Update app.py

app.py

@@ -2,7 +2,9 @@ import streamlit as st
 import time
 from huggingface_hub import from_pretrained_fastai
 import gensim
-
+import nltk
+nltk.download('punkt')
+from nltk.tokenize import sent_tokenize
 
 # textRank
 def textrank(corpus, ratio=0.2):    
@@ -44,21 +46,26 @@ model_type = st.radio('Pick a model',['RankText', 'Bart'])
 text = st.text_area('Enter or paste your text')
 
 def start_summarize(long_text, model):
+  num_of_sentences = len(sent_tokenize(long_text))
+  if num_of_sentences > 4:
     if model=="RankText":
       summary = textrank(long_text)[0]
-      if summary=="":
-        summary = long_text
+      # if summary=="":
+      # summary = long_text
     else:
       if model=="Bart":
         repo_id = "Aviv/Moran_Aviv_Bart"
         inf_learn = from_pretrained_fastai(repo_id)  
         summary = inf_learn.blurr_generate([long_text])[0]['generated_texts']
-    st.success(summary)
-    time.sleep(10)
-    grade = st.select_slider(
-     'What do you think about the summary?',
-     options=['Bad', 'Good', 'Excellent'],
-     value=('Good'))
+    # repo_id = "Aviv/Moran_Aviv_Bart"
+    # inf_learn = from_pretrained_fastai(repo_id)
+  else:
+    summary = long_text
+  st.success(summary)
+#   time.sleep(10)
+  grade = st.select_slider('What do you think about the summary?', options=['Bad', 'Good', 'Excellent'], value=('Good'))
+
+
   
 st.button('Summarize', on_click=start_summarize, args=(text, model_type, ) )
 # increment = st.button('Increment', on_click=increment_counter,