Create Q_A.py

Q_A.py

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+import streamlit as st
+import pysbd
+from transformers import pipeline
+from sentence_transformers import CrossEncoder
+from  transformers  import  AutoTokenizer, AutoModelWithLMHead, pipeline
+
+class QuestionAnswering:
+
+  def __init__(self):
+    model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
+    self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+    self.model = AutoModelWithLMHead.from_pretrained(model_name)    
+    self.sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
+    self.passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+    self.qa_model = pipeline("question-answering",'a-ware/bart-squadv2') 
+    
+  def fetch_answers(self, question, document):
+      document_paragraphs = document.splitlines()
+      query_paragraph_list = [(question, para) for para in document_paragraphs if len(para.strip()) > 0 ]
+      
+      scores = self.passage_retreival_model.predict(query_paragraph_list)
+      top_5_indices = scores.argsort()[-3:]
+      top_5_query_paragraph_list = [query_paragraph_list[i] for i in top_5_indices ]
+      top_5_query_paragraph_list.reverse()
+      
+      top_5_query_paragraph_answer_list = ""
+      count = 1
+      for query, passage in top_5_query_paragraph_list:
+       passage_sentences = self.sentence_segmenter.segment(passage)
+       answer = self.qa_model(question = query, context = passage)['answer']
+       evidence_sentence = ""
+       for i in range(len(passage_sentences)):
+           if answer.startswith('.') or answer.startswith(':'):
+               answer = answer[1:].strip()
+           if answer in passage_sentences[i]:
+               evidence_sentence = evidence_sentence + " " + passage_sentences[i]
+       
+                    
+       model_input = f"question: {query} context: {evidence_sentence}"
+       encoded_input = self.tokenizer([model_input],
+                                 return_tensors='pt',
+                                 max_length=512,
+                                 truncation=True)
+                                       
+       output = self.model.generate(input_ids = encoded_input.input_ids,
+                               attention_mask = encoded_input.attention_mask)
+       output_answer = self.tokenizer.decode(output[0], skip_special_tokens=True)
+  
+       result_str = ""+str(count)+": "+ output_answer +"\n"  
+       result_str = result_str + " "+ evidence_sentence + "\n\n"
+       top_5_query_paragraph_answer_list += result_str
+       count+=1
+       
+      return top_5_query_paragraph_answer_list
+
+