Create app.py

app.py

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+import streamlit as st
+import json
+import requests
+import time
+from newspaper import Article
+
+
+# Page title layout
+c1, c2 = st.columns([0.32, 2])
+
+with c1:
+     st.image("images/newspaper.png", width=85)
+
+with c2:
+    st.title("FastNews Article Summarizer")
+
+st.markdown("**Generate summaries of articles and blog posts using abstractive summarization with Google's Pegasus language model.**")
+
+
+# Sidebar content
+st.sidebar.subheader("About the app")
+st.sidebar.info("This app uses 🤗HuggingFace's [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) model.\
+                 \nYou can find the source code [here](https://github.com/ivnlee/streamlit-text-summarizer)")
+st.sidebar.write("\n\n")
+st.sidebar.markdown("**Get a free API key from HuggingFace:**")
+st.sidebar.markdown("* Create a [free account](https://huggingface.co/join) or [login](https://huggingface.co/login)")
+st.sidebar.markdown("* Go to **Settings** and then **Access Tokens**")
+st.sidebar.markdown("* Create a new Token (select 'read' role)")
+st.sidebar.markdown("* Paste your API key in the text box")
+st.sidebar.divider()
+st.sidebar.write("Please make sure your article is in English and is not behind a paywall.")
+st.sidebar.write("\n\n")
+st.sidebar.divider()
+st.sidebar.caption("Created by [Ivan Lee](https://ivan-lee.medium.com/) using [Streamlit](https://streamlit.io/)🎈.")
+
+
+# Inputs 
+st.subheader("Enter the URL of the article you want to summarize")
+default_url = "https://"
+url = st.text_input("URL:", default_url)
+
+headers_ = {
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36'
+    }
+
+fetch_button = st.button("Fetch article")
+
+if fetch_button:
+    article_url = url
+    session = requests.Session()
+
+    try:
+        response_ = session.get(article_url, headers=headers_, timeout=10)
+    
+        if response_.status_code == 200:
+
+            with st.spinner('Fetching your article...'):
+                time.sleep(3)
+                st.success('Your article is ready for summarization!')
+
+        else:
+            st.write("Error occurred while fetching article.")
+
+    except Exception as e:
+        st.write(f"Error occurred while fetching article: {e}")
+
+
+# HuggingFace API KEY input
+API_KEY = st.text_input("Enter your HuggingFace API key", type="password")
+
+# HuggingFace API inference URL.
+API_URL = "https://api-inference.huggingface.co/models/google/pegasus-cnn_dailymail"
+
+headers = {"Authorization": f"Bearer {API_KEY}"}
+
+
+submit_button = st.button("Submit")
+
+# Download and parse the article
+if submit_button:
+    article = Article(url)
+    article.download()
+    article.parse()
+
+    title = article.title
+    text = article.text
+
+    # HuggingFace API request function
+    def query(payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.json()
+    
+    with st.spinner('Doing some AI magic, please wait...'):
+        time.sleep(1)
+
+        # Query the API
+        output = query({"inputs": text, })
+
+       # Display the results
+        summary = output[0]['summary_text'].replace('<n>', " ") 
+
+        st.divider()
+        st.subheader("Summary")
+        st.write(f"Your article: **{title}**")
+        st.write(f"**{summary}**")