Update app.py

app.py

@@ -142,13 +142,16 @@ model_type = st.sidebar.selectbox(
     "Model type", options=["Facebook-Bart", "Sshleifer-DistilBart"]
 )
 
+max_len= st.sidebar.slider("Maximum length of the summarized text, if the total text length is greater than 500 tokens the text will be divided into chunks of 500 and the max length represents the length of each chunk",min_value=100,max_value=500)
+min_len= st.sidebar.slider("Minimum length of the summarized text",min_value=30)
+
 st.markdown(
     "Model Source: [Facebook-Bart-large-CNN](https://huggingface.co/facebook/bart-large-cnn) and [Sshleifer-distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6)"
 )
 
 st.markdown(
     """The app supports extractive summarization which aims to identify the salient information that is then extracted and grouped together to form a concise summary. 
-    For documents or text that is more than 500 words long, the app will divide the text into chunks and summarize each chunk.
+    For documents or text that is more than 500 words long, the app will divide the text into chunks and summarize each chunk. Please note when using the sidebar slider, those values represent the min/max text length per chunk of text to be summarized. If your article to be summarized is 1000 words, it will be divided into two chunks of 500 words first then the default max length of 100 words is applied per chunk, resulting in a summarized text with 200 words maximum. 
     There are two models available to choose from:""")
 
 st.markdown("""   
@@ -216,7 +219,7 @@ if summarize:
             text="Loading Facebook-Bart Model and Extracting summary. This might take a few seconds depending on the length of your text..."
         ):
             summarizer_model = facebook_model()
-            summarized_text = summarizer_model(text_to_summarize, max_length=100, min_length=30)
+            summarized_text = summarizer_model(text_to_summarize, max_length=max_len, min_length=min_len)
             summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text])
     
     elif model_type == "Sshleifer-DistilBart":
@@ -229,7 +232,7 @@ if summarize:
             text="Loading Sshleifer-DistilBart Model and Extracting summary. This might take a few seconds depending on the length of your text..."
         ):
             summarizer_model = schleifer_model()
-            summarized_text = summarizer_model(text_to_summarize, max_length=100, min_length=30)
+            summarized_text = summarizer_model(text_to_summarize, max_length=max_len, min_length=min_len)
             summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text])       
     
     # final summarized output