start with one model

app.py

@@ -5,18 +5,18 @@ 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")
-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")
-io9 = gr.Interface.load("huggingface/google/pegasus-wikihow")           
-io10 = gr.Interface.load('huggingface/google/pegasus-gigaword')
-io11 = gr.Interface.load("huggingface/google/pegasus-billsum")
-io12 = gr.Interface.load("huggingface/google/pegasus-big_patent")           
-io13 = gr.Interface.load("huggingface/google/pegasus-aeslc")           
+# 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")
+# io9 = gr.Interface.load("huggingface/google/pegasus-wikihow")           
+# io10 = gr.Interface.load('huggingface/google/pegasus-gigaword')
+# io11 = gr.Interface.load("huggingface/google/pegasus-billsum")
+# io12 = gr.Interface.load("huggingface/google/pegasus-big_patent")           
+# io13 = gr.Interface.load("huggingface/google/pegasus-aeslc")           
 
 desc =  "Let Hugging Face models summarize texts for you. Note: Shorter articles generate faster summaries. This summarizer uses bart-large-cnn model by Facebook, pegasus by Google and distilbart-cnn-12-6 by Sshleifer. You can compare these models against each other on their performances. Sample Text input is provided!"
         
@@ -42,7 +42,8 @@ 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,
+iface = Parallel(io1, 
+                #  io2, io3, io4, io5, io6, io7, io8, io9, io10, io11, io12, io13,
                  theme='huggingface', 
                  title= 'Hugging Face Text Summarizer', 
                  description = desc,