification")
+    haystack_doc['Indicator Label'] = 'NA'
+    haystack_doc['PA_check'] = haystack_doc['Policy-Action Label'].apply(lambda x: True if len(x) != 0 else False)
+                            
+    df1 = haystack_doc[haystack_doc['PA_check'] == True]
+    df = haystack_doc[haystack_doc['PA_check'] == False]
+    if not classifier_model:
+        classifier_model = st.session_state['indicator_classifier']
+    
+        predictions = classifier_model(list(df1.text))
+
+    list_ = []
+    for i in range(len(predictions)):
+
+      temp = predictions[i]
+      placeholder = {}
+      for j in range(len(temp)):
+        placeholder[temp[j]['label']] = temp[j]['score']
+      list_.append(placeholder)
+    labels_ = [{**list_[l]} for l in range(len(predictions))]
+    truth_df = DataFrame.from_dict(labels_)
+    truth_df = truth_df.round(2)
+    truth_df = truth_df.astype(float) >= threshold
+    truth_df = truth_df.astype(str)
+    categories = list(truth_df.columns)
+    truth_df['Indicator Label'] = truth_df.apply(lambda x: {i if x[i]=='True' else 
+                                              None for i in categories}, axis=1)
+    truth_df['Indicator Label'] = truth_df.apply(lambda x: list(x['Indicator Label'] 
+                                                            -{None}),axis=1)
+    df1['Indicator Label'] = list(truth_df['Indicator Label'])
+    df = pd.concat([df,df1])
+    df = df.drop(columns = ['PA_check'])
+    return df

utils/netzero_classifier.py

@@ -0,0 +1,88 @@
+from typing import List, Tuple
+from typing_extensions import Literal
+import logging
+import pandas as pd
+from pandas import DataFrame, Series
+from utils.config import getconfig
+from utils.preprocessing import processingpipeline
+import streamlit as st
+from transformers import pipeline
+
+# Labels dictionary ###
+_lab_dict = {
+            'NEGATIVE':'NO NETZERO TARGET',
+            'NETZERO':'NETZERO TARGET',
+            }
+
+@st.cache_resource
+def load_netzeroClassifier(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('netzero','MODEL')
+    
+    logging.info("Loading netzero classifier")    
+    doc_classifier = pipeline("text-classification", 
+                            model=classifier_name, 
+                            top_k =1)
+
+    return doc_classifier
+
+
+@st.cache_data
+def netzero_classification(haystack_doc:pd.DataFrame,
+                        threshold:float = 0.8, 
+                        classifier_model:pipeline= 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 Netzero Extraction")
+    haystack_doc['Netzero Label'] = 'NA'
+    haystack_doc['Netzero Score'] = 'NA'
+    temp = haystack_doc[haystack_doc['Target Label'] == 'TARGET']
+    df = haystack_doc[haystack_doc['Target Label'] == 'NEGATIVE']
+
+    if not classifier_model:
+        classifier_model = st.session_state['netzero_classifier']
+    
+    results = classifier_model(list(temp.text))
+    labels_= [(l[0]['label'],l[0]['score']) for l in results]
+    temp['Netzero Label'],temp['Netzero Score'] = zip(*labels_)
+    df = pd.concat([df,temp])
+    df = df.reset_index(drop =True)
+    df.index += 1
+
+    return df

utils/policyaction_classifier.py

@@ -0,0 +1,101 @@
+from typing import List, Tuple
+from typing_extensions import Literal
+import logging
+import pandas as pd
+from pandas import DataFrame, Series
+from utils.config import getconfig
+from utils.preprocessing import processingpipeline
+import streamlit as st
+from transformers import pipeline
+
+## Labels dictionary ###
+_lab_dict = {
+            'NEGATIVE':'NO TARGET INFO',
+            'TARGET':'TARGET',
+            }
+
+@st.cache_resource
+def load_policyactionClassifier(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('policyaction','MODEL')
+    
+    logging.info("Loading classifier")  
+      
+    doc_classifier = pipeline("text-classification",
+                              model=classifier_name,
+                            return_all_scores=True, 
+                            function_to_apply= "sigmoid")
+
+    return doc_classifier
+
+
+@st.cache_data
+def policyaction_classification(haystack_doc:pd.DataFrame,
+                        threshold:float = 0.5, 
+                        classifier_model:pipeline= 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 Policy/Action. Extraction")
+    haystack_doc['Policy-Action Label'] = 'NA'                       
+    if not classifier_model:
+        classifier_model = st.session_state['policyaction_classifier']
+    
+    predictions = classifier_model(list(haystack_doc.text))
+    list_ = []
+    for i in range(len(predictions)):
+
+      temp = predictions[i]
+      placeholder = {}
+      for j in range(len(temp)):
+        placeholder[temp[j]['label']] = temp[j]['score']
+      list_.append(placeholder)
+    labels_ = [{**list_[l]} for l in range(len(predictions))]
+    truth_df = DataFrame.from_dict(labels_)
+    truth_df = truth_df.round(2)
+    truth_df = truth_df.astype(float) >= threshold
+    truth_df = truth_df.astype(str)
+    categories = list(truth_df.columns)
+    truth_df['Policy-Action Label'] = truth_df.apply(lambda x: {i if x[i]=='True' 
+                                        else None for i in categories}, axis=1)
+    truth_df['Policy-Action Label'] = truth_df.apply(lambda x: 
+                                  list(x['Policy-Action Label'] -{None}),axis=1)
+
+    haystack_doc['Policy-Action Label'] =  list(truth_df['Policy-Action Label'])   
+
+    return haystack_doc

utils/preprocessing (1).py

@@ -0,0 +1,291 @@
+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
+

utils/sector_classifier.py

@@ -0,0 +1,106 @@
+from typing import List, Tuple
+from typing_extensions import Literal
+import logging
+import pandas as pd
+from pandas import DataFrame, Series
+from utils.config import getconfig
+from utils.preprocessing import processingpipeline
+import streamlit as st
+from transformers import pipeline
+
+
+@st.cache_resource
+def load_sectorClassifier(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('sector','MODEL')
+    
+    logging.info("Loading sector classifier")
+    # we are using the pipeline as the model is multilabel and DocumentClassifier 
+    # from Haystack doesnt support multilabel
+    # in pipeline we use 'sigmoid' to explicitly tell pipeline to make it multilabel
+    # if not then it will automatically use softmax, which is not a desired thing.
+    # doc_classifier = TransformersDocumentClassifier(
+    #                     model_name_or_path=classifier_name,
+    #                     task="text-classification",
+    #                     top_k = None)
+
+    doc_classifier = pipeline("text-classification", 
+                            model=classifier_name, 
+                            return_all_scores=True, 
+                            function_to_apply= "sigmoid")
+
+    return doc_classifier
+
+
+@st.cache_data
+def sector_classification(haystack_doc:pd.DataFrame,
+                        threshold:float = 0.5, 
+                        classifier_model:pipeline= 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 Sector Identification")
+    haystack_doc['Sector Label'] = 'NA'
+ #   df1 = haystack_doc[haystack_doc['Target Label'] == 'TARGET']
+  #  df = haystack_doc[haystack_doc['Target Label'] == 'NEGATIVE']
+    if not classifier_model:
+        classifier_model = st.session_state['sector_classifier']
+    
+        predictions = classifier_model(list(haystack_doc.text))
+
+    list_ = []
+    for i in range(len(predictions)):
+
+      temp = predictions[i]
+      placeholder = {}
+      for j in range(len(temp)):
+        placeholder[temp[j]['label']] = temp[j]['score']
+      list_.append(placeholder)
+    labels_ = [{**list_[l]} for l in range(len(predictions))]
+    truth_df = DataFrame.from_dict(labels_)
+    truth_df = truth_df.round(2)
+    truth_df = truth_df.astype(float) >= threshold
+    truth_df = truth_df.astype(str)
+    categories = list(truth_df.columns)
+    truth_df['Sector Label'] = truth_df.apply(lambda x: {i if x[i]=='True' else 
+                                              None for i in categories}, axis=1)
+    truth_df['Sector Label'] = truth_df.apply(lambda x: list(x['Sector Label'] 
+                                                            -{None}),axis=1)
+    haystack_doc['Sector Label'] = list(truth_df['Sector Label'])
+  #  df = pd.concat([df,df1])
+    return haystack_doc

utils/target_classifier.py

@@ -0,0 +1,89 @@
+from typing import List, Tuple
+from typing_extensions import Literal
+import logging
+import pandas as pd
+from pandas import DataFrame, Series
+from utils.config import getconfig
+from utils.preprocessing import processingpipeline
+import streamlit as st
+from transformers import pipeline
+
+## Labels dictionary ###
+_lab_dict = {
+            'NEGATIVE':'NO TARGET INFO',
+            'TARGET':'TARGET',
+            }
+
+@st.cache_resource
+def load_targetClassifier(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('target','MODEL')
+    
+    logging.info("Loading classifier")  
+      
+    doc_classifier = pipeline("text-classification", 
+                            model=classifier_name, 
+                            top_k =1)
+
+    return doc_classifier
+
+
+@st.cache_data
+def target_classification(haystack_doc:pd.DataFrame,
+                        threshold:float = 0.5, 
+                        classifier_model:pipeline= 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 Target Extraction")
+    if not classifier_model:
+        classifier_model = st.session_state['target_classifier']
+    
+    results = classifier_model(list(haystack_doc.text))
+    labels_= [(l[0]['label'],
+               l[0]['score']) for l in results]
+           
+
+    df1 = DataFrame(labels_, columns=["Target Label","Relevancy"])
+    df = pd.concat([haystack_doc,df1],axis=1)
+    
+    df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True)  
+    df.index += 1
+    df['Label_def'] = df['Target Label'].apply(lambda i: _lab_dict[i])
+
+    return df

utils/uploadAndExample (1).py

@@ -0,0 +1,39 @@
+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':
+        
+       # if 'filename' in st.session_state:
+          # Delete all the items in Session state
+        #    for key in st.session_state.keys():
+         #       del st.session_state[key]
+
+        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