Create app.py

app.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import streamlit as st
+from PIL import Image
+from bs4 import BeautifulSoup as soup
+from urllib.request import urlopen
+from newspaper import Article
+import io
+import nltk
+nltk.download('punkt')
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import pipeline
+from rouge import Rouge
+from nltk.sentiment import SentimentIntensityAnalyzer
+
+
+# In[2]:
+
+
+st.set_page_config(page_title='InNews: A Summarised News📰 Portal', page_icon="newspaper.ico")
+
+
+# In[3]:
+
+
+def fetch_news_search_topic(topic):
+    site = 'https://news.google.com/rss/search?q={}'.format(topic)
+    op = urlopen(site)  # Open that site
+    rd = op.read()  # read data from site
+    op.close()  # close the object
+    sp_page = soup(rd, 'xml')  # scrapping data from site
+    news_list = sp_page.find_all('item')  # finding news
+    return news_list
+
+
+# In[4]:
+
+
+def fetch_top_news():
+    site = 'https://news.google.com/news/rss'
+    op = urlopen(site)  # Open that site
+    rd = op.read()  # read data from site
+    op.close()  # close the object
+    sp_page = soup(rd, 'xml')  # scrapping data from site
+    news_list = sp_page.find_all('item')  # finding news
+    return news_list
+
+
+# In[5]:
+
+
+def fetch_category_news(topic):
+    site = 'https://news.google.com/news/rss/headlines/section/topic/{}'.format(topic)
+    op = urlopen(site)  # Open that site
+    rd = op.read()  # read data from site
+    op.close()  # close the object
+    sp_page = soup(rd, 'xml')  # scrapping data from site
+    news_list = sp_page.find_all('item')  # finding news
+    return news_list
+
+
+# In[6]:
+
+
+def fetch_news_poster(poster_link):
+    try:
+        u = urlopen(poster_link)
+        raw_data = u.read()
+        image = Image.open(io.BytesIO(raw_data))
+        st.image(image, use_column_width=True)
+    except:
+        image = Image.open("no_image.jpg")
+        st.image(image, use_column_width=True)
+
+
+# In[7]:
+
+
+from nltk.sentiment import SentimentIntensityAnalyzer
+
+def get_sentiment_label(sentiment_score):
+    if sentiment_score >= 0.05:
+        return "Positive"
+    elif sentiment_score <= -0.05:
+        return "Negative"
+    else:
+        return "Neutral"
+
+def display_news(list_of_news, news_quantity):
+    tokenizer = T5Tokenizer.from_pretrained('t5-base')
+    model = T5ForConditionalGeneration.from_pretrained('t5-base')
+    rouge = Rouge()
+    sentiment_analyzer = SentimentIntensityAnalyzer()  # Sentiment Analysis model
+
+    c = 0
+    for news in list_of_news:
+        c += 1
+        st.write('**({}) {}**'.format(c, news.title.text))
+        news_data = Article(news.link.text)
+        try:
+            news_data.download()
+            news_data.parse()
+            news_data.nlp()
+        except Exception as e:
+            st.error(e)
+        
+        # Abstractive Summarization
+        input_text = news_data.text
+        inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)
+        outputs = model.generate(inputs, max_length=500, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+        summary = tokenizer.decode(outputs[0])
+
+        fetch_news_poster(news_data.top_image)
+        with st.expander(news.title.text):
+            st.markdown(
+                '''<h6 style='text-align: justify;'>{}"</h6>'''.format(summary),
+                unsafe_allow_html=True)
+            st.markdown("[Read more at {}...]({})".format(news.source.text, news.link.text))
+            
+            # Calculate ROUGE scores
+            reference_summary = news_data.summary
+            rouge_scores = rouge.get_scores(summary, reference_summary)
+            rouge_1 = rouge_scores[0]['rouge-1']['f']
+            rouge_2 = rouge_scores[0]['rouge-2']['f']
+            rouge_l = rouge_scores[0]['rouge-l']['f']
+            
+            st.success("ROUGE-1 Score: {:.2f}".format(rouge_1))
+            st.success("ROUGE-2 Score: {:.2f}".format(rouge_2))
+            st.success("ROUGE-L Score: {:.2f}".format(rouge_l))
+            
+            # Perform sentiment analysis
+            sentiment_scores = sentiment_analyzer.polarity_scores(summary)
+            sentiment_score = sentiment_scores['compound']
+            sentiment_label = get_sentiment_label(sentiment_score)
+
+            st.write("Sentiment Score:", sentiment_score)
+            st.write("Sentiment Label:", sentiment_label)
+        
+        st.success("Published Date: " + news.pubDate.text)
+        if c >= news_quantity:
+            break
+
+
+# In[8]:
+
+
+def run():
+    st.title("InNews: A Summarised News📰")
+    image = Image.open("newspaper.png") 
+
+    col1, col2, col3 = st.columns([3, 5, 3])
+
+    with col1:
+        st.write("")
+
+    with col2:
+        st.image(image, use_column_width=False)
+
+    with col3:
+        st.write("")
+    category = ['--Select--', 'Trending🔥 News', 'Favourite💙 Topics', 'Search🔍 Topic']
+    cat_op = st.selectbox('Select your Category', category)
+    if cat_op == category[0]:
+        st.warning('Please select Type!!')
+    elif cat_op == category[1]:
+        st.subheader("✅ Here is the Trending🔥 news for you")
+        no_of_news = st.slider('Number of News:', min_value=5, max_value=25, step=1)
+        news_list = fetch_top_news()
+        display_news(news_list, no_of_news)
+    elif cat_op == category[2]:
+        av_topics = ['Choose Topic', 'WORLD', 'NATION', 'BUSINESS', 'TECHNOLOGY', 'ENTERTAINMENT', 'SPORTS', 'SCIENCE',
+                     'HEALTH']
+        st.subheader("Choose your favourite Topic")
+        chosen_topic = st.selectbox("Choose your favourite Topic", av_topics)
+        if chosen_topic == av_topics[0]:
+            st.warning("Please Choose the Topic")
+        else:
+            no_of_news = st.slider('Number of News:', min_value=5, max_value=25, step=1)
+            news_list = fetch_category_news(chosen_topic)
+            if news_list:
+                st.subheader("✅ Here are the some {} News for you".format(chosen_topic))
+                display_news(news_list, no_of_news)
+            else:
+                st.error("No News found for {}".format(chosen_topic))
+
+    elif cat_op == category[3]:
+        user_topic = st.text_input("Enter your Topic🔍")
+        no_of_news = st.slider('Number of News:', min_value=5, max_value=15, step=1)
+
+        if st.button("Search") and user_topic != '':
+            user_topic_pr = user_topic.replace(' ', '')
+            news_list = fetch_news_search_topic(topic=user_topic_pr)
+            if news_list:
+                st.subheader("✅ Here are the some {} News for you".format(user_topic.capitalize()))
+                display_news(news_list, no_of_news)
+            else:
+                st.error("No News found for {}".format(user_topic))
+        else:
+            st.warning("Please write Topic Name to Search🔍")
+
+
+run()
+
+
+
+
+
+