Create app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+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")
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#print ("device ",device)
+model = model.to(device)# Diverse Beam search
+#print ("\n\n")
+#print ("Original: ",context)
+
+def generate_text(inp):
+    context = inp
+    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_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
+        
+
+output_text = gr.outputs.Textbox()
+gr.Interface(generate_text,"textbox", output_text).launch(inline=False)