Update app.py

app.py

@@ -219,6 +219,9 @@ with tab1:
         # Keep only necessary columns
         df = df[['content','Class']]
         
+        #show Classification Results
+        st.subheader("Classification Results")
+        st.write(df)
 
         # Word Cloud Visualization
         def create_wordcloud(text_data):
@@ -232,11 +235,7 @@ with tab1:
         st.subheader("Word Cloud of News Content")
         create_wordcloud(df['content'])
 
-
-        #show Classification Results
-        st.subheader("Classification Results")
-        st.write(df)
-    
+        #show class distribution
         st.subheader("Class Distribution")
         class_dist = df['Class'].value_counts()
         st.bar_chart(class_dist)
@@ -256,19 +255,23 @@ with tab2:
     st.markdown('<div class="card">', unsafe_allow_html=True)
     st.header("Ask Questions Based on Uploaded News Content File")
     st.write("Ask questions about news content and get answers from our AI model.")
-    
+
+    #check file is uploaded
     if uploaded_file is not None:
         context = ' '.join(df['content'].tolist())
         st.write(f"Loaded {len(df)} news excerpts")
     else:
         st.warning("Please upload a CSV file.")
-    
+
+    #generate the answer based on uloaded news content file using the given model 
     question = st.text_input("Enter your question:")
     if st.button("Get Answer"):
+        #check for file available
         if uploaded_file is None:
             st.error("Please upload a CSV file before asking a question.")
         elif context and question:
             with st.spinner("Searching for answers..."):
+                #load the model for Q&A pipline
                 qa_pipeline = load_qa_model()
                 result = qa_pipeline(question=question, context=context)
                 st.subheader("Answer")
@@ -277,12 +280,15 @@ with tab2:
                 st.write(f"Confidence: {result['score']:.2f}")
         else:
             st.error("Please enter a question.")
-    
+
+    #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.text_area("Enter the news content (context):")
+    article = 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"):
+        #check for selected context and question are available
         if context_1 and question_1:
             qa_pipeline = load_qa_model()
             answer_1 = qa_pipeline(question=question_1, context=context_1)
@@ -297,11 +303,12 @@ with tab3:
     st.write("Explore additional functionalities to enhance your news analysis.")
     
     
-    # Named Entity Recognition
+    # Named Entity Recognition of news content
     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..."):
+            #load the model
             ner_pipeline = pipeline("ner", grouped_entities=True)
             results = ner_pipeline(ner_text)
             entities = []
@@ -318,6 +325,7 @@ with tab3:
     summary_text = st.text_area("Enter news content to summarize:", height=150)
     if st.button("Generate Summary"):
         with st.spinner("Generating summary..."):
+            #load the summarization model
             summarizer = pipeline("summarization")
             summary = summarizer(summary_text, max_length=130, min_length=30)
             st.write(summary[0]['summary_text'])
@@ -329,6 +337,7 @@ with tab3:
     sentiment_text = st.text_area("Enter text for news content analysis:", height=100)
     if st.button("Analyze Sentiment"):
         with st.spinner("Analyzing sentiment..."):
+            #load the model
             sentiment_pipeline = pipeline("sentiment-analysis")
             result = sentiment_pipeline(sentiment_text)[0]
             st.write(f"Label: {result['label']}")