Update app.py

app.py

@@ -41,24 +41,10 @@ from transformers_interpret import SequenceClassificationExplainer
 cls_explainer = SequenceClassificationExplainer(
     model,
     tokenizer)
-    
-# load the model from disk
-#filename = 'resil_lstm_model.sav'
-#lmodel = pickle.load(open(filename, 'rb'))
-
-# load the model from disk
-#filename = 'tokenizer.pickle'
-#tok = pickle.load(open(filename, 'rb'))
 
 def process_final_text(text):
     X_test = str(text).lower()
-    #l = []
-    #l.append(X_test)
-    #test_sequences = tok.texts_to_sequences(l)
-    #test_sequences_matrix = sequence.pad_sequences(test_sequences,maxlen=max_len)
-    #lstm_prob = lmodel.predict(test_sequences_matrix.tolist()).flatten()
-    #lstm_pred = np.where(lstm_prob>=0.5,1,0)
-    
+
     encoded_input = tokenizer(X_test, return_tensors='pt')
     output = model(**encoded_input)
     scores = output[0][0].detach().numpy()
@@ -114,11 +100,6 @@ def process_final_text(text):
     
     word_attributions = [(letter[i], score[i]) for i in range(0, len(letter))]
 
-    # # Paraphraser:
-    # batch = para_tokenizer(X_test, return_tensors='pt')
-    # generated_ids = para_model.generate(batch['input_ids'])
-    # para_list = para_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
-
     return {"Persuasive": float(scores.numpy()[1]), "Non-Persuasive": float(scores.numpy()[0])},keywords,NER,word_attributions
     
 def main(prob1):