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| import time | |
| import streamlit as st | |
| import torch | |
| import string | |
| from annotated_text import annotated_text | |
| from flair.data import Sentence | |
| from flair.models import SequenceTagger | |
| from transformers import BertTokenizer, BertForMaskedLM | |
| import BatchInference as bd | |
| import batched_main_NER as ner | |
| import aggregate_server_json as aggr | |
| import json | |
| DEFAULT_TOP_K = 20 | |
| SPECIFIC_TAG=":__entity__" | |
| def POS_get_model(model_name): | |
| val = SequenceTagger.load(model_name) # Load the model | |
| return val | |
| def getPos(s: Sentence): | |
| texts = [] | |
| labels = [] | |
| for t in s.tokens: | |
| for label in t.annotation_layers.keys(): | |
| texts.append(t.text) | |
| labels.append(t.get_labels(label)[0].value) | |
| return texts, labels | |
| def getDictFromPOS(texts, labels): | |
| return [["dummy",t,l,"dummy","dummy" ] for t, l in zip(texts, labels)] | |
| 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 | |
| def load_pos_model(): | |
| checkpoint = "flair/pos-english" | |
| return POS_get_model(checkpoint) | |
| def init_session_states(): | |
| if 'top_k' not in st.session_state: | |
| st.session_state['top_k'] = 20 | |
| if 'pos_model' not in st.session_state: | |
| st.session_state['pos_model'] = None | |
| if 'bio_model' not in st.session_state: | |
| st.session_state['bio_model'] = None | |
| if 'phi_model' not in st.session_state: | |
| st.session_state['phi_model'] = None | |
| if 'ner_bio' not in st.session_state: | |
| st.session_state['ner_bio'] = None | |
| if 'ner_phi' not in st.session_state: | |
| st.session_state['ner_phi'] = None | |
| if 'aggr' not in st.session_state: | |
| st.session_state['aggr'] = None | |
| def get_pos_arr(input_text,display_area): | |
| if (st.session_state['pos_model'] is None): | |
| display_area.text("Loading model 3 of 3.Loading POS model...") | |
| st.session_state['pos_model'] = load_pos_model() | |
| s = Sentence(input_text) | |
| st.session_state['pos_model'].predict(s) | |
| texts, labels = getPos(s) | |
| pos_results = getDictFromPOS(texts, labels) | |
| return pos_results | |
| def perform_inference(text,display_area): | |
| if (st.session_state['bio_model'] is None): | |
| display_area.text("Loading model 1 of 3. Bio model...") | |
| st.session_state['bio_model'] = bd.BatchInference("bio/desc_a100_config.json",'ajitrajasekharan/biomedical',False,False,DEFAULT_TOP_K,True,True, "bio/","bio/a100_labels.txt",False) | |
| if (st.session_state['phi_model'] is None): | |
| display_area.text("Loading model 2 of 3. PHI model...") | |
| st.session_state['phi_model'] = bd.BatchInference("bbc/desc_bbc_config.json",'bert-base-cased',False,False,DEFAULT_TOP_K,True,True, "bbc/","bbc/bbc_labels.txt",False) | |
| #Load POS model if needed and gets POS tags | |
| if (SPECIFIC_TAG not in text): | |
| pos_arr = get_pos_arr(text,display_area) | |
| else: | |
| pos_arr = None | |
| if (st.session_state['ner_bio'] is None): | |
| display_area.text("Initializing BIO module...") | |
| st.session_state['ner_bio'] = ner.UnsupNER("bio/ner_a100_config.json") | |
| if (st.session_state['ner_phi'] is None): | |
| display_area.text("Initializing PHI module...") | |
| st.session_state['ner_phi'] = ner.UnsupNER("bbc/ner_bbc_config.json") | |
| if (st.session_state['aggr'] is None): | |
| display_area.text("Initializing Aggregation modeule...") | |
| st.session_state['aggr'] = aggr.AggregateNER("./ensemble_config.json") | |
| display_area.text("Getting results from BIO model...") | |
| bio_descs = st.session_state['bio_model'].get_descriptors(text,pos_arr) | |
| display_area.text("Getting results from PHI model...") | |
| phi_results = st.session_state['phi_model'].get_descriptors(text,pos_arr) | |
| display_area.text("Aggregating BIO & PHI results...") | |
| bio_ner = st.session_state['ner_bio'].tag_sentence_service(text,bio_descs) | |
| phi_ner = st.session_state['ner_phi'].tag_sentence_service(text,phi_results) | |
| combined_arr = [json.loads(bio_ner),json.loads(phi_ner)] | |
| aggregate_results = st.session_state['aggr'].fetch_all(text,combined_arr) | |
| return aggregate_results | |
| sent_arr = [ | |
| "Lou Gehrig who works for XCorp and lives in New York suffers from Parkinson's ", | |
| "Parkinson who works for XCorp and lives in New York suffers from Lou Gehrig's", | |
| "lou gehrig was diagnosed with Parkinson's ", | |
| "A eGFR below 60 indicates chronic kidney disease", | |
| "Overexpression of EGFR occurs across a wide range of different cancers", | |
| "Stanford called", | |
| "He was diagnosed with non small cell lung cancer", | |
| "Her hypophysitis secondary to ipilimumab was well managed with supplemental hormones", | |
| "I met my girl friends at the pub ", | |
| "I met my New York friends at the pub", | |
| "I met my XCorp friends at the pub", | |
| "I met my two friends at the pub", | |
| "Bio-Techne's genomic tools include advanced tissue-based in-situ hybridization assays sold under the ACD brand as well as a portfolio of assays for prostate cancer diagnosis ", | |
| "There are no treatment options specifically indicated for ACD and physicians must utilize agents approved for other dermatology conditions", "As ACD has been implicated in apoptosis-resistant glioblastoma (GBM), there is a high medical need for identifying novel ACD-inducing drugs ", | |
| "Located in the heart of Dublin , in the family home of acclaimed writer Oscar Wilde , ACD provides the perfect backdrop to inspire Irish (and Irish-at-heart) students to excel in business and the arts", | |
| "Patients treated with anticancer chemotherapy drugs ( ACD ) are vulnerable to infectious diseases due to immunosuppression and to the direct impact of ACD on their intestinal microbiota ", | |
| "In the LASOR trial , increasing daily imatinib dose from 400 to 600mg induced MMR at 12 and 24 months in 25% and 36% of the patients, respectively, who had suboptimal cytogenetic responses ", | |
| "The sky turned dark in advance of the storm that was coming from the east ", | |
| "She loves to watch Sunday afternoon football with her family ", | |
| "Paul Erdos died at 83 " | |
| ] | |
| sent_arr_masked = [ | |
| "Lou Gehrig:__entity__ who works for XCorp:__entity__ and lives in New:__entity__ York:__entity__ suffers from Parkinson's:__entity__ ", | |
| "Parkinson:__entity__ who works for XCorp:__entity__ and lives in New:__entity__ York:__entity__ suffers from Lou:__entity__ Gehrig's:__entity__", | |
| "lou:__entity__ gehrig:__entity__ was diagnosed with Parkinson's:__entity__ ", | |
| "A eGFR:__entity__ below 60 indicates chronic kidney disease", | |
| "Overexpression of EGFR:__entity__ occurs across a wide range of different cancers", | |
| "Stanford:__entity__ called", | |
| "He was diagnosed with non:__entity__ small:__entity__ cell:__entity__ lung:__entity__ cancer:__entity__", | |
| "Her hypophysitis:__entity__ secondary to ipilimumab:__entity__ was well managed with supplemental:__entity__ hormones:__entity__", | |
| "I met my girl:__entity__ friends at the pub ", | |
| "I met my New:__entity__ York:__entity__ friends at the pub", | |
| "I met my XCorp:__entity__ friends at the pub", | |
| "I met my two:__entity__ friends at the pub", | |
| "Bio-Techne's genomic tools include advanced tissue-based in-situ hybridization assays sold under the ACD:__entity__ brand as well as a portfolio of assays for prostate cancer diagnosis ", | |
| "There are no treatment options specifically indicated for ACD:__entity__ and physicians must utilize agents approved for other dermatology conditions", | |
| "As ACD:__entity__ has been implicated in apoptosis-resistant glioblastoma (GBM), there is a high medical need for identifying novel ACD-inducing drugs ", | |
| "Located in the heart of Dublin , in the family home of acclaimed writer Oscar Wilde , ACD:__entity__ provides the perfect backdrop to inspire Irish (and Irish-at-heart) students to excel in business and the arts", | |
| "Patients treated with anticancer chemotherapy drugs ( ACD:__entity__ ) are vulnerable to infectious diseases due to immunosuppression and to the direct impact of ACD on their intestinal microbiota ", | |
| "In the LASOR:__entity__ trial:__entity__ , increasing daily imatinib dose from 400 to 600mg induced MMR at 12 and 24 months in 25% and 36% of the patients, respectively, who had suboptimal cytogenetic responses ", | |
| "The sky turned dark:__entity__ in advance of the storm that was coming from the east ", | |
| "She loves to watch Sunday afternoon football:__entity__ with her family ", | |
| "Paul:__entity__ Erdos:__entity__ died at 83:__entity__ " | |
| ] | |
| def init_selectbox(): | |
| return st.selectbox( | |
| 'Choose any of the sentences in pull-down below', | |
| sent_arr,key='my_choice') | |
| def on_text_change(): | |
| text = st.session_state.my_text | |
| print("in callback: " + text) | |
| perform_inference(text) | |
| def main(): | |
| try: | |
| init_session_states() | |
| st.markdown("<h3 style='text-align: center;'>Biomedical and PHI NER ensemble</h3>", unsafe_allow_html=True) | |
| st.markdown("<h4 style='text-align: center;'>Using pretrained models with <a href='https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html'>no fine tuning</a></h4>", unsafe_allow_html=True) | |
| #st.markdown(""" | |
| #<h3 style="font-size:16px; color: #ff0000; text-align: center"><b>App under construction... (not in working condition yet)</b></h3> | |
| #""", unsafe_allow_html=True) | |
| st.markdown(""" | |
| <p style="text-align:center;"><img src="https://ajitrajasekharan.github.io/images/1.png" width="700"></p> | |
| <br/> | |
| <br/> | |
| """, unsafe_allow_html=True) | |
| st.write("This app uses 3 models. Two Pretrained Bert models (**no fine tuning**) and a POS tagger") | |
| with st.form('my_form'): | |
| selected_sentence = init_selectbox() | |
| text_input = st.text_area(label='Type any sentence below',value="") | |
| submit_button = st.form_submit_button('Submit') | |
| input_status_area = st.empty() | |
| display_area = st.empty() | |
| if submit_button: | |
| start = time.time() | |
| if (len(text_input) == 0): | |
| text_input = sent_arr_masked[sent_arr.index(selected_sentence)] | |
| input_status_area.text("Input sentence: " + text_input) | |
| results = perform_inference(text_input,display_area) | |
| display_area.empty() | |
| with display_area.container(): | |
| st.text(f"prediction took {time.time() - start:.2f}s") | |
| st.json(results) | |
| st.markdown(""" | |
| <small style="font-size:16px; color: #8f8f8f; text-align: left"><i><b>Note:</b> The example sentences in the pull-down above test biomedical & PHI entities. To see valid predictions specifically for biomedical entities <a href='https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-biomedical' target='_blank'>use this app</a>. For PHI entities only (Person,location, Organization) <a href='https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-PER-ORG-LOC' target='_blank'>use this app</a> </i></small> | |
| """, unsafe_allow_html=True) | |
| st.markdown(""" | |
| <small style="font-size:16px; color: #7f7f7f; text-align: left"><br/><br/>Models used: <br/>(1) <a href='https://huggingface.co/ajitrajasekharan/biomedical' target='_blank'>Biomedical model</a> pretrained on Pubmed,Clinical trials and BookCorpus subset.<br/>(2) Bert-base-cased (for PHI entities - Person/location/organization etc.)<br/>(3) Flair POS tagger</small> | |
| #""", unsafe_allow_html=True) | |
| st.markdown(""" | |
| <h3 style="font-size:16px; color: #9f9f9f; text-align: center"><b> <a href='https://huggingface.co/spaces/ajitrajasekharan/Qualitative-pretrained-model-evaluation' target='_blank'>App link to examine pretrained models</a> used to perform NER without fine tuning</b></h3> | |
| """, unsafe_allow_html=True) | |
| st.markdown(""" | |
| <h3 style="font-size:16px; color: #9f9f9f; text-align: center">Github <a href='http://github.com/ajitrajasekharan/unsupervised_NER' target='_blank'>link to same working code </a>(without UI) as separate microservices</h3> | |
| """, unsafe_allow_html=True) | |
| except Exception as e: | |
| print("Some error occurred in main") | |
| st.exception(e) | |
| if __name__ == "__main__": | |
| main() | |