Upload app.py

app.py

@@ -0,0 +1,154 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov 22 09:54:41 2022
+
+@author: luol2
+"""
+
+
+
+import streamlit as st
+import argparse
+from src.nn_model import bioTag_CNN,bioTag_BERT,bioTag_Bioformer
+from src.dic_ner import dic_ont
+from src.tagging_text import bioTag
+import os
+import time
+import json
+import sys
+
+
+st.set_page_config(
+    page_title="PhenoTagger",
+    page_icon=":shark:",
+    #  layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items={
+        'Get Help': 'https://www.extremelycoolapp.com/help',
+        'Report a bug': "https://www.extremelycoolapp.com/bug",
+        'About': "# This is a header. This is an *extremely* cool app!"
+    }
+)
+st.title('PhenoTagger Demo')
+
+
+
+
+    
+
+
+# with st.spinner('Model is being loaded..'):
+  
+#   print('load model done!')
+ 
+
+
+    
+with st.form(key="my_form"):
+        
+        @st.cache(allow_output_mutation=True)
+        def load_model():
+            ontfiles={'dic_file':'./dict_new/noabb_lemma.dic',
+                      'word_hpo_file':'./dict_new/word_id_map.json',
+                      'hpo_word_file':'./dict_new/id_word_map.json'}
+            
+            # if para_set['model_type']=='cnn':
+            #     vocabfiles={'w2vfile':'../vocab/bio_embedding_intrinsic.d200',   
+            #                 'charfile':'../vocab/char.vocab',
+            #                 'labelfile':'../dict_new/lable.vocab',
+            #                 'posfile':'../vocab/pos.vocab'}
+            #     modelfile='../models/cnn_p5n5_b128_95_hponew1.h5'
+            
+            # elif para_set['model_type']=='bioformer':
+            vocabfiles={'labelfile':'./dict_new/lable.vocab',
+                        'config_path':'./vocab/bioformer-cased-v1.0/bert_config.json',
+                        'checkpoint_path':'./vocab/bioformer-cased-v1.0/bioformer-cased-v1.0-model.ckpt-2000000',
+                        'vocab_path':'./vocab/bioformer-cased-v1.0/vocab.txt'}
+            modelfile='./vocab/bioformer_p5n5_b64_1e-5_95_hponew3.h5'
+            # else:
+            #     print('Model type is wrong, please select cnn or bioformer.')
+            #     sys.exit()
+            
+        
+            biotag_dic=dic_ont(ontfiles)    
+        
+            # if para_set['model_type']=='cnn':
+            #     nn_model=bioTag_CNN(vocabfiles)
+            #     nn_model.load_model(modelfile)
+            # elif para_set['model_type']=='bioformer':
+            nn_model=bioTag_Bioformer(vocabfiles)
+            session=nn_model.load_model(modelfile)
+            test_tag='1232'
+            return nn_model,biotag_dic,test_tag,session
+    
+    
+        #hyper-parameter
+        st.sidebar.header("Hyperparameter Settings")
+        sbform = st.sidebar.form("Hyper-paramiters")
+        # para_model=sbform.selectbox('Model', ['cnn', 'bioformer'])
+        para_overlap=sbform.selectbox('Return overlapping concepts', ['True', 'False'])
+        para_abbr=sbform.selectbox('Identify abbreviations', ['True', 'False'])
+        para_threshold = sbform.slider('Threshold:', min_value=0.5, max_value=0.95, value=0.95, step=0.05)
+        sbform.form_submit_button("Setting")
+        
+        st.write('parameters:', para_overlap,para_abbr,para_threshold)
+        nn_model,biotag_dic,test_tag,session=load_model()
+        
+        
+        input_text = st.text_area(
+            "Paste your text below (max 500 words)",
+            height=510,
+        )
+
+        MAX_WORDS = 500
+        import re
+        res = len(re.findall(r"\w+", input_text))
+        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! 😊"
+            )
+
+            input_text = input_text[:MAX_WORDS]
+
+        submit_button = st.form_submit_button(label="✨ Get me the data!")
+        
+        if para_overlap=='True':
+            para_overlap=True
+        else:
+            para_overlap=False
+        if para_abbr=='True':
+            para_abbr=True
+        else:
+            para_abbr=False
+        para_set={
+                  #model_type':para_model, # cnn or bioformer
+                  'onlyLongest':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
+                  }
+    
+    
+
+if not submit_button:
+    st.stop()
+        
+
+st.markdown(f"""**Results:**\n""")
+# print('dic...........:',biotag_dic.keys())
+print('........:',test_tag)
+print('........!!!!!!:',input_text)
+print('...input:',input_text)
+tag_result=bioTag(session,input_text,biotag_dic,nn_model,onlyLongest=para_set['onlyLongest'], abbrRecog=para_set['abbrRecog'],Threshold=para_set['ML_Threshold'])
+for ele in tag_result:
+    start = ele[0]
+    last = ele[1]
+    mention = input_text[int(ele[0]):int(ele[1])]
+    type='Phenotype'
+    id=ele[2]
+    score=ele[3]
+    output=start+"\t"+last+"\t"+mention+"\t"+id+"\n"
+    st.info(output)
+