Update app.py

app.py

@@ -9,7 +9,7 @@ except ValueError as e:
     st.error(f"Error loading summarization model: {e}")
     summarizer_loaded = False
 
-# Load the news classification pipeline
+# Load the Question classification pipeline
 model_name = "elozano/bert-base-cased-news-category"
 try:
     classifier = pipeline("text-classification", model=model_name, return_all_scores=True)
@@ -19,13 +19,13 @@ except ValueError as e:
     classifier_loaded = False
 
 # Streamlit app title
-st.title("Summarization and News Classification")
+st.title("Long Question Summarization and Classification")
 
 # Tab layout
-tab1, tab2 = st.tabs(["Text Summarization", "News Classification"])
+tab1, tab2 = st.tabs(["Question Summarization", "Question Classification"])
 
 with tab1:
-    st.header("Text Summarization")
+    st.header("Question Summarization")
     # Input text for summarization
     text_to_summarize = st.text_area("Enter text to summarize:", "")
     if st.button("Summarize"):
@@ -41,13 +41,13 @@ with tab1:
             st.warning("Please enter text to summarize and ensure the model is loaded.")
 
 with tab2:
-    st.header("News Classification")
-    # Input text for news classification
+    st.header("Questions Classification")
+    # Input text for Question classification
     text_to_classify = st.text_area("Enter text to classify:", "")
     if st.button("Classify"):
         if classifier_loaded and text_to_classify:
             try:
-                # Perform news classification
+                # Perform uestion classification
                 results = classifier(text_to_classify)[0]
                 # Find the category with the highest score
                 max_score = max(results, key=lambda x: x['score'])