5 models

app.py

@@ -5,10 +5,10 @@ import gradio as gr
 from gradio.mix import Parallel, Series
 
 io1 = gr.Interface.load('huggingface/google/pegasus-large')
-# io2 = gr.Interface.load("huggingface/google/pegasus-cnn_dailymail")
-# io3 = gr.Interface.load("huggingface/google/pegasus-xsum")           
-# io4 = gr.Interface.load('huggingface/google/pegasus-newsroom')
-# io5 = gr.Interface.load("huggingface/google/pegasus-multi_news")
+io2 = gr.Interface.load("huggingface/google/pegasus-cnn_dailymail")
+io3 = gr.Interface.load("huggingface/google/pegasus-xsum")           
+io4 = gr.Interface.load('huggingface/google/pegasus-newsroom')
+io5 = gr.Interface.load("huggingface/google/pegasus-multi_news")
 # io6 = gr.Interface.load("huggingface/google/pegasus-reddit_tifu")           
 # io7 = gr.Interface.load('huggingface/google/pegasus-arxiv')
 # io8 = gr.Interface.load("huggingface/google/pegasus-pubmed")
@@ -43,7 +43,11 @@ In this article, we will discuss the latest innovations in machine learning tech
 sample = [[y],[x],[z]]
 
 iface = Parallel(io1, 
-                #  io2, io3, io4, io5, io6, io7, io8, io9, io10, io11, io12, io13,
+                 io2, 
+                 io3, 
+                 io4, 
+                 io5, 
+                #  io6, io7, io8, io9, io10, io11, io12, io13,
                  theme='huggingface', 
                  title= 'Hugging Face Text Summarizer', 
                  description = desc,