Delete utils/ghg_classifier.py

utils/ghg_classifier.py

@@ -1,96 +0,0 @@
-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