Create app.py

app.py

@@ -0,0 +1,27 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-large-vietnews-summarization")
+
+model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-large-vietnews-summarization")
+
+def preprocess(inp):
+    text = "summarize: " + inp + " </s>"
+    features = tokenizer(text, return_tensors="pt")
+    return features['input_ids'], features['attention_masks']
+def predict(input_ids, attention_masks):
+    outputs = model.generate(
+        input_ids=input_ids, attention_mask=attention_masks,
+        max_length=256,
+        early_stopping=True,
+    )
+    res = tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]
+    return res
+
+if __name__ == '__main__':
+    st.title("ViT5 News Abstractive Summarization (Vietnamese)")
+    with st.container():
+        txt = st.text_area('Enter long documment...', ' ')
+        inp_ids, attn_mask = preprocess(txt)
+        st.write('Summary:', predict(inp_ids, attn_mask))
+