wzkariampuzha commited on
Commit
d647316
1 Parent(s): 24c9761

Update extract_abs.py

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  1. extract_abs.py +5 -5
extract_abs.py CHANGED
@@ -279,9 +279,9 @@ def search_term_extraction(search_term:Union[int,str], maxResults:int, filtering
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  #Returns a Pandas dataframe
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  def streamlit_extraction(search_term:Union[int,str], maxResults:int, filtering:str, #for abstract search
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- NER_pipeline:Any, entity_classes:Union[Set[str],List[str]], #for biobert extraction
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- extract_diseases:bool, GARD_dict:Dict[str,str], max_length:int, #for disease extraction
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- classify_model_vars:Tuple[Any,Any,Any,Any,Any]) -> Any: #for classification
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  #Format of Output
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  ordered_labels = order_labels(entity_classes)
@@ -301,7 +301,7 @@ def streamlit_extraction(search_term:Union[int,str], maxResults:int, filtering:s
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  #Gather title+abstracts into a dictionary {pmid:abstract}
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  pmid_abs = classify_abs.search_getAbs(search_term_list, maxResults, filtering)
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- st.write("GATHERED " +str(len(pmid_abs))+" PubMed IDs.")
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  i = 0
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  my_bar = st.progress(i)
@@ -352,7 +352,7 @@ def API_extraction(search_term:Union[int,str], maxResults:int, filtering:str, #f
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  model_outputs = [NER_pipeline(sent) for sent in sentences]
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  extraction = parse_info(sentences, model_outputs, entity_classes, extract_diseases, GARD_dict, max_length)
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  if extraction:
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- extraction.update({'PMID':pmid, 'ABSTRACT':abstract, 'EPI_PROB':epi_prob, 'IsEpi':isEpi})
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  extraction = OrderedDict([(term, extraction[term]) for term in json_output])
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  results['entries'].append(extraction)
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  #Returns a Pandas dataframe
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  def streamlit_extraction(search_term:Union[int,str], maxResults:int, filtering:str, #for abstract search
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+ NER_pipeline:Any, entity_classes:Union[Set[str],List[str]], #for biobert extraction
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+ extract_diseases:bool, GARD_dict:Dict[str,str], max_length:int, #for disease extraction
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+ classify_model_vars:Tuple[Any,Any,Any,Any,Any]) -> Any: #for classification
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  #Format of Output
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  ordered_labels = order_labels(entity_classes)
 
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  #Gather title+abstracts into a dictionary {pmid:abstract}
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  pmid_abs = classify_abs.search_getAbs(search_term_list, maxResults, filtering)
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+ st.write("Gathered " +str(len(pmid_abs))+" PubMed IDs. Classifying and extracting epidemiology information...")
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  i = 0
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  my_bar = st.progress(i)
 
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  model_outputs = [NER_pipeline(sent) for sent in sentences]
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  extraction = parse_info(sentences, model_outputs, entity_classes, extract_diseases, GARD_dict, max_length)
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  if extraction:
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+ extraction.update({'PMID':pmid, 'ABSTRACT':abstract, 'EPI_PROB':epi_prob})
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  extraction = OrderedDict([(term, extraction[term]) for term in json_output])
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  results['entries'].append(extraction)
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