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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on May 15 16:21:25 2023 | |
| @author: luol2 | |
| """ | |
| import streamlit as st | |
| from src.nn_model import bioTag_CNN,bioTag_BERT | |
| from src.dic_ner import dic_ont | |
| from src.tagging_text import bioTag | |
| import os | |
| import json | |
| from pandas import DataFrame | |
| import nltk | |
| nltk.download('punkt') | |
| nltk.download('averaged_perceptron_tagger') | |
| nltk.download('wordnet') | |
| st.set_page_config( | |
| page_title="PhenoTagger_CL", | |
| page_icon="🎈", | |
| layout="wide", | |
| menu_items={ | |
| 'Get Help': 'https://www.ncbi.nlm.nih.gov/research/bionlp/', | |
| 'About': "PhenoTagger-CL" | |
| } | |
| ) | |
| # def _max_width_(): | |
| # max_width_str = f"max-width: 2400px;" | |
| # st.markdown( | |
| # f""" | |
| # <style> | |
| # .reportview-container .main .block-container{{ | |
| # {max_width_str} | |
| # }} | |
| # </style> | |
| # """, | |
| # unsafe_allow_html=True, | |
| # ) | |
| # _max_width_() | |
| # c30, c31, c32 = st.columns([2.5, 1, 3]) | |
| # with c30: | |
| # # st.image("logo.png", width=400) | |
| st.title("👨⚕️ PhenoTagger_CL Demo") | |
| with st.expander("ℹ️ - About this app", expanded=True): | |
| st.write( | |
| """ | |
| - This app is an easy-to-use interface built in Streamlit for [PhenoTagger](https://github.com/ncbi-nlp/PhenoTagger) library! | |
| - PhenoTagger is a hybrid method that combines dictionary and deep learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. Please refer to [our paper](https://doi.org/10.1093/bioinformatics/btab019) for more details. | |
| - Contact: [NLM/NCBI BioNLP Research Group](https://www.ncbi.nlm.nih.gov/research/bionlp/) | |
| """ | |
| ) | |
| st.markdown("") | |
| st.markdown("") | |
| st.markdown("## 📌 Paste document ") | |
| with st.form(key="my_form"): | |
| ce, c1, ce, c2, c3 = st.columns([0.07, 1, 0.07, 4, 0.07]) | |
| with c1: | |
| ModelType = st.radio( | |
| "Choose your Ontology", | |
| ['CL'], #["HPO(Default)", "UBERON"], | |
| #help="Bioformer is more precise, CNN is more efficient", | |
| ) | |
| if ModelType == "CL": | |
| # kw_model = KeyBERT(model=roberta) | |
| #@st.cache(allow_output_mutation=True) | |
| def load_model(): | |
| ontfiles={'dic_file':'./dict_cl/noabb_lemma.dic', | |
| 'word_id_file':'./dict_cl/word_id_map.json', | |
| 'id_word_file':'./dict_cl/id_word_map.json'} | |
| vocabfiles={'labelfile':'./dict_cl/lable.vocab', | |
| 'checkpoint_path':'./models/bioformer-cased-v1.0/', | |
| 'lowercase':False} | |
| modelfile='./models/bioformer-CL.h5' | |
| biotag_dic=dic_ont(ontfiles) | |
| nn_model=bioTag_BERT(vocabfiles) | |
| nn_model.load_model(modelfile) | |
| return nn_model,biotag_dic | |
| nn_model,biotag_dic = load_model() | |
| else: | |
| pass | |
| ''' | |
| #@st.cache(allow_output_mutation=True) | |
| def load_model(): | |
| ontfiles={'dic_file':'./dict_uberon/noabb_lemma.dic', | |
| 'word_id_file':'./dict_uberon/word_id_map.json', | |
| 'id_word_file':'./dict_uberon/id_word_map.json'} | |
| vocabfiles={'labelfile':'./dict_uberon/lable.vocab', | |
| 'checkpoint_path':'./models_v1.2/bioformer-cased-v1.0/', | |
| 'lowercase':False} | |
| modelfile='./models_v1.2/bioformer-UBERON.h5' | |
| biotag_dic=dic_ont(ontfiles) | |
| nn_model=bioTag_BERT(vocabfiles) | |
| nn_model.load_model(modelfile) | |
| return nn_model,biotag_dic | |
| nn_model,biotag_dic = load_model() | |
| ''' | |
| para_overlap = st.checkbox( | |
| "Overlap concept", | |
| value=False, | |
| help="Tick this box to identify overlapping concepts", | |
| ) | |
| para_abbr = st.checkbox( | |
| "Abbreviaitons", | |
| value=True, | |
| help="Tick this box to identify abbreviations", | |
| ) | |
| para_threshold = st.slider( | |
| "Threshold", | |
| min_value=0.5, | |
| max_value=1.0, | |
| value=0.95, | |
| step=0.05, | |
| help="Retrun the preditions which socre over the threshold.", | |
| ) | |
| with c2: | |
| doc = st.text_area( | |
| "Paste your text below", | |
| value = 'Uniparental disomy has recently been recognized to cause human disorders, including Prader-Willi syndrome (PWS). We describe a particularly instructive case which raises important issues concerning the mechanisms producing uniparental disomy and whose evaluation provides evidence that trisomy may precede uniparental disomy in a fetus. Chorionic villus sampling performed for advanced maternal age revealed trisomy 15 in all direct and cultured cells, though the fetus appeared normal. Chromosome analysis of amniocytes obtained at 15 wk was normal in over 100 cells studied. The child was hypotonic at birth, and high-resolution banding failed to reveal the deletion of 15q11-13, a deletion which is found in 50%-70% of patients with PWS. Over time, typical features of PWS developed. Molecular genetic analysis using probes for chromosome 15 revealed maternal disomy. Maternal nondisjunction with fertilization of a disomic egg by a normal sperm, followed by loss of the paternal 15, is a likely cause of confined placental mosaicism and uniparental disomy in this case of PWS, and advanced maternal age may be a predisposing factor.', | |
| height=400, | |
| ) | |
| # MAX_WORDS = 500 | |
| # import re | |
| # res = len(re.findall(r"\w+", doc)) | |
| # if res > MAX_WORDS: | |
| # st.warning( | |
| # "⚠️ Your text contains " | |
| # + str(res) | |
| # + " words." | |
| # + " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊" | |
| # ) | |
| # doc = doc[:MAX_WORDS] | |
| submit_button = st.form_submit_button(label="✨ Submit!") | |
| if not submit_button: | |
| st.stop() | |
| #st.write(para_overlap,para_abbr,para_threshold) | |
| para_set={ | |
| #model_type':para_model, # cnn or bioformer | |
| 'onlyLongest': not para_overlap, # False: return overlap concepts, True only longgest | |
| 'abbrRecog':para_abbr,# False: don't identify abbr, True: identify abbr | |
| 'ML_Threshold':para_threshold,# the Threshold of deep learning model | |
| } | |
| st.markdown("") | |
| st.markdown("## 💡 Tagging results:") | |
| with st.spinner('Wait for tagging...'): | |
| tag_result=bioTag(doc,biotag_dic,nn_model,onlyLongest=para_set['onlyLongest'], abbrRecog=para_set['abbrRecog'],Threshold=para_set['ML_Threshold']) | |
| st.markdown('<font style="color: rgb(128, 128, 128);">Move the mouse🖱️ over the entity to display the HPO id.</font>', unsafe_allow_html=True) | |
| # print('dic...........:',biotag_dic.keys()) | |
| # st.write('parameters:', para_overlap,para_abbr,para_threshold) | |
| html_results='' | |
| text_results=doc+'\n' | |
| entity_end=0 | |
| hpoid_count={} | |
| if len(tag_result)>=0: | |
| for ele in tag_result: | |
| entity_start=int(ele[0]) | |
| html_results+=doc[entity_end:entity_start] | |
| entity_end=int(ele[1]) | |
| entity_id=ele[2] | |
| entity_score=ele[3] | |
| text_results+=ele[0]+'\t'+ele[1]+'\t'+doc[entity_start:entity_end]+'\t'+ele[2]+'\t'+format(float(ele[3]),'.2f')+'\n' | |
| if entity_id not in hpoid_count.keys(): | |
| hpoid_count[entity_id]=1 | |
| else: | |
| hpoid_count[entity_id]+=1 | |
| html_results+='<font style="background-color: rgb(255, 204, 0)'+';" title="'+entity_id+'">'+doc[entity_start:entity_end]+'</font>' | |
| html_results+=doc[entity_end:] | |
| else: | |
| html_results=doc | |
| st.markdown('<table border="1"><tr><td>'+html_results+'</td></tr></table>', unsafe_allow_html=True) | |
| #table | |
| data_entity=[] | |
| for ele in hpoid_count.keys(): | |
| segs=ele.split(';') | |
| term_name='' | |
| for seg in segs: | |
| term_name+=biotag_dic.id_word[seg][0]+';' | |
| temp=[ele,term_name,hpoid_count[ele]] #hpoid, term name, count | |
| data_entity.append(temp) | |
| st.markdown("") | |
| st.markdown("") | |
| # st.markdown("## Table output:") | |
| # cs, c1, c2, c3, cLast = st.columns([2, 1.5, 1.5, 1.5, 2]) | |
| # with c1: | |
| # CSVButton2 = download_button(keywords, "Data.csv", "📥 Download (.csv)") | |
| # with c2: | |
| # CSVButton2 = download_button(keywords, "Data.txt", "📥 Download (.txt)") | |
| # with c3: | |
| # CSVButton2 = download_button(keywords, "Data.json", "📥 Download (.json)") | |
| # st.header("") | |
| df = ( | |
| DataFrame(data_entity, columns=["Ontology_id", "Term name","Frequency"]) | |
| .sort_values(by="Frequency", ascending=False) | |
| .reset_index(drop=True) | |
| ) | |
| df.index += 1 | |
| c1, c2, c3 = st.columns([1, 4, 1]) | |
| # format_dictionary = { | |
| # "Relevancy": "{:.1%}", | |
| # } | |
| # df = df.format(format_dictionary) | |
| with c2: | |
| st.table(df) | |
| c1, c2, c3 = st.columns([1, 1, 1]) | |
| with c2: | |
| st.download_button('Download annotations', text_results) | |