Create app.py

app.py

@@ -0,0 +1,22 @@
+import streamlit as st
+from transformers import pipeline
+
+
+@st.cache(allow_output_mutation=True)
+def summarize_model():
+    model = pipeline("summarization")
+    return model
+
+
+summ = summarize_model()
+st.title("Summarize Your Text")
+st.subheader("Paste any article in the text area below and click on the 'Summarize Text' button to get the summarized textual data")
+st.subheader("This application is using HuggingFace's transformers pre-trained model for text summarization.")
+sentence = st.text_area('Paste your copied data here...', height=100)
+button = st.button("Summarize Text")
+max_lengthy = st.sidebar.slider('Maximum summary length (words)', 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..."):
+    if button and sentence:
+        summary = summ(sentence, max_length = max_lengthy, min_length = 50, num_beams=num_beamer, do_sample=True,early_stopping=True, repetition_penalty=1.5, length_penalty=1.5)[0]
+        st.write(summary['summary_text'])