Update app.py

app.py

@@ -26,10 +26,10 @@ from modeling import (
 from run_classifier_dataset_utils import processors, output_modes, compute_metrics
 from data_loader import load_train_data, load_train_data_kf, load_test_data, load_sentence_data
 
-#from frame_semantic_transformer import FrameSemanticTransformer
+from frame_semantic_transformer import FrameSemanticTransformer
 
-#frame_transformer = FrameSemanticTransformer()
-#frame_transformer.setup()
+frame_transformer = FrameSemanticTransformer()
+frame_transformer.setup()
 
 CONFIG_NAME = "config.json"
 WEIGHTS_NAME = "pytorch_model.bin"
@@ -112,14 +112,12 @@ def main():
 
     #tokenizer.tokenize('the debate has sharpened.')
 
-    def run_one_sentence(sentence):
-        print('sentence:', sentence)
-        sentence = re.sub(r'([.,!?()-]+)', r' \1 ', sentence)
+    def run_one_sentence(sentence, test_frame):
+        sentence = re.sub(r'([^\w\s])', r' \1 ', sentence)
         sentence = ' '.join(sentence.split())
-        print('sentence:', sentence)
-
-        #result = frame_transformer.detect_frames(sentence)
-        #print(result)
+        frame_sentence = re.sub(r'\s+([^\w\s])', r'\1', sentence)
+        print('sentence1:', sentence)
+        print('sentence2:', frame_sentence)
 
         model.eval()
         s_batch = load_sentence_data(args, sentence, ['0','1'], tokenizer, 'classification')
@@ -152,21 +150,44 @@ def main():
 
             pred = logits.detach().cpu().numpy()
             pred = np.argmax(pred, axis=1)
-            pred_list = [None for _ in range(len(sentence.split()))]
+            sentence_list = sentence.split()
+            pred_list = [None for _ in range(len(sentence_list))]
             for i,n in enumerate(idx):
                 pred_list[n] = 'M' if pred[i] == 1 else None
-            print(len(pred_list), pred_list)
+            frame_list = []
+            if test_frame:
+                sentence_frame = frame_transformer.detect_frames(frame_sentence)
+                for i,n in enumerate(idx):
+                    if pred[i] == 1:
+                        word_loc = frame_sentence.find(sentence_list[n])
+                        word_frame = frame_transformer.detect_frames(sentence_list[n])
+                        if word_loc in sentence_frame.trigger_locations and 0 in word_frame.trigger_locations:
+                            frame_list = frame_list + [
+                                ('['+sentence_list[n]+']', None), 
+                                (sentence_frame.frames[sentence_frame.trigger_locations.index(word_loc)].name, 'Contextual'),
+                                (word_frame.frames[0].name, 'Literal'),
+                                (' \n', None)
+                            ]
+                        else:
+                            frame_list = frame_list + [
+                                ('['+sentence_list[n]+']', None), 
+                            ]
+            
             label_list = [(w, p) for w,p in zip(sentence.split(), pred_list)]
-            print(label_list)
-            return label_list, result
-    #import pdb; pdb.set_trace()
+            #print(label_list)
+            return label_list, frame_list
 
     demo = gr.Interface(
         run_one_sentence,
+        [
         gr.Textbox(placeholder="Enter sentence here..."),
-        ['highlight', 'json'], 
+        gr.Checkbox(label="Test frame", value=False),
+        ],
+        [gr.HighlightedText(label='Metaphor Detection'), gr.HighlightedText(label='Frame Extraction')], 
         examples=[
             ['while new departments are born and others extended .'],
+            ['Dimples played in his cheeks .'],
+            ['For a whole week they had worked closely together , sharing flasks of coffee and packets of cigarettes and Paula had grown to like the pixieish little man who by his very nature offered her no challenge — and no threat .'],
             ['The sounds are the same as those of daylight , yet somehow the night magnifies and sharpens the creak of a yielding block , the sigh of air over a shroud , the stretching of a sail , the hiss of water sliding sleek against the hull , the curl of a quarter-wave falling away , and the thump as a wave strikes the cutwater to be sheared into two bright slices of whiteness .'],
             ['and finally, the debate has sharpened.']
         ]