Create app.py

app.py

@@ -0,0 +1,21 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("nsi319/legal-pegasus")
+model = AutoModelForSeq2SeqLM.from_pretrained("nsi319/legal-pegasus")
+
+def summarise(text):
+  input_tokenized = tokenizer.encode(text, return_tensors='pt',max_length=1024,truncation=True)
+  summary_ids = model.generate(input_tokenized,
+                                    num_beams=9,
+                                    no_repeat_ngram_size=3,
+                                    length_penalty=2.0,
+                                    min_length=150,
+                                    max_length=250,
+                                    early_stopping=True)
+  return [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids][0]
+
+
+
+iface = gr.Interface(fn=summarise, inputs="text", outputs="text")
+iface.launch()