# -*- coding: utf-8 -*- """Untitled0.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/13kE5uGoL2gfzSwTJli-WZolqCNBZXxNV """ import tensorflow as tf import numpy as np import pandas as pd import streamlit as st import re import os import csv from tqdm import tqdm import faiss from nltk.translate.bleu_score import sentence_bleu from datetime import datetime def decontractions(phrase): """decontracted takes text and convert contractions into natural form. ref: https://stackoverflow.com/questions/19790188/expanding-english-language-contractions-in-python/47091490#47091490""" # specific phrase = re.sub(r"won\'t", "will not", phrase) phrase = re.sub(r"can\'t", "can not", phrase) phrase = re.sub(r"won\’t", "will not", phrase) phrase = re.sub(r"can\’t", "can not", phrase) # general phrase = re.sub(r"n\'t", " not", phrase) phrase = re.sub(r"\'re", " are", phrase) phrase = re.sub(r"\'s", " is", phrase) phrase = re.sub(r"\'d", " would", phrase) phrase = re.sub(r"\'ll", " will", phrase) phrase = re.sub(r"\'t", " not", phrase) phrase = re.sub(r"\'ve", " have", phrase) phrase = re.sub(r"\'m", " am", phrase) phrase = re.sub(r"n\’t", " not", phrase) phrase = re.sub(r"\’re", " are", phrase) phrase = re.sub(r"\’s", " is", phrase) phrase = re.sub(r"\’d", " would", phrase) phrase = re.sub(r"\’ll", " will", phrase) phrase = re.sub(r"\’t", " not", phrase) phrase = re.sub(r"\’ve", " have", phrase) phrase = re.sub(r"\’m", " am", phrase) return phrase def preprocess(text): # convert all the text into lower letters # remove the words betweent brakets () # remove these characters: {'$', ')', '?', '"', '’', '.', '°', '!', ';', '/', "'", '€', '%', ':', ',', '('} # replace these spl characters with space: '\u200b', '\xa0', '-', '/' text = text.lower() text = decontractions(text) text = re.sub('[$)\?"’.°!;\'€%:,(/]', '', text) text = re.sub('\u200b', ' ', text) text = re.sub('\xa0', ' ', text) text = re.sub('-', ' ', text) return text #importing bert tokenizer and loading the trained question embedding extractor model from transformers import AutoTokenizer, TFGPT2Model @st.cache(allow_output_mutation=True) def return_biobert_tokenizer_model(): '''returns pretrained biobert tokenizer and question extractor model''' biobert_tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/BioRedditBERT-uncased") question_extractor_model1=tf.keras.models.load_model('question_extractor_model_2_11') return biobert_tokenizer,question_extractor_model1 #importing gpt2 tokenizer and loading the trained gpt2 model from transformers import GPT2Tokenizer,TFGPT2LMHeadModel @st.cache(allow_output_mutation=True) def return_gpt2_tokenizer_model(): '''returns pretrained gpt2 tokenizer and gpt2 model''' gpt2_tokenizer=GPT2Tokenizer.from_pretrained("gpt2") tf_gpt2_model=TFGPT2LMHeadModel.from_pretrained("tf_gpt2_model_2_118_50000") return gpt2_tokenizer,tf_gpt2_model #preparing the faiss search qa=pd.read_pickle('train_gpt_data.pkl') question_bert = qa["Q_FFNN_embeds"].tolist() answer_bert = qa["A_FFNN_embeds"].tolist() question_bert = np.array(question_bert) answer_bert = np.array(answer_bert) question_bert = question_bert.astype('float32') answer_bert = answer_bert.astype('float32') answer_index = faiss.IndexFlatIP(answer_bert.shape[-1]) question_index = faiss.IndexFlatIP(question_bert.shape[-1]) answer_index.add(answer_bert) question_index.add(question_bert) print('finished initializing') #defining function to prepare the data for gpt inference #https://github.com/ash3n/DocProduct def preparing_gpt_inference_data(gpt2_tokenizer,question,question_embedding): topk=20 scores,indices=answer_index.search( question_embedding.astype('float32'), topk) q_sub=qa.iloc[indices.reshape(20)] line = '`QUESTION: %s `ANSWER: ' % ( question) encoded_len=len(gpt2_tokenizer.encode(line)) for i in q_sub.iterrows(): line='`QUESTION: %s `ANSWER: %s ' % (i[1]['question'],i[1]['answer']) + line line=line.replace('\n','') encoded_len=len(gpt2_tokenizer.encode(line)) if encoded_len>=1024: break return gpt2_tokenizer.encode(line)[-1024:] #function to generate answer given a question and the required answer length def give_answer(question,answer_len): preprocessed_question=preprocess(question) question_len=len(preprocessed_question.split(' ')) truncated_question=preprocessed_question if question_len>500: truncated_question=' '.join(preprocessed_question.split(' ')[:500]) biobert_tokenizer,question_extractor_model1= return_biobert_tokenizer_model() gpt2_tokenizer,tf_gpt2_model= return_gpt2_tokenizer_model() encoded_question= biobert_tokenizer.encode(truncated_question) max_length=512 padded_question=tf.keras.preprocessing.sequence.pad_sequences( [encoded_question], maxlen=max_length, padding='post') question_mask=[[1 if token!=0 else 0 for token in question] for question in padded_question] embeddings=question_extractor_model1({'question':np.array(padded_question),'question_mask':np.array(question_mask)}) gpt_input=preparing_gpt_inference_data(gpt2_tokenizer,truncated_question,embeddings.numpy()) mask_start = len(gpt_input) - list(gpt_input[::-1]).index(4600) + 1 input=gpt_input[:mask_start+1] if len(input)>(1024-answer_len): input=input[-(1024-answer_len):] gpt2_output=gpt2_tokenizer.decode(tf_gpt2_model.generate(input_ids=tf.constant([np.array(input)]),max_length=1024,temperature=0.7)[0]) answer=gpt2_output.rindex('`ANSWER: ') return gpt2_output[answer+len('`ANSWER: '):] #defining the final function to generate answer assuming default answer length to be 20 def final_func_1(question): answer_len=25 return give_answer(question,answer_len) def main(): st.title('Medical Chatbot') question=st.text_input('Question',"Type Here") result="" if st.button('ask'): #with st.spinner("You Know! an apple a day keeps doctor away!"): start=datetime.now() result=final_func_1(question) end_time =datetime.now() st.success("Here is the answer") st.text(result) st.text("result recieved within "+str((end_time-start).total_seconds())) if __name__=='__main__': main()