Update app.py

app.py

@@ -2,6 +2,8 @@
 #Import Libraries
 import spacy
 nlp = spacy.load("en_core_web_sm")
+from transformer_srl import dataset_readers, models, predictors
+predictor = predictors.SrlTransformersPredictor.from_path("srl_bert_base_conll2012.tar.gz","transformer_srl")
 import re
 import nltk
 nltk.download('stopwords')
@@ -59,9 +61,30 @@ def semantic_graph(text_input, srl_model, flow, predication, action, setting, ca
     srl_df = pd.DataFrame(columns=columns)
     pred_id = 1
 
-    srl_model = 'VerbAtlas'
+    #srl_model = 'VerbAtlas'
 #Branch for SRL models:
-    if srl_model == 'VerbAtlas':
+    if srl_model == 'Transformer_SRL':
+        for index, row in sentence_df.iterrows():
+            srl = predictor.predict((row['content']))
+            for each_verb in srl['verbs']:
+                srl_elements = re.findall(r'\[.*?\]', each_verb['description'])
+                instance = {}
+                instance['sentence_id'] = row['sentence_id']
+                instance['frame'] = each_verb['frame']
+                instance['lemma'] = each_verb['lemma']
+                instance['pred_id'] = pred_id
+                any_element = False
+                for element in srl_elements:
+                    if element.split(':')[0][1:] != each_verb['frame']:
+                        any_element = True
+                        srl_content = element[1:-1].split(':')
+                        # print(srl_content)
+                        instance[srl_content[0].strip()] = srl_content[1].strip()
+                if any_element == True:
+                    srl_df = srl_df.append(instance, ignore_index=True)
+                    pred_id += 1
+        srl_df['Pred'] = srl_df['lemma']
+    elif srl_model == 'VerbAtlas':
         URL = "https://verbatlas.org/api/model"
         for index, row in sentence_df.iterrows():
             try: