Create app.py

app.py

@@ -0,0 +1,23 @@
+import gradio as gr
+import torch
+from transformers import BertTokenizerFast, EncoderDecoderModel
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+ckpt = 'mrm8488/bert2bert_shared-spanish-finetuned-summarization'
+tokenizer = BertTokenizerFast.from_pretrained(ckpt)
+model = EncoderDecoderModel.from_pretrained(ckpt).to(device)
+
+def generate_summary(text):
+
+   inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
+   input_ids = inputs.input_ids.to(device)
+   attention_mask = inputs.attention_mask.to(device)
+   output = model.generate(input_ids, attention_mask=attention_mask)
+   return tokenizer.decode(output[0], skip_special_tokens=True)
+
+demo = gr.Interface(fn=generate_summary, 
+                    inputs=gr.Textbox(lines=10, placeholder="Insert the text here"), 
+                    outputs=gr.Textbox(lines=4)
+                    )
+
+demo.launch()