Create app.py

app.py

@@ -0,0 +1,16 @@
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+import gradio as grad
+
+model_name="google/pegasus-xsum"
+pega_tokenizer = PegasusTokenizer.from_pretrained(model_name)
+model = PegasusForConditionalGeneration.from_prtrained(model_name)
+
+def summarize(text):
+  tokens = pega_tokenizer(text, truncation=True, padding="longest", return_tensors="pt")
+  trans_text = model.generate(**tokens, num_return_sequences=5, max_length=200, temperature=1.5, num_beams=10)
+  response = pega_tokenizer.batch_decode(trans_text, skip_special_tokens=True)
+  return response
+
+in_text = grad.Textbox(lines=10, label="English", placeholder="English text here")
+out_text = grad.Textbox(lines=10, label="Summary")
+grad.Interface(summarize, inputs=in_text, outputs=out_text).launch()