Create app.py

app.py

@@ -0,0 +1,31 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load model (cached)
+@st.cache_resource
+def load_summarizer():
+    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+    return summarizer
+
+summarizer = load_summarizer()
+
+# Streamlit UI
+st.set_page_config(page_title="Text Summarizer", layout="centered")
+st.title("📝 Text Summarizer")
+st.markdown("Enter a long piece of text, and this app will summarize it using a Hugging Face transformer model.")
+
+# Input box
+text_input = st.text_area("Enter your text here", height=300)
+
+# Button to summarize
+if st.button("Summarize"):
+    if not text_input.strip():
+        st.warning("⚠️ Please enter some text first.")
+    else:
+        with st.spinner("Summarizing..."):
+            try:
+                summary = summarizer(text_input, max_length=130, min_length=30, do_sample=False)
+                st.subheader("Summary:")
+                st.success(summary[0]['summary_text'])
+            except Exception as e:
+                st.error(f"❌ Error during summarization: {e}")