Update app.py

app.py

@@ -2,34 +2,40 @@ import streamlit as st
 from transformers import pipeline
 
 # Load the text summarization pipeline
-model3_p1 = pipeline("summarization", model="syndi-models/titlewave-t5-base")
+try:
+    model3_p1 = pipeline("summarization", model="syndi-models/titlewave-t5-base")
+except ValueError as e:
+    st.error(f"Error loading summarization model: {e}")
 
-# Load the classification pipeline
+# Load the news classification pipeline
 model_name2_p2 = "elozano/bert-base-cased-news-category"
-classifier = pipeline("text-classification", model=model_name2_p2, return_all_scores=True)
+try:
+    classifier = pipeline("text-classification", model=model_name2_p2, return_all_scores=True)
+except ValueError as e:
+    st.error(f"Error loading classification model: {e}")
 
 # Streamlit app title
-st.title("Question Summarization and Classification")
+st.title("Summarization and News Classification")
 
 # Tab layout
-tab1, tab2 = st.tabs(["Question Summarization", "Question Classification"])
+tab1, tab2 = st.tabs(["Text Summarization", "News Classification"])
 
 with tab1:
-    st.header("Question Summarization")
+    st.header("Text Summarization")
     # Input text for summarization
-    text_to_summarize = st.text_area("Enter question to summarize:", "")
-    if st.button("Summarize"):
+    text_to_summarize = st.text_area("Enter text to summarize:", "")
+    if st.button("Summarize") and 'model3_p1' in globals():
         # Perform text summarization
         summary = model3_p1(text_to_summarize, max_length=130, min_length=30, do_sample=False)
         # Display the summary result
         st.write("Summary:", summary[0]['summary_text'])
 
 with tab2:
-    st.header("Question Classification")
+    st.header("News Classification")
     # Input text for news classification
-    text_to_classify = st.text_area("Enter question title to classify:", "")
-    if st.button("Classify"):
-        # Perform question classification
+    text_to_classify = st.text_area("Enter text to classify:", "")
+    if st.button("Classify") and 'classifier' in globals():
+        # Perform news classification
         results = classifier(text_to_classify)[0]
         # Display the classification result
         max_score = float('-inf')