Upload app.py

app.py

@@ -0,0 +1,83 @@
+import os
+import streamlit as st
+import torch
+import string
+from transformers import BertTokenizer, BertForMaskedLM
+
+st.set_page_config(page_title='Next Word Prediction Model', page_icon=None, layout='centered', initial_sidebar_state='auto')
+
+@st.cache()
+def load_model(model_name):
+  try:
+    if model_name.lower() == "bert":
+      bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+      bert_model = BertForMaskedLM.from_pretrained('bert-base-uncased').eval()
+      return bert_tokenizer,bert_model
+  except Exception as e:
+    pass
+
+#use joblib to fast your function
+
+def decode(tokenizer, pred_idx, top_clean):
+  ignore_tokens = string.punctuation + '[PAD]'
+  tokens = []
+  for w in pred_idx:
+    token = ''.join(tokenizer.decode(w).split())
+    if token not in ignore_tokens:
+      tokens.append(token.replace('##', ''))
+  return '\n'.join(tokens[:top_clean])
+
+def encode(tokenizer, text_sentence, add_special_tokens=True):
+  text_sentence = text_sentence.replace('<mask>', tokenizer.mask_token)
+    # if <mask> is the last token, append a "." so that models dont predict punctuation.
+  if tokenizer.mask_token == text_sentence.split()[-1]:
+    text_sentence += ' .'
+
+    input_ids = torch.tensor([tokenizer.encode(text_sentence, add_special_tokens=add_special_tokens)])
+    mask_idx = torch.where(input_ids == tokenizer.mask_token_id)[1].tolist()[0]
+  return input_ids, mask_idx
+
+def get_all_predictions(text_sentence, top_clean=5):
+    # ========================= BERT =================================
+  input_ids, mask_idx = encode(bert_tokenizer, text_sentence)
+  with torch.no_grad():
+    predict = bert_model(input_ids)[0]
+  bert = decode(bert_tokenizer, predict[0, mask_idx, :].topk(top_k).indices.tolist(), top_clean)
+  return {'bert': bert}
+
+def get_prediction_eos(input_text):
+  try:
+    input_text += ' <mask>'
+    res = get_all_predictions(input_text, top_clean=int(top_k))
+    return res
+  except Exception as error:
+    pass
+
+try:
+
+  st.markdown("<h1 style='text-align: center;'>Next Word Prediction</h1>", unsafe_allow_html=True)
+  st.markdown("<h4 style='text-align: center; color: #B2BEB5;'><i>Keywords  : BertTokenizer, BertForMaskedLM, Pytorch</i></h4>", unsafe_allow_html=True)
+
+  st.sidebar.text("Next Word Prediction Model")
+  top_k = st.sidebar.slider("Select How many words do you need", 1 , 25, 1) #some times it is possible to have less words
+  print(top_k)
+  model_name = st.sidebar.selectbox(label='Select Model to Apply',  options=['BERT', 'XLNET'], index=0,  key = "model_name")
+
+  bert_tokenizer, bert_model  = load_model(model_name) 
+  input_text = st.text_area("Enter your text here")
+
+  #click outside box of input text to get result
+  res = get_prediction_eos(input_text)
+
+  answer = []
+  print(res['bert'].split("\n"))
+  for i in res['bert'].split("\n"):
+  	answer.append(i)
+  answer_as_string = "    ".join(answer)
+  st.text_area("Predicted List is Here",answer_as_string,key="predicted_list") 
+  st.image('https://freepngimg.com/download/keyboard/6-2-keyboard-png-file.png',use_column_width=True)
+  st.markdown("<h6 style='text-align: center; color: #808080;'>Created By <a href='https://github.com/7Vivek'>Vivek</a> - Checkout complete project <a href='https://github.com/7Vivek/Next-Word-Prediction-Streamlit'>here</a></h6>", unsafe_allow_html=True)
+
+except Exception as e:
+  print("SOME PROBLEM OCCURED")  
+