Upload app.py

app.py

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+# -*- 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()