Update app.py

app.py

@@ -1,36 +1,26 @@
-#import torch
-#from transformers import pipeline
-#import gradio as gr
-#import streamlit as st
-# from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
-# from gradio.mix import Parallel, Series
-
-
-#desc = "Summarize your text! (audio transcription available soon)"
-import streamlit as st
+import torch
 from transformers import pipeline
+import gradio as gr
+from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
+from gradio.mix import Parallel, Series
+
 
-pipe = pipeline('sentiment-analysis')
-text = st.text_area('enter some text!')
+desc = "Summarize your text! (audio transcription available soon)"
 
-if text:
-  out = pipe(text)
-  st.json(out)
-  
 
-#qa_model = 'huggingface/SamuelMiller/qa_squad'
-#my_model = 'huggingface/SamuelMiller/lil_sumsum'
-#better_model = 'huggingface/google/pegasus-large'
+qa_model = 'huggingface/SamuelMiller/qa_squad'
+my_model = 'huggingface/SamuelMiller/lil_sumsum'
+better_model = 'huggingface/google/pegasus-large'
 
-#def summarize(text):
-  #summ = gr.Interface.load(qa_model)      
-  #summary = summ(text)
-  #return summary
-#iface = gr.Interface(fn=summarize,
-                 #theme='huggingface', 
-                 #title= 'sum_it', 
-                 #description= desc,
-                 #inputs= "text",
-                 #outputs= 'textbox')
-#iface.launch(inline = False)
+def summarize(text):
+  summ = gr.Interface.load(qa_model)      
+  summary = summ(text)
+  return summary
+iface = gr.Interface(fn=summarize,
+                 theme='huggingface', 
+                 title= 'sum_it', 
+                 description= desc,
+                 inputs= "text",
+                 outputs= 'textbox')
+iface.launch(inline = False)