Create app.py

app.py

@@ -0,0 +1,32 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load model and tokenizer
+@st.cache_resource()
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum")
+    model = AutoModelForSeq2SeqLM.from_pretrained("google/pegasus-xsum")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+st.title("Text Summarization with Pegasus-XSum")
+st.write("Enter text below and get a summarized version using the Pegasus model.")
+
+# User input
+text_input = st.text_area("Enter text to summarize:", "")
+
+if st.button("Summarize"):
+    if text_input:
+        # Tokenize input
+        inputs = tokenizer(text_input, return_tensors="pt", truncation=True, max_length=512)
+        
+        # Generate summary
+        summary_ids = model.generate(**inputs, max_length=60, min_length=10, length_penalty=2.0, num_beams=4)
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+        
+        # Display summary
+        st.subheader("Summary:")
+        st.write(summary)
+    else:
+        st.warning("Please enter some text to summarize.")