diff options
| -rwxr-xr-x | server.py | 9 |
1 files changed, 5 insertions, 4 deletions
@@ -616,7 +616,7 @@ def sentences(): out_pos = "" out_neg = "" num_abstract = 0 - stress_cellular = "<br><br><br>"+"<b>Sentence(s) describing celluar stress (classified using a deep learning model):</b><hr>" + stress_cellular = "<br><br><br>"+"</ol><b>Sentence(s) describing celluar stress (classified using a deep learning model):</b><hr><ol>" stress_systemic = "<b>Sentence(s) describing systemic stress (classified using a deep learning model):</b><hr>" with open(tf_name, "r") as df: all_sents=df.read() @@ -629,12 +629,13 @@ def sentences(): if(pmid+cat0 not in pmid_list): pmid_list.append(pmid+cat0) if(cat0=='stress'): - out_pred = "<li> "+ text + " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid +"\" target=_new>PMID:"+pmid+"<br></a>" out4 = predict_sent(text) if(out4 == 'pos'): - out_pos += out_pred + out_pred_pos = "<li> "+ text + " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid +"\" target=_new>PMID:"+pmid+"<br></a>" + out_pos += out_pred_pos else: - out_neg += out_pred + out_pred_neg = "<li>"+ text + " <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=" + pmid +"\" target=_new>PMID:"+pmid+"<br></a>" + out_neg += out_pred_neg out1="<h3>"+gene0 + " and " + cat0 + "</h3>\n" if len(pmid_list)>1: out2 = str(num_abstract) + ' sentences in ' + str(len(pmid_list)) + ' studies' + "<br><br>" |