Made the view by category section faster

app.py

@@ -135,7 +135,22 @@ if uploaded_file:
 
     # ----------------- Classification -----------------
     with st.spinner("🔍 Classifying news articles..."):
-        df['class'] = df['cleaned_text'].apply(lambda text: classifier(text)[0]['label'])
+        # Get full classification results (label + score)
+        classification_results = df['cleaned_text'].apply(lambda text: classifier(text)[0])
+
+        # Store all classification data in the dataframe
+        df['class'] = classification_results.apply(lambda x: x['label'])
+        df['confidence'] = classification_results.apply(lambda x: x['score'])
+
+
+        # Cache the categorized articles in session state
+        @st.cache_data
+        def get_category_articles(_df):
+            return {category: _df[_df['class'] == category] for category in _df['class'].unique()}
+
+
+        st.session_state.category_articles = get_category_articles(df)
+        st.session_state.classified_df = df  # Store the full classified dataframe
 
     st.success("✅ Classification Complete!")
     st.write("🔎 Classified Results:", df[['content', 'class']].head())
@@ -235,41 +250,28 @@ if uploaded_file:
                     st.warning("Date parsing failed")
 
 # ----------------- News Category Explorer -----------------
+    
     st.subheader("🔍 Explore News by Category")
 
-    with st.spinner("🔍 Classifying news articles..."):
-        classified = df['cleaned_text'].apply(lambda text: classifier(text)[0])
-        df['class'] = classified.apply(lambda x: x['label'])
-        df['confidence'] = classified.apply(lambda x: x['score'])
-
-    # Get unique categories
-    categories = df['class'].unique()
-
-    # Create 5 columns for category buttons
-    cols = st.columns(5)
-
-    # Create a dictionary to store category articles
-    @st.cache_data
-    def get_category_articles(df):
-        return {category: df[df['class'] == category] for category in df['class'].unique()}
-
-    category_articles = get_category_articles(df)
-
-    # Place each category button in its own column
-    for i, category in enumerate(categories):
-        with cols[i]:
-            if st.button(category, key=f"btn_{category}"):
-                # Create pop-up window
-                with st.popover(f"📰 Articles in {category}", use_container_width=True):
-                    st.markdown(f"### {category} Articles")
-                    articles = category_articles[category]
-
-                    # Display articles with expandable content
-                    for idx, row in articles.iterrows():
-                        with st.expander(f"Article {idx + 1}: {row['content'][:50]}...", expanded=False):
-                            st.write(row['content'])
-                            st.caption(f"Classification confidence: {row['confidence']:.2f}")
-
+    if 'category_articles' in st.session_state:
+        categories = st.session_state.classified_df['class'].unique()
+        cols = st.columns(min(5, len(categories)))
+
+        for i, category in enumerate(categories):
+            with cols[i % len(cols)]:
+                if st.button(category, key=f"btn_{category}"):
+                    with st.popover(f"📰 {category} Articles", use_container_width=True):
+                        st.markdown(f"### {category} Articles")
+                        articles = st.session_state.category_articles[category]
+
+                        with st.container(height=600):
+                            for idx, row in articles.iterrows():
+                                with st.expander(f"Article {idx + 1}", expanded=False):
+                                    st.write(row['content'])
+                                    st.caption(f"Confidence: {row['confidence']:.2f}")
+    else:
+        st.warning("Classification data not found. Please upload and classify a file first.")
+        
 # ----------------- Footer -----------------
 st.markdown("---")
 st.markdown("<p style='text-align:center; color:#666;'>🚀 Built with using Streamlit & Hugging Face</p>", unsafe_allow_html=True)