Update app.py

Parameter tuning of Pegasus model.

app.py

@@ -78,6 +78,27 @@
 # Because abstractive summarization involves paraphrasing words, it is also more time-consuming; 
 # however, it has the potential to produce a more polished and coherent summary.
 
+# 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()
+
+#------------------------------------------------------------------------------------------
+# 6. Same model with some tuning with some parameters: num_return_sequences=5, max_length=200, temperature=1.5, num_beams=10
+
 from transformers import PegasusForConditionalGeneration, PegasusTokenizer
 import gradio as grad
 
@@ -87,8 +108,8 @@ 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")