requirements.txt

streamlit
transformers
torch

app.py

@@ -0,0 +1,37 @@
+import streamlit as st
+from transformers import pipeline
+
+st.set_page_config(page_title="LLM Text Summarizer", layout="centered")
+st.title("LLM-Powered Text Summarizer")
+st.markdown("This app summarizes long texts using a Hugging Face transformer model (`facebook/bart-large-cnn`).")
+
+@st.cache_resource
+def load_model():
+    return pipeline("summarization", model="facebook/bart-large-cnn")
+
+summarizer = load_model()
+
+text = st.text_area("Enter text to summarize", height=300)
+
+if st.button("Summarize"):
+    if len(text.strip()) == 0:
+        st.warning("Please enter some text first.")
+    else:
+        word_count = len(text.split())
+
+        if word_count > 1300:
+            max_tokens = 1300
+        elif word_count > 300:
+            max_tokens = 350
+        else:
+            max_tokens = 100
+
+        with st.spinner("Generating summary..."):
+            summary = summarizer(
+                text,
+                max_length=max_tokens,
+                min_length=int(max_tokens * 0.5),
+                do_sample=False
+            )
+            st.subheader("Summary:")
+            st.success(summary[0]['summary_text'])