JanetBackEnd/NLU.py

import spacy
import spacy_transformers
import torch

class NLU:    
    def __init__(self, query_rewriter, coref_resolver, intent_classifier, offensive_classifier, entity_extractor, ambig_classifier):

        self.intent_classifier = intent_classifier
        self.entity_extractor = entity_extractor
        self.offensive_classifier = offensive_classifier 
        self.coref_resolver = coref_resolver
        self.query_rewriter = query_rewriter
        self.ambig_classifier = ambig_classifier
    
    def _resolve_coref(self, history):
        to_resolve = history + ' <COREF_SEP_TOKEN> ' + self.to_process
        doc = self.coref_resolver(to_resolve)
        token_mention_mapper = {}
        output_string = ""
        clusters = [
                val for key, val in doc.spans.items() if key.startswith("coref_cluster")
        ]

        # Iterate through every found cluster
        for cluster in clusters:
            first_mention = cluster[0]
            # Iterate through every other span in the cluster
            for mention_span in list(cluster)[1:]:
                # Set first_mention as value for the first token in mention_span in the token_mention_mapper
                token_mention_mapper[mention_span[0].idx] = first_mention.text + mention_span[0].whitespace_
                for token in mention_span[1:]:
                    # Set empty string for all the other tokens in mention_span
                    token_mention_mapper[token.idx] = ""

        # Iterate through every token in the Doc
        for token in doc:
            # Check if token exists in token_mention_mapper
            if token.idx in token_mention_mapper:
                output_string += token_mention_mapper[token.idx]
            # Else add original token text
            else:
                output_string += token.text + token.whitespace_
        cleaned_query = output_string.split(" <COREF_SEP_TOKEN> ", 1)[1]
        return cleaned_query

    def _intentpredictor(self):
        pred = self.intent_classifier(self.to_process)[0]
        return pred['label'], pred['score']
    
    def _ambigpredictor(self):
        pred = self.ambig_classifier(self.to_process)[0]
        if pred['label'] in ['clear', 'somewhat_clear']:
            return False
        else:
            return True
    
    def _entityextractor(self):
        entities = []
        doc = self.entity_extractor(self.to_process)
        for entity in doc.ents:
            if entity.text not in ['.', ',', '?', ';']:
                entities.append({'entity': entity.label_, 'value': entity.text})
        return entities

    def _offensepredictor(self):
        pred = self.offensive_classifier(self.to_process)[0]['label']
        if pred != "neither":
            return True
        else:
            return False

    def _rewrite_query(self, history):
        text = history + " ||| " + self.to_process
        return self.query_rewriter(text)[0]['generated_text'] 


    def process_utterance(self, utterance, history_consec, history_sep):
        """
        Query -> coref resolution & intent extraction -> if intents are not confident or if query is ambig -> rewrite query and recheck -> if still ambig, ask a clarifying question
        """
        self.to_process = utterance
        
        self.to_process = self._resolve_coref(history_consec)
        
        intent, score = self._intentpredictor()
        #print(score)
        if score > 0.5:
            entities = self._entityextractor()
            offense = self._offensepredictor()
            if intent in ['FINDPAPER', 'FINDDATASET', 'SUMMARIZEPAPER'] and len(entities) == 0:
                return {"modified_prompt": self.to_process, "intent": intent, "entities": entities, "is_offensive": offense, "is_clear": False}
            return {"modified_prompt": self.to_process, "intent": intent, "entities": entities, "is_offensive": offense, "is_clear": True}
        else:
            if self._ambigpredictor():
                self.to_process = self._rewrite_query(history_sep)
                intent, score = self._intentpredictor()
                entities = self._entityextractor()
                offense = self._offensepredictor()
                if score > 0.5 or not self._ambigpredictor():
                    if intent in ['FINDPAPER', 'FINDDATASET', 'SUMMARIZEPAPER'] and len(entities) == 0:
                        return {"modified_prompt": self.to_process, "intent": intent, "entities": entities, "is_offensive": offense, "is_clear": False}
                    return {"modified_prompt": self.to_process, "intent": intent, "entities": entities, "is_offensive": offense, 
                            "is_clear": True}
                else:
                    return {"modified_prompt": self.to_process, "intent": intent, "entities": entities, "is_offensive": offense,
                            "is_clear": False}
            else:
                entities = self._entityextractor()
                offense = self._offensepredictor()
                if intent in ['FINDPAPER', 'FINDDATASET', 'SUMMARIZEPAPER'] and len(entities) == 0:
                    return {"modified_prompt": self.to_process, "intent": intent, "entities": entities, "is_offensive": offense, "is_clear": False}
                return {"modified_prompt": self.to_process, "intent": intent, "entities": entities, "is_offensive": offense, "is_clear": True}