updates

utils/interp_space_utils.py

@@ -137,8 +137,8 @@ def instance_to_df(instance, predicted_author=None, ground_truth_author=None):
                     
     ])
 
-    if type(instance['Q_fullText']) == list:
-        task_authos_df = task_authos_df.groupby('authorID').agg({'fullText': lambda x: list(x)}).reset_index()
+    # if type(instance['Q_fullText']) == list:
+    #     task_authos_df = task_authos_df.groupby('authorID').agg({'fullText': lambda x: list(x)}).reset_index()
 
     return task_authos_df
 

utils/ui.py

@@ -135,6 +135,9 @@ def update_task_display(mode, iid, instances, background_df, mystery_file, cand1
     task_authors_df['g2v_vector'] = task_authors_g2v
     print(f"Gram2Vec feature generation complete")
 
+    # Computing predicted author by checking pairwise cosine similarity over luar embeddings
+    col_name = f'{model_name.split("/")[-1]}_style_embedding'
+    predicted_author = compute_predicted_author(task_authors_df, col_name) 
     
     #generating html for the task
     header_html, mystery_html, candidate_htmls = task_HTML(mystery_txt, candidate_texts, predicted_author, ground_truth_author)