Update classify_abs.py

classify_abs.py

@@ -291,6 +291,7 @@ def streamlit_getAbs(searchterm_list:Union[List[str],List[int],str], maxResults:
             searchterm_list = list(searchterm_list)
     #maxResults is multiplied by a little bit because sometimes the results returned is more than maxResults
     percent_by_step = 1/(maxResults*1.05) 
+    API_Loading = st.spinner("Gathering PubMed IDs...")
     PMIDs_bar = st.progress(0)
     for dz in searchterm_list:
         term = ''
@@ -325,36 +326,36 @@ def streamlit_getAbs(searchterm_list:Union[List[str],List[int],str], maxResults:
                     PMIDs_bar.progress(min(round(len(pmids)*percent_by_step,1),1.0))
     PMIDs_bar.empty()
     
-    with st.success('Found '+str(len(pmids))+' PMIDs. Gathering Abstracts and Filtering...'):
-        abstracts_bar = st.progress(0)
-        percent_by_step = 1/(maxResults)
-        if filtering !='none' or filtering !='strict':
-            filter_terms = set(searchterm_list).union(set(str(re.sub(',','',' '.join(searchterm_list))).split()).difference(STOPWORDS))
-
-        for pmid in pmids:
-            abstract = PMID_getAb(pmid)
-            if len(abstract)>5:
-                #do filtering here
-                if filtering == 'strict':
-                    uncased_ab = abstract.lower()
-                    for term in searchterm_list:
-                        if term.lower() in uncased_ab:
-                            pmid_abs[pmid] = abstract
-                            abstracts_bar.progress(min(round(len(pmid_abs)*percent_by_step,1),1.0))
-                            break
-                elif filtering =='none':
-                    pmid_abs[pmid] = abstract
-                    abstracts_bar.progress(min(round(len(pmid_abs)*percent_by_step,1),1.0))
-                
-                #Default filtering is 'lenient'.
-                else:
-                    #Else and if are separated for readability and to better understand logical flow.
-                    if set(filter_terms).intersection(set(word_tokenize(abstract))):
+    API_Loading = st.spinner("Found "+str(len(pmids))+" PMIDs. Gathering Abstracts and Filtering...")
+    abstracts_bar = st.progress(0)
+    percent_by_step = 1/(maxResults)
+    if filtering !='none' or filtering !='strict':
+        filter_terms = set(searchterm_list).union(set(str(re.sub(',','',' '.join(searchterm_list))).split()).difference(STOPWORDS))
+
+    for pmid in pmids:
+        abstract = PMID_getAb(pmid)
+        if len(abstract)>5:
+            #do filtering here
+            if filtering == 'strict':
+                uncased_ab = abstract.lower()
+                for term in searchterm_list:
+                    if term.lower() in uncased_ab:
                         pmid_abs[pmid] = abstract
                         abstracts_bar.progress(min(round(len(pmid_abs)*percent_by_step,1),1.0))
-        abstracts_bar.empty()
-    
-    st.success('Found '+str(len(pmids))+' PMIDs. Gathered '+str(len(pmid_abs))+' Relevant Abstracts.')
+                        break
+            elif filtering =='none':
+                pmid_abs[pmid] = abstract
+                abstracts_bar.progress(min(round(len(pmid_abs)*percent_by_step,1),1.0))
+            
+            #Default filtering is 'lenient'.
+            else:
+                #Else and if are separated for readability and to better understand logical flow.
+                if set(filter_terms).intersection(set(word_tokenize(abstract))):
+                    pmid_abs[pmid] = abstract
+                    abstracts_bar.progress(min(round(len(pmid_abs)*percent_by_step,1),1.0))
+    abstracts_bar.empty()
+    API_Loading.empty()
+    st.success('Found '+str(len(pmids))+' PMIDs. Gathered '+str(len(pmid_abs))+' Relevant Abstracts. Classifying and extracting epidemiology information...')
     
     return pmid_abs, (len(pmids),len(pmid_abs))