Create app.py

app.py

@@ -0,0 +1,24 @@
+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)
+
+def generate_text(inp):
+  text = "paraphrase: "+context + " </s>"
+  context = inp
+  encoding = tokenizer.encode_plus(text, max_length=256, 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 = max_length=256, 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)
+    return sent
+  
+output_text = gr.outputs.Textbox()
+gr.Interface(generate_text, "textbox", output_text).launch(inline=False)