Create app.py

app.py

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+import streamlit as st
+st.title("Text Summarizer")
+options=['Paste the url',"Paste the raw article"]
+select=st.radio("Options",options)
+if select == "Paste the url":
+    import newspaper
+    from newspaper import Article
+    import nltk
+    nltk.download('punkt')
+    url =st.text_input('Paste the article link below',"")
+    language={"English":"en","Hindi":"hi"}
+    selected_lang=st.selectbox("Language of the article", language)
+    if st.button("Summarize"):
+        my_article = Article(url, selected_lang)
+        my_article.download()
+        my_article.parse()
+        st.header(my_article.title)
+        # NLP on the article
+        
+        my_article.nlp()
+        # Extract summary
+        st.markdown(my_article.summary)
+        # Extract keywords
+        st.markdown('Keywords: '+','.join([i for i in (my_article.keywords)]))
+else:
+    from transformers import PegasusForConditionalGeneration
+    from transformers import PegasusTokenizer
+    from transformers import pipeline
+    model_name = "google/pegasus-xsum"
+    pegasus_tokenizer = PegasusTokenizer.from_pretrained(model_name)
+    input_text=st.text_area("Input the text to summarize","")
+    if st.button("Summarize"):
+        st.text("It may take a minute or two.")
+        nwords=len(input_text.split(" "))
+        summarizer = pipeline("summarization", model=model_name, tokenizer=pegasus_tokenizer,min_length=int(nwords/10)+20, max_length=int(nwords/5+20), framework="pt")
+        summary=summarizer(input_text)[0]['summary_text']
+        st.header("Summary")
+        st.markdown(summary)
+