adding time info

app.py

@@ -6,6 +6,7 @@ import pickle
 import nltk
 nltk.download('punkt') # tokenizer
 nltk.download('averaged_perceptron_tagger') # postagger
+import time
 
 from input_format import *
 from score import *
@@ -28,7 +29,8 @@ def get_similar_paper(
     author_id_input, 
     num_papers_show=10
 ):
-    print('retrieving similar papers')
+    print('retrieving similar papers...')
+    start = time.time()
     input_sentences = sent_tokenize(abstract_text_input)
     
     # TODO handle pdf file input
@@ -41,7 +43,7 @@ def get_similar_paper(
         name, papers = get_text_from_author_id(author_id_input)
     
     # Compute Doc-level affinity scores for the Papers 
-    print('computing scores')
+    print('computing scores...')
     titles, abstracts, doc_scores = compute_document_score(
         doc_model, 
         tokenizer,
@@ -63,7 +65,8 @@ def get_similar_paper(
     doc_scores = doc_scores[:num_papers_show]
     
     display_title = ['[ %0.3f ] %s'%(s, t) for t, s in zip(titles, doc_scores)]
-    print('retrieval done')
+    end = time.time()
+    print('retrieval complete in [%0.2f] seconds'%(end - start))
     
     return (
         gr.update(choices=display_title, interactive=True, visible=True), # set of papers
@@ -79,7 +82,8 @@ def get_highlights(
     abstract,
     K=2
 ):
-    print('obtaining highlights')
+    print('obtaining highlights..')
+    start = time.time()
     # Compute sent-level and phrase-level affinity scores for each papers
     sent_ids, sent_scores, info = get_highlight_info(
         sent_model, 
@@ -105,7 +109,8 @@ def get_highlights(
         'highlight': word_scores
     }
     pickle.dump(tmp, open('highlight_info.pkl', 'wb'))
-    print('done')
+    end = time.time()
+    print('done in [%0.2f] seconds'%(end - start))
     
     # update the visibility of radio choices
     return gr.update(visible=True)