Create app.py

app.py

@@ -0,0 +1,38 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load summarization model
+@st.cache_resource
+def load_summarizer():
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    return summarizer
+
+# Summarize input text
+def summarize_text(input_text, max_length=130, min_length=30):
+    summarizer = load_summarizer()
+    
+    summary = summarizer(input_text, max_length=max_length, min_length=min_length, do_sample=False)
+    
+    return summary[0]['summary_text']
+
+# Streamlit app interface
+def main():
+    st.title("Text Summarization App")
+
+    # Input box for text to summarize
+    input_text = st.text_area("Enter the text you want to summarize:", "Paste your article or text here...")
+
+    # Slider for max and min length of the summary
+    max_length = st.slider("Maximum length of summary:", min_value=50, max_value=300, value=130)
+    min_length = st.slider("Minimum length of summary:", min_value=20, max_value=100, value=30)
+
+    # Button to summarize
+    if st.button("Summarize"):
+        with st.spinner("Summarizing..."):
+            summary = summarize_text(input_text, max_length=max_length, min_length=min_length)
+            st.subheader("Summary")
+            st.write(summary)
+
+# Run the app
+if __name__ == '__main__':
+    main()