Update app.py

app.py

@@ -215,14 +215,6 @@ with tab1:
     
         # Classify each article and store predictions
         df["Class"] = df["preprocessed_content"].apply(lambda text: classifier(text)[0]["label"])
-    
-        # Keep only necessary columns
-        df = df[['content','Class']]
-        df1=df[['content']]
-        
-        #show Classification Results
-        st.subheader("Classification Results")
-        st.write(df)
 
         # Word Cloud Visualization
         def create_wordcloud(text_data):
@@ -234,7 +226,16 @@ with tab1:
             st.pyplot(plt)
 
         st.subheader("Word Cloud of News Content")
-        create_wordcloud(df['content'])
+        create_wordcloud(df['preprocessed_content'])
+        
+        # Keep only necessary columns
+        df = df[['content','Class']]
+
+        
+        #show Classification Results
+        st.subheader("Classification Results")
+        st.write(df)
+
 
         #show class distribution
         st.subheader("Class Distribution")
@@ -285,7 +286,7 @@ with tab2:
     #generate the answer based on selected news content using the given model
     st.markdown("---")
     st.header("Ask Questions Based on Your News Content")
-    context_1 = st.selectbox("Choose an article for the question:", df1['content'].tolist())
+    context_1 = st.selectbox("Choose an article for the question:", df['content'].tolist())
     
     question_1 = st.text_input("Enter your question:", key="question_input")
     if st.button("Get Answer", key="get_answer_1"):