Update utils/adapmit_classifier.py

utils/adapmit_classifier.py

@@ -1,101 +1,100 @@
-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.config import getconfig
-from utils.preprocessing import processingpipeline
-import streamlit as st
-from haystack.nodes import TransformersDocumentClassifier
-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(df1.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)
-    df1['Adapt-Mitig Label'] = list(truth_df['Adapt-Mitig Label'])
-    df = pd.concat([df,df1])
-
-    return df
+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.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(df1.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)
+    df1['Adapt-Mitig Label'] = list(truth_df['Adapt-Mitig Label'])
+    df = pd.concat([df,df1])
+
+    return df