Update app.py

app.py

@@ -35,24 +35,20 @@ from transformers import (
 )
 
 #initializing the tokenizer and the model
-model_type_2 ="valurank/pegasus-multi_news-headline"
-tokenizer_2 = AutoTokenizer.from_pretrained(model_type_2)
-model_2 = AutoModelForSeq2SeqLM.from_pretrained(model_type_2)
+tokenizer = AutoTokenizer.from_pretrained("abdulmatinomotoso/pegasus-samsum")
+model = AutoModelForSeq2SeqLM.from_pretrained("abdulmatinomotoso/pegasus-samsum")
 
 #Defining a function to generate the headlines
 def headline_generator_2(file):
   input_text = clean_text(file)
   input_text = sent_tokenize(input_text)
   text = ''.join(input_text[:6])
-
-  with tokenizer_2.as_target_tokenizer():
-        batch = tokenizer_2(
-            text, truncation=True, return_tensors="pt"
-        )
-        
-  translated = model_2.generate(**batch)
-  summary_2 = tokenizer_2.batch_decode(translated, skip_special_tokens=True, max_length=20)
-  return summary_2[0]
+  
+  inputs = tokenizer(text,truncation=True, return_tensors="pt")
+  summary_ids = model.generate(inputs["input_ids"])
+  summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+  
+  return summary
   
 #creating an interface for the headline generator using gradio
 demo = gr.Interface(headline_generator_2, inputs=[gr.inputs.Textbox(label="Drop your .txt file here", optional=False)],