Update src/paraphrase/Paraphrase.py

src/paraphrase/Paraphrase.py

@@ -3,35 +3,29 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch
 import src.exception.Exception.Exception as ExceptionCustom
 
-
 METHOD = "PARAPHRASE"
 
-
 tokenizer = AutoTokenizer.from_pretrained("BlackKakapo/flan-t5-base-paraphrase-ro")
 model = AutoModelForSeq2SeqLM.from_pretrained("BlackKakapo/flan-t5-base-paraphrase-ro")
 
 # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 # model.to(device)
 
-
 def paraphraseParaphraseMethod(requestValue : str):
+    exception = ""
+    result_value = ""
 
-	exception = ""
-	result_value = ""
-
+    exception = ExceptionCustom.checkForException(requestValue, METHOD)
+    if exception != "":
+        return "", exception
 
-	exception = ExceptionCustom.checkForException(requestValue, METHOD)
-	if exception != "":
-		return "", exception
+    tokenized_sent_list = sent_tokenize(requestValue)
 
-	tokenized_sent_list = sent_tokenize(requestValue)
+    for SENTENCE in tokenized_sent_list:
+        text = "paraphrase: " + SENTENCE
 
-	for SENTENCE in tokenized_sent_list:
-
-		text = "paraphrase: " + SENTENCE
-
-		encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt")
-		input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
+        encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt")
+        input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
 
         beam_outputs = model.generate(
             input_ids=input_ids, 
@@ -46,11 +40,11 @@ def paraphraseParaphraseMethod(requestValue : str):
             num_beams=1
         )
 
-		for beam_output in beam_outputs:
-		    text_para = tokenizer.decode(beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-		    
-		    if SENTENCE.lower().strip() != text_para.lower().strip():
-		    	result_value += text_para + " "
-		    	break
+        for beam_output in beam_outputs:
+            text_para = tokenizer.decode(beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+
+            if SENTENCE.lower().strip() != text_para.lower().strip():
+                result_value += text_para + " "
+                break
 
-	return result_value, ""
+    return result_value, ""