Update app.py

app.py

@@ -142,7 +142,7 @@ tab1, tab2, tab3 = st.tabs(["News Classification", "Ask Questions", "Advanced Fe
 
 with tab1:
     st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.header("News Classification Pipeline")
+    st.header("News Classification ")
     st.write("Upload a CSV file containing news excerpts to classify them into categories.")
     
     # File uploader with a descriptive message
@@ -274,23 +274,10 @@ with tab3:
     st.header("Advanced Features")
     st.write("Explore additional functionalities to enhance your news analysis.")
     
-    # Sentiment Analysis
-    st.subheader("📊 Sentiment Analysis")
-    sentiment_text = st.text_area("Enter text for sentiment analysis:", height=100)
-    if st.button("Analyze Sentiment"):
-        with st.spinner("Analyzing sentiment..."):
-            sentiment_pipeline = pipeline("sentiment-analysis")
-            result = sentiment_pipeline(sentiment_text)[0]
-            st.write(f"Label: {result['label']}")
-            st.write(f"Confidence: {result['score']:.2f}")
-            if result['label'] == 'POSITIVE':
-                st.success("This text appears positive!")
-            else:
-                st.warning("This text appears negative.")
     
     # Named Entity Recognition
-    st.subheader("🏷 Named Entity Recognition")
-    ner_text = st.text_area("Enter text for entity recognition:", height=100)
+    st.subheader("Named Entity Recognition Of News Content")
+    ner_text = st.text_area("Enter News Content for entity recognition:", height=100)
     if st.button("Extract Entities"):
         with st.spinner("Identifying entities..."):
             ner_pipeline = pipeline("ner", grouped_entities=True)
@@ -305,14 +292,29 @@ with tab3:
             st.table(pd.DataFrame(entities))
     
     # Text Summarization
-    st.subheader("✍ Text Summarization")
-    summary_text = st.text_area("Enter text to summarize:", height=150)
+    st.subheader("News Content Summarization")
+    summary_text = st.text_area("Enter news content to summarize:", height=150)
     if st.button("Generate Summary"):
         with st.spinner("Generating summary..."):
             summarizer = pipeline("summarization")
             summary = summarizer(summary_text, max_length=130, min_length=30)
             st.write(summary[0]['summary_text'])
     st.markdown('</div>', unsafe_allow_html=True)
+    
+
+    # Sentiment Analysis
+    st.subheader("News Tone Detector")
+    sentiment_text = st.text_area("Enter text for news content analysis:", height=100)
+    if st.button("Analyze Sentiment"):
+        with st.spinner("Analyzing sentiment..."):
+            sentiment_pipeline = pipeline("sentiment-analysis")
+            result = sentiment_pipeline(sentiment_text)[0]
+            st.write(f"Label: {result['label']}")
+            st.write(f"Confidence: {result['score']:.2f}")
+            if result['label'] == 'POSITIVE':
+                st.success("This text appears positive!")
+            else:
+                st.warning("This text appears negative.")
 
 # Enhanced Sidebar with branding and instructions
 with st.sidebar: