Create app.py

app.py

@@ -0,0 +1,45 @@
+# Imports
+import os
+import torch
+import re
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import transformers
+import gradio as gr
+
+
+WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
+
+model_name = "csebuetnlp/mT5_multilingual_XLSum"
+tokenizer = AutoTokenizer.from_pretrained(model_name,use_fast=False)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+def generate_summary(text):
+
+   input_ids = tokenizer(
+    [WHITESPACE_HANDLER(text)],
+    return_tensors="pt",
+    padding="max_length",
+    truncation=True,
+    max_length=512)["input_ids"]
+
+   output_ids = model.generate(
+       input_ids=input_ids,
+       max_length=84,
+       no_repeat_ngram_size=2,
+       num_beams=4
+   )[0]
+
+   summary = tokenizer.decode(
+       output_ids,
+       skip_special_tokens=True,
+       clean_up_tokenization_spaces=False
+   )
+
+   return summary
+
+demo = gr.Interface(fn=generate_summary,
+                    inputs=gr.Textbox(lines=10, placeholder="Matinni kiriting!"),
+                    outputs=gr.Textbox(lines=4)
+                    )
+
+demo.launch()