Create app.py

app.py

@@ -0,0 +1,108 @@
+import time
+import streamlit as st
+import torch
+import string
+
+from transformers import BertTokenizer, BertForMaskedLM
+
+@st.cache()
+def load_bert_model(model_name):
+  try:
+    bert_tokenizer = BertTokenizer.from_pretrained(model_name)
+    bert_model = BertForMaskedLM.from_pretrained(model_name).eval()
+    return bert_tokenizer,bert_model
+  except Exception as e:
+    pass
+
+
+
+  
+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_bert_prediction(input_text,top_k):
+  try:
+    input_text += ' <mask>'
+    res = get_all_predictions(input_text, top_clean=int(top_k))
+    return res
+  except Exception as error:
+    pass
+
+try:
+
+  st.title("Qualitative evaluation of Pretrained BERT models")
+  st.markdown("""
+        <a href="https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html"><small style="font-size:18px; color: #8f8f8f">This app is used to qualitatively examine the performance of pretrained models to do NER , <b>with no fine tuning</b></small></a>
+        """, unsafe_allow_html=True)
+  st.write("Incomplete. Work in progress...")
+  #st.write("https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html")
+  st.write("CLS vectors as well as the model prediction for a blank position are examined")
+
+  top_k = 10
+  print(top_k)
+
+
+  bert_tokenizer, bert_model  = load_bert_model('ajitrajasekharan/biomedical')
+  default_text = "Imatinib is used to treat"
+  
+ 
+  input_text = st.text_area(
+    label="Original text",
+    value=default_text,
+  )
+
+  start = None
+  if st.button("Submit"):
+    start = time.time()
+    with st.spinner("Computing"):
+    
+
+            
+
+            try:
+                res = get_bert_prediction(default_text,top_k)
+	
+
+                st.header("JSON:")
+
+                st.json(res)
+
+            except Exception as e:
+                st.error("Some error occured!" + str(e))
+                st.stop()
+	
+    st.write("---")
+
+
+	
+    if start is not None:
+        st.text(f"prediction took {time.time() - start:.2f}s")
+
+except Exception as e:
+  print("SOME PROBLEM OCCURED")  
+	
+