Update app.py

app.py

@@ -2,12 +2,12 @@ import streamlit as st
 from transformers import pipeline
 
 # Title of the app
-st.title("Multi-Task NLP App with Hugging Face Transformers")
+st.title("Multi-Task NLP App with Transformers")
 st.write("Explore advanced NLP tasks: Sentiment Analysis, Text Summarization, and Question Answering.")
 
 # Load pre-trained models
 sentiment_analyzer = pipeline("sentiment-analysis")
-summarizer = pipeline("summarization")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 qa_pipeline = pipeline("question-answering")
 
 # Sidebar for task selection
@@ -26,14 +26,24 @@ if task == "Sentiment Analysis":
 
 elif task == "Text Summarization":
     st.header("Text Summarization")
-    user_input = st.text_area("Enter text to summarize:")
-    if st.button("Summarize"):
-        if user_input:
-            result = summarizer(user_input, max_length=50, min_length=25, do_sample=False)
+    # User input
+user_input = st.text_area("Enter text to summarize:")
+
+# Summarization parameters
+max_length = st.slider("Max Length of Summary", 50, 150, 100)
+min_length = st.slider("Min Length of Summary", 10, 50, 25)
+
+if st.button("Summarize"):
+    if user_input:
+        try:
+            # Generate summary
+            result = summarizer(user_input, max_length=max_length, min_length=min_length, do_sample=False)
             st.write("Summary:")
             st.write(result[0]['summary_text'])
-        else:
-            st.write("Please enter some text to summarize.")
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+    else:
+        st.write("Please enter some text to summarize.")
 
 elif task == "Question Answering":
     st.header("Question Answering")