Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
-# import torch
-# from transformers import PegasusForConditionalGeneration, PegasusTokenizer
-# from sentence_splitter import SentenceSplitter, split_text_into_sentences
+import torch
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+from sentence_splitter import SentenceSplitter, split_text_into_sentences
 
-# model_name = 'tuner007/pegasus_paraphrase'
-# torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-# tokenizer = PegasusTokenizer.from_pretrained(model_name)
-# model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
+model_name = 'tuner007/pegasus_paraphrase'
+torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+tokenizer = PegasusTokenizer.from_pretrained(model_name)
+model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
 
 
 def get_response(input_text, num_return_sequences):
@@ -35,23 +35,7 @@ def get_response_from_text(
 
 
 def greet(context):
-    # paraphrased_text = []
-    paragraphs = context.split("\n")
-    # for paragraph in paragraphs:
-
-    #     try:
-
-    #         paraphrased_paragraph = get_response_from_text(paragraph)
-    
-    #         paraphrased_text.append(paraphrased_paragraph)
-
-    #     except:
-    #         pass
-
-    # # Join the text list into a single text with newlines
-    # output_text = "\n".join(paraphrased_text)
-
-    return paragraphs
+    return get_response_from_text(context)
 
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text", title="Developed by Farhan Siddiqui")