update layout

app.py

@@ -20,7 +20,7 @@ config = {
     "logic_lambda": 0.5,
     "prior": "random",
     "mask_rate": 0.0,
-    "cand_k": 3,
+    "cand_k": 1,
     "max_seq1_length": 256,
     "max_seq2_length": 128,
     "max_num_questions": 8,
@@ -35,9 +35,6 @@ os.system('rm -r LOREN/results/')
 os.system('rm -r LOREN/models/')
 os.system('mv LOREN/* ./')
 
-# os.makedirs('data/', exist_ok=True)
-# os.system('wget -O data/fever.db https://s3-eu-west-1.amazonaws.com/fever.public/wiki_index/fever.db')
-
 model_dir = snapshot_download('Jiangjie/loren')
 config['fc_dir'] = os.path.join(model_dir, 'fact_checking/roberta-large/')
 config['mrc_dir'] = os.path.join(model_dir, 'mrc_seq2seq/bart-base/')
@@ -49,15 +46,6 @@ from src.loren import Loren
 
 loren = Loren(config)
 try:
-    # js = {
-    #     'id': 0,
-    #     'evidence': ['EVIDENCE1', 'EVIDENCE2'],
-    #     'question': ['QUESTION1', 'QUESTION2'],
-    #     'claim_phrase': ['CLAIMPHRASE1', 'CLAIMPHRASE2'],
-    #     'local_premise': [['E1 ' * 100, 'E1' * 100, 'E1' * 10], ['E2', 'E2', 'E2']],
-    #     'phrase_veracity': [[0.1, 0.5, 0.4], [0.1, 0.7, 0.2]],
-    #     'claim_veracity': 'SUPPORT'
-    # }
     js = loren.check('Donald Trump won the 2020 U.S. presidential election.')
 except Exception as e:
     raise ValueError(e)
@@ -65,31 +53,38 @@ except Exception as e:
 
 def gradio_formatter(js, output_type):
     if output_type == 'e':
-        data = {'Evidence': js['evidence']}
+        data = {'Evidence': [f'* {x}' for x in js['evidence']]}
     elif output_type == 'z':
         data = {
-            'Claim Phrase': js['claim_phrases'],
-            'Local Premise': js['local_premises'],
-            'p_SUP': [round(x[2], 4) for x in js['phrase_veracity']],
-            'p_REF': [round(x[0], 4) for x in js['phrase_veracity']],
-            'p_NEI': [round(x[1], 4) for x in js['phrase_veracity']],
+            'Claim Phrase': [f'* {x}' for x in js['claim_phrases']],
+            'Local Premise': [f'* {x}' for x in js['local_premises']],
+            'p_SUP': ['%.4f' % x[2] for x in js['phrase_veracity']],
+            'p_REF': ['%.4f' % x[0] for x in js['phrase_veracity']],
+            'p_NEI': ['%.4f' % x[1] for x in js['phrase_veracity']],
         }
     else:
         raise NotImplementedError
     data = pd.DataFrame(data)
-    pt = PrettyTable(field_names=list(data.columns))
+    pt = PrettyTable(field_names=list(data.columns), 
+        align='l', border=True, hrules=1, vrules=1)
     for v in data.values:
         pt.add_row(v)
-
     html = pt.get_html_string(attributes={
-        'style': 'border-width: 1px; border-collapse: collapse',
-        'align': 'left',
-        'border': '1'
+        'style': 'border-width: 2px; bordercolor: black'
     }, format=True)
     return html
 
 
 def run(claim):
+    # js = {
+    #     'id': 0,
+    #     'evidence': ['EVIDENCE1', 'EVIDENCE2'],
+    #     'question': ['QUESTION1', 'QUESTION2'],
+    #     'claim_phrases': ['CLAIMPHRASE1', 'CLAIMPHRASE2'],
+    #     'local_premises': [['E1 ' * 100, 'E1 ' * 100, 'E1 ' * 10], ['E2', 'E2', 'E2']],
+    #     'phrase_veracity': [[0.1, 0.5, 0.4], [0.1, 0.7, 0.2]],
+    #     'claim_veracity': 'SUPPORT'
+    # }
     js = loren.check(claim)
     ev_html = gradio_formatter(js, 'e')
     z_html = gradio_formatter(js, 'z')
@@ -110,7 +105,7 @@ iface = gr.Interface(
               'The Cry of the Owl is based on a book by an American.',
               'Smriti Mandhana is an Indian woman.'],
     title="LOREN",
-    layout='horizontal',
+    layout='vertical',
     description="LOREN is an interpretable Fact Verification model against Wikipedia. "
                 "This is a demo system for \"LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification\". "
                 "See the paper for technical details. You can add FLAG on the bottom to record interesting or bad cases! \n"