Update app.py

app.py

@@ -58,23 +58,37 @@
 # out=grad.Textbox(lines=1, label="French")
 # grad.Interface(translate, inputs=txt, outputs=out).launch()
 
+# 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()
+
 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)
+    translated_txt = mdl.generate(**tokens,num_return_sequences=5,max_length=200,temperature=1.5,num_beams=10)
+    response = pegasus_tkn.batch_decode(translated_txt, 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()
 
 
-