Update app.py

app.py

@@ -1,27 +1,26 @@
 import gradio as gr
-import torch
-
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality")
 tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality")
-
-device = torch.device("cude" if torch.cuda.is_available() else "cpu")
-model = model.to(device)
-
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)# Diverse Beam search
 def generate_text(inp):
-  text = "paraphrase: "+context + " </s>"
   context = inp
-  encoding = tokenizer.encode_plus(text, max_length=128, padding=True, return_tensors="pt")
-  input_ids, attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
+  text = "paraphrase: "+context + " </s>"
+  encoding = tokenizer.encode_plus(text,max_length =128, padding=True, return_tensors="pt")
+  input_ids,attention_mask  = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
   model.eval()
-  diverse_beams_output = model.generate(
-    input_ids=input_ids, attention_mask= attention_mask, max_length=128, early_stopping=True, num_beams=5, num_beam_groups=5, num_return_sequences=5, diversity_penalty=0.70)
-    
-  sent = tokenizer.decode(diverse_beams_outputs[0], skip_special_tokens = True, clean_up_tokenization_spaces = True)
+  diverse_beam_outputs = model.generate(
+    input_ids=input_ids,attention_mask=attention_mask,
+    max_length=128,
+    early_stopping=True,
+    num_beams=5,
+    num_beam_groups = 5,
+    num_return_sequences=5,
+    diversity_penalty = 0.70)
+  sent = tokenizer.decode(diverse_beam_outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
   return sent
 
-title = "Paraphraser One"
-description = "Paraphrase means to express meaning using different words. Write or paste your text below, submit, and the machine will attempt to express your meaning using different words."
-
 output_text = gr.outputs.Textbox()
-gr.Interface(generate_text, "textbox", output_text, title=title, description=description).launch(inline=False)
+gr.Interface(generate_text,"textbox", output_text).launch(inline=False)