updated code'

app.py

@@ -1,18 +1,29 @@
 import streamlit as st
 from transformers import pipeline
 
-# Force PyTorch backend
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn", framework="pt")
+# Set page title and description
+st.title("📄 Text Summarization App")
+st.write("""
+This app allows users to upload a text file and get a summarized version using a Natural Language Processing (NLP) model. 
+It uses the `transformers` library from Hugging Face, which provides state-of-the-art machine learning models.
+""")
 
-st.title("Text Summarization App")
+# Load summarization pipeline
+summarizer = pipeline("summarization")
 
-# Input text
-text = st.text_area("Enter your text:", "")
+# File uploader
+uploaded_file = st.file_uploader("Upload a text file", type=["txt"])
 
-if st.button("Summarize"):
-    if text:
-        summary = summarizer(text, max_length=50, min_length=10, do_sample=False)
-        st.subheader("Summary:")
-        st.write(summary[0]["summary_text"])
-    else:
-        st.warning("Please enter some text.")
+if uploaded_file is not None:
+    # Read the file content
+    text = uploaded_file.read().decode("utf-8")
+    
+    # Display original text (optional)
+    st.subheader("Original Text")
+    st.text_area("Content:", text, height=200)
+
+    # Summarize the text
+    if st.button("Summarize"):
+        summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
+        st.subheader("Summarized Text")
+        st.write(summary[0]['summary_text'])