Create group_classifier.py

utils/group_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 = {
+    0: 'Children and Youth',
+    1: 'Informal sector workers',
+    2: 'Other',
+    3: 'Rural populations',
+    4: 'Sexual minorities (LGBTQI+)',
+    5: 'Urban populations',
+    6: 'Women'}
+
+@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