Update app.py

app.py

@@ -162,14 +162,17 @@ def add_new_eval(
                     if id_to_eval == intervention_id:
                         references = gold_dataset['cqs']
                         reference_set = [row['cq'] for row in references[indx]]
+                        print(reference_set, flush=True)
                         for cq in line['cqs']:
                             # TODO: compare to each reference and get a value
                             cq_text = cq['cq']
+                            print(cq_text, flush=True)
 
                             #if args.metric == 'similarity':
                             sentence_embedding = similarity_model.encode(cq_text)
                             reference_embedding = similarity_model.encode(reference_set)
                             sims = similarity_model.similarity(sentence_embedding, reference_embedding).tolist()[0]
+                            print(sims, flush=True)
 
                             winner = np.argmax(sims)
                             # make sure the similarity of the winning reference sentence is at least 0.65