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app.py

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+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+import gradio as grad
+mdl_name = "google/pegasus-xsum"
+pegasus_tkn = PegasusTokenizer.from_pretrained(mdl_name)
+mdl = PegasusForConditionalGeneration.from_pretrained(mdl_name)
+
+
+
+
+def summarize(text):
+    tokens = pegasus_tkn(text, truncation=True, padding="longest", return_tensors="pt")
+    txt_summary = mdl.generate(**tokens)
+    response = pegasus_tkn.batch_decode(txt_summary, skip_special_tokens=True)
+    return response
+txt=grad.Textbox(lines=10, label="English", placeholder="English Text here")
+out=grad.Textbox(lines=10, label="Summary")
+grad.Interface(summarize, inputs=txt, outputs=out).launch()

requirements.txt

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+gradio
+transformers
+torch
+transformers[sentencepiece]