Upload app.py

app.py

@@ -0,0 +1,39 @@
+import torch
+from transformers import (T5ForConditionalGeneration,T5Tokenizer)
+import gradio as gr
+
+def set_seed(seed):
+  torch.manual_seed(seed)
+set_seed(42)
+
+best_model_path = "aditi2222/t5-paraphrase"
+model = T5ForConditionalGeneration.from_pretrained(best_model_path)
+tokenizer = T5Tokenizer.from_pretrained('aditi2222/t5-paraphrase')
+
+def tokenize_data(text):
+    # Tokenize the review body
+    #input_ = "paraphrase: "+ str(text) + ' </s>'
+
+    max_len = 64
+    # tokenize inputs
+    tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')
+
+    inputs={"input_ids": tokenized_inputs['input_ids'],
+        "attention_mask": tokenized_inputs['attention_mask']
+    }
+    
+    return inputs
+
+def generate_answers(text):
+    inputs = tokenize_data(text)
+    results= model.generate(input_ids= inputs['input_ids'].to(device), attention_mask=inputs['attention_mask'].to(device), do_sample=True,
+                            max_length=64,
+                            top_k=120,
+                            top_p=0.98,
+                            early_stopping=True,
+                            num_return_sequences=1)
+    answer = tokenizer.decode(results[0], skip_special_tokens=True)
+    return answer
+
+iface = gr.Interface(fn=generate_answers, inputs=['text'],outputs=["text"])
+iface.launch(share=True)