Create app.py

app.py

@@ -0,0 +1,29 @@
+import streamlit as st
+from transformers import pipeline
+
+
+# Function to load the model
+@st.cache(allow_output_mutation=True)
+def load_model():
+    summarizer = pipeline("text2text-generation", model="pk248/pegasus-samsum")
+    return summarizer
+
+
+# Load the model
+model = load_model()
+
+# Streamlit UI
+st.title("Text Summarization App")
+st.write("This app uses the pk248/pegasus-samsum model to summarize text.")
+
+# Text input
+user_input = st.text_area("Enter text to summarize:", height=200)
+
+# Summarize button
+if st.button("Summarize"):
+    if user_input:
+        # Model inference
+        summary = model(user_input, max_length=100, min_length=5, length_penalty=2.0)
+        st.write(summary[0]["generated_text"])
+    else:
+        st.write("Please enter some text to summarize.")