Update app.py

app.py

@@ -108,9 +108,6 @@ def get_pos_arr(input_text,display_area):
 
 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...")
@@ -122,32 +119,22 @@ def perform_inference(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)]
+  phi_ner = st.session_state['ner_phi'].tag_sentence_service(text,phi_results)
 
-  aggregate_results = st.session_state['aggr'].fetch_all(text,combined_arr)
-  return aggregate_results
+  return phi_ner
 
 
 sent_arr = [
@@ -212,19 +199,12 @@ def main():
 
     init_session_states()
 
-    st.markdown("<h3 style='text-align: center;'>NER using pretrained models with <a href='https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html'>no fine tuning</a></h3>", 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("<h3 style='text-align: center;'>NER using pretrained models with <a href='https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html'>no fine tuning</a><br/><br/></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")
+    st.write("This app uses 2 models.  Bert-base-cased(**no fine tuning**) and a POS tagger")
 
 
     with st.form('my_form'):
@@ -254,7 +234,7 @@ def main():
    # )
 
     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>
+    <small style="font-size:16px; color: #7f7f7f; text-align: left"><br/><br/>Models used: <br/>(1) Bert-base-cased (for PHI entities - Person/location/organization etc.)<br/>(2) 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>