Update app.py

app.py

@@ -1,11 +1,13 @@
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
 import gradio as grad
-import ast
-mdl_name = "distilbert-base-cased-distilled-squad"
-my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
-def answer_question(question,context):
-    text= "{"+"'question': '"+question+"','context': '"+context+"'}"
-    di=ast.literal_eval(text)
-    response = my_pipeline(di)
+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
-grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()
+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()