Update app.py

app.py

@@ -81,11 +81,33 @@ st.markdown("""
         background-color: #ffffff;
         color : #FF6347;
     }
+
+    .header {
+        display: flex;
+        align-items: center;
+        margin-bottom: 20px;
+    }
+    .header img {
+        height: 50px;  /* Adjust logo size */
+        margin-right: 10px;
+    }
+    .header h3 {
+        font-size: 24px;
+        color: #4CAF50;  /* Adjust the color of the header */
+        margin: 0;
+    
     </style>
     """, unsafe_allow_html=True)
 
+st.markdown("""
+    <div class="header">
+        <img src="https://via.placeholder.com/50x50?text=Logo" alt="Logo">
+        <h1>Daily Mirror News Analyzer</h1>
+    </div>
+""", unsafe_allow_html=True)
+
 # App title and description
-st.title("Daily Mirror News Analyzer")
+
 st.markdown("""
     Analyze news excerpts with our powerful AI tools:
     - Classify news articles into categories
@@ -238,29 +260,36 @@ with tab2:
     st.header("Question Answering Pipeline")
     st.write("Ask questions about news content and get answers from our AI model.")
     
-    
-    
     if uploaded_file is not None:
-        
+        # Load the CSV and prepare context for the model
         context = ' '.join(df['content'].tolist())  # Use predictions for Q&A
         st.write(f"Loaded {len(df)} news excerpts")
-
     else:
         st.warning("Please upload a CSV file.")
-   
     
+    # Input field for the question
     question = st.text_input("Enter your question:")
     
-    if st.button("Get Answer") and context and question:
-        with st.spinner("Searching for answers..."):
-            qa_pipeline = load_qa_model()
-            result = qa_pipeline(question=question, context=context)
-            
-            st.subheader("Answer")
-            st.success(result['answer'])
-            
-            st.subheader("Details")
-            st.write(f"Confidence: {result['score']:.2f}")
+    # Handle the "Get Answer" button
+    if st.button("Get Answer"):
+        if uploaded_file is None:
+            # Display an error message if no file is uploaded
+            st.error("Please upload a CSV file before asking a question.")
+        elif context and question:
+            # If both a file and a question are provided, answer the question
+            with st.spinner("Searching for answers..."):
+                qa_pipeline = load_qa_model()  # Ensure this function is defined elsewhere
+                result = qa_pipeline(question=question, context=context)
+                
+                # Display the answer and details
+                st.subheader("Answer")
+                st.success(result['answer'])
+                
+                st.subheader("Details")
+                st.write(f"Confidence: {result['score']:.2f}")
+        else:
+            st.error("Please enter a question.")
+
             
 
 with tab3: