Updated

app.py

@@ -1,25 +1,38 @@
 import streamlit as st
 from transformers import pipeline
 
-# Initialize the summarizer pipeline
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+# Available summarization models (you can expand this list)
+available_models = [
+    "facebook/t5-small",
+    "google/pegasus-xsum",
+    "sshleifer/distilbart-cnn-12-6",
+]
 
-def summarize_text(text):
-    summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
-    return summary[0]['summary_text']
+@st.cache_resource
+def load_summarizer(model_name):
+    """Loads the summarization pipeline for a given model."""
+    summarizer = pipeline("summarization", model=model_name)
+    return summarizer
 
-# Streamlit app layout
-st.title("Text Summarizer")
-st.write("This app uses Hugging Face's transformers to summarize any text you provide.")
+st.title("Text Summarization App")
 
-# User input
-input_text = st.text_area("Enter Text to Summarize", height=200)
+text_to_summarize = st.text_area("Enter text to summarize:", height=300)
+
+selected_model = st.selectbox("Choose a summarization model:", available_models)
 
 if st.button("Summarize"):
-    if input_text:
-        with st.spinner("Summarizing..."):
-            summary = summarize_text(input_text)
-            st.subheader("Summary:")
-            st.write(summary)
+    if text_to_summarize:
+        with st.spinner(f"Summarizing using {selected_model}..."):
+            summarizer = load_summarizer(selected_model)
+            summary = summarizer(text_to_summarize, max_length=150, min_length=30, do_sample=False)[0]['summary_text']
+        st.subheader("Summary:")
+        st.write(summary)
     else:
         st.warning("Please enter some text to summarize.")
+
+st.sidebar.header("About")
+st.sidebar.info(
+    "This app uses the `transformers` library from Hugging Face "
+    "to perform text summarization. You can select from various "
+    "pre-trained models."
+)