v2

app.py

@@ -4,41 +4,68 @@ import streamlit as st
 from newspaper import Article
 from transformers import pipeline
 
+# set config
 st.set_page_config(layout="wide", page_title="SummarizeLink")
 
-# load the summarization model
+# load the summarization model (cache for faster loading)
 @st.cache(allow_output_mutation=True)
 def load_summarize_model():
     # model = pipeline("summarization", model='sshleifer/distilbart-cnn-12-6')
     model = pipeline("summarization")
     return model
+
+# loading the model
 summ = load_summarize_model()
 
-# define functions
+# define the down functions
 def download_and_parse_article(url):
+    """Downloads and parses an article from a URL.
+
+    Parameters
+    ----------
+    url : str
+        The URL of the article to download and parse.
+
+    Returns
+    -------
+    article : newspaper.Article
+        The article downloaded and parsed.
+    """
+    # define the article
     article = Article(url)
+    # download and parse the article
     article.download()
     article.parse()
+    # return the article
     return article.text
 
-# define the app
+# APP
+# set title and subtitle
 st.title("SummarizeLink")
-st.text("Paste any article link below and click on the 'Summarize Text' button to get the summarized data")
-# st.subheader("This application is using HuggingFace's transformers pre-trained model for text summarization.")
+st.markdown("Paste any article link below and click on the 'Summarize' button.")
+st.markdown("*Note:* We truncate the text incase the article is lengthy! 🖖")
+# create the input text box and setting panel
 link = st.text_area('Paste your link here...', "https://towardsdatascience.com/a-guide-to-the-knowledge-graphs-bfb5c40272f1", height=50)
 button = st.button("Summarize")
-max_lengthy = st.sidebar.slider('Max summary length', min_value=30, max_value=700, value=100, step=10)
-# num_beamer = st.sidebar.slider('Speed vs quality of Summary (1 is fastest but less accurate)', min_value=1, max_value=8, value=4, step=1)
-with st.spinner("Summarizing..."):
+min_length = st.sidebar.slider('Min summary length', min_value=10, max_value=100, value=50, step=10)
+max_length = st.sidebar.slider('Max summary length', min_value=30, max_value=700, value=100, step=10)
+num_beams = st.sidebar.slider('Beam length', min_value=1, max_value=10, value=5, step=1)
+
+# if button is clicked
+with st.spinner("Parsing article and Summarizing..."):
     if button and link:
-        text = download_and_parse_article(link)     # get the text
+        # get the text
+        text = download_and_parse_article(link)  
+        # summarize the text
         summary = summ(text, 
                        truncation=True,
-                       max_length = max_lengthy, 
-                       min_length = 50, 
-                       num_beams=5, 
+                       max_length = max_length, 
+                       min_length = min_length, 
+                       num_beams=num_beams, 
                        do_sample=True,
                        early_stopping=True, 
                        repetition_penalty=1.5, 
                        length_penalty=1.5)[0]
+        # display the summary
+        st.markdown("**Summary:**")
         st.write(summary['summary_text'])

requirements.txt

@@ -1,2 +1,2 @@
 streamlit
-newspaper
+newspaper3k