Update pages/facebook_extractor.py

pages/facebook_extractor.py

@@ -681,135 +681,136 @@ def main():
                 st.session_state.chat_history = []
                 st.rerun()
     
-    # Main content
-    col1, col2 = st.columns([1, 1])
+    # Main content - RESTRUCTURED LAYOUT
+    st.header("📊 Extraction Results")
     
-    with col1:
-        st.header("📊 Extraction Results")
-        
-        if st.session_state.facebook_data:
-            data = st.session_state.facebook_data
-            page_info = data['page_info']
-            content_blocks = data['content_blocks']
-            source = data.get('source', 'unknown')
-            
-            if source == 'demo':
-                st.warning("📝 **Demo Data** - Realistic simulation (Facebook restrictions)")
-            else:
-                st.success("✅ **Real Data** - Successfully extracted")
-            
-            # Show processing mode
-            if st.session_state.processing_mode == "simple":
-                st.info("🔧 **Simple Analysis Mode** - Rule-based processing")
-            else:
-                st.info("🤖 **AI Analysis Mode** - Embedding-based processing")
-            
-            # Metrics
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                st.metric("Content Blocks", len(content_blocks))
-            with col2:
-                st.metric("Data Source", source.upper())
-            with col3:
-                st.metric("Analysis Mode", "AI" if st.session_state.processing_mode == "ai" else "Simple")
-            
-            # Page info
-            st.subheader("🏷️ Page Information")
-            st.write(f"**Title:** {page_info['title']}")
-            st.write(f"**URL Type:** {data['url_type']}")
-            st.write(f"**Description:** {page_info.get('description', 'No description')}")
-            
-            if page_info.get('member_count'):
-                st.write(f"**Members:** {page_info['member_count']}")
-            elif page_info.get('follower_count'):
-                st.write(f"**Followers:** {page_info['follower_count']}")
-            
-            st.write(f"**Access:** {page_info.get('access_note', 'Public content')}")
-            
-            # Content samples
-            st.subheader("📝 Content Analysis")
-            for i, block in enumerate(content_blocks):
-                with st.expander(f"Content {i+1} - {block['content_type']} ({block['word_count']} words)"):
-                    st.write(block['content'])
-                    st.caption(f"Public: {block['is_public_content']}")
+    if st.session_state.facebook_data:
+        data = st.session_state.facebook_data
+        page_info = data['page_info']
+        content_blocks = data['content_blocks']
+        source = data.get('source', 'unknown')
+        
+        if source == 'demo':
+            st.warning("📝 **Demo Data** - Realistic simulation (Facebook restrictions)")
+        else:
+            st.success("✅ **Real Data** - Successfully extracted")
         
+        # Show processing mode
+        if st.session_state.processing_mode == "simple":
+            st.info("🔧 **Simple Analysis Mode** - Rule-based processing")
         else:
-            st.info("""
-            ## 📘 Facebook Data Extractor
-            
-            **University Project Feature**
-            
-            **How it works:**
-            1. Enter any Facebook URL
-            2. System tries real data extraction
-            3. If blocked, uses **realistic demo data**
-            4. Choose between AI or Simple analysis
-            
-            **Analysis Modes:**
-            - 🤖 **AI Analysis**: Uses embeddings and Mistral AI
-            - 🔧 **Simple Analysis**: Rule-based (works without embeddings)
-            
-            **Perfect for demonstrating:**
-            - Social media data extraction concepts
-            - AI analysis capabilities
-            - Platform integration
-            - Error handling strategies
-            """)
+            st.info("🤖 **AI Analysis Mode** - Embedding-based processing")
+        
+        # Metrics
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric("Content Blocks", len(content_blocks))
+        with col2:
+            st.metric("Data Source", source.upper())
+        with col3:
+            st.metric("Analysis Mode", "AI" if st.session_state.processing_mode == "ai" else "Simple")
+        
+        # Page info
+        st.subheader("🏷️ Page Information")
+        st.write(f"**Title:** {page_info['title']}")
+        st.write(f"**URL Type:** {data['url_type']}")
+        st.write(f"**Description:** {page_info.get('description', 'No description')}")
+        
+        if page_info.get('member_count'):
+            st.write(f"**Members:** {page_info['member_count']}")
+        elif page_info.get('follower_count'):
+            st.write(f"**Followers:** {page_info['follower_count']}")
+        
+        st.write(f"**Access:** {page_info.get('access_note', 'Public content')}")
+        
+        # Content samples
+        st.subheader("📝 Content Analysis")
+        for i, block in enumerate(content_blocks):
+            with st.expander(f"Content {i+1} - {block['content_type']} ({block['word_count']} words)"):
+                st.write(block['content'])
+                st.caption(f"Public: {block['is_public_content']}")
     
-    with col2:
-        st.header("💬 Analysis Chat")
-        
-        if st.session_state.chatbot and st.session_state.facebook_data:
-            # Display chat history
-            for chat in st.session_state.chat_history:
-                if chat["role"] == "user":
-                    with st.chat_message("user"):
-                        st.write(chat['content'])
-                elif chat["role"] == "assistant":
-                    with st.chat_message("assistant"):
-                        st.write(chat['content'])
-            
-            # Chat input
-            user_input = st.chat_input("Ask about the Facebook data...")
-            
-            if user_input:
-                st.session_state.chat_history.append({"role": "user", "content": user_input})
-                
-                with st.spinner("🤔 Analyzing..."):
-                    try:
-                        if st.session_state.chatbot == "simple":
-                            # Use simple analysis
-                            response = simple_chat_analysis(user_input, st.session_state.facebook_data)
-                            st.session_state.chat_history.append({"role": "assistant", "content": response})
-                        else:
-                            # Use AI chatbot
-                            response = st.session_state.chatbot.invoke({"question": user_input})
-                            answer = response.get("answer", "I couldn't generate a response.")
-                            st.session_state.chat_history.append({"role": "assistant", "content": answer})
-                        st.rerun()
-                    except Exception as e:
-                        error_msg = f"Analysis Error: {str(e)}"
-                        st.session_state.chat_history.append({"role": "assistant", "content": error_msg})
-                        st.rerun()
+    else:
+        st.info("""
+        ## 📘 Facebook Data Extractor
+        
+        **University Project Feature**
+        
+        **How it works:**
+        1. Enter any Facebook URL
+        2. System tries real data extraction
+        3. If blocked, uses **realistic demo data**
+        4. Choose between AI or Simple analysis
+        
+        **Analysis Modes:**
+        - 🤖 **AI Analysis**: Uses embeddings and Mistral AI
+        - 🔧 **Simple Analysis**: Rule-based (works without embeddings)
+        
+        **Perfect for demonstrating:**
+        - Social media data extraction concepts
+        - AI analysis capabilities
+        - Platform integration
+        - Error handling strategies
+        """)
+
+    # Chat section - SEPARATE from main content
+    st.markdown("---")
+    st.header("💬 Analysis Chat")
+    
+    if st.session_state.chatbot and st.session_state.facebook_data:
+        # Display chat history
+        for chat in st.session_state.chat_history:
+            if chat["role"] == "user":
+                with st.chat_message("user"):
+                    st.write(chat['content'])
+            elif chat["role"] == "assistant":
+                with st.chat_message("assistant"):
+                    st.write(chat['content'])
+        
+        # Suggested questions when no history
+        if not st.session_state.chat_history:
+            st.subheader("💡 Try asking:")
+            suggestions = [
+                "What is this Facebook group/page about?",
+                "Summarize the main content and purpose",
+                "What kind of community is this?",
+                "Analyze the engagement and activity level"
+            ]
             
-            # Suggested questions
-            if not st.session_state.chat_history:
-                st.subheader("💡 Try asking:")
-                suggestions = [
-                    "What is this Facebook group/page about?",
-                    "Summarize the main content and purpose",
-                    "What kind of community is this?",
-                    "Analyze the engagement and activity level"
-                ]
-                
-                for suggestion in suggestions:
-                    if st.button(suggestion, key=f"fb_suggest_{suggestion}"):
-                        st.info(f"Type: '{suggestion}' in chat")
+            cols = st.columns(len(suggestions))
+            for i, suggestion in enumerate(suggestions):
+                with cols[i]:
+                    if st.button(suggestion, key=f"fb_suggest_{suggestion}", use_container_width=True):
+                        st.info(f"Type: '{suggestion}' in the chat below")
+    
+    elif st.session_state.facebook_data:
+        st.info("💬 Start chatting about the Facebook data")
+    else:
+        st.info("🔍 Extract Facebook data to enable analysis")
+
+    # CHAT INPUT - AT THE VERY BOTTOM, OUTSIDE ALL CONTAINERS
+    if st.session_state.chatbot and st.session_state.facebook_data:
+        user_input = st.chat_input("Ask about the Facebook data...")
         
-        elif st.session_state.facebook_data:
-            st.info("💬 Start chatting about the Facebook data")
-        else:
-            st.info("🔍 Extract Facebook data to enable analysis")
+        if user_input:
+            st.session_state.chat_history.append({"role": "user", "content": user_input})
+            
+            with st.spinner("🤔 Analyzing..."):
+                try:
+                    if st.session_state.chatbot == "simple":
+                        # Use simple analysis
+                        response = simple_chat_analysis(user_input, st.session_state.facebook_data)
+                        st.session_state.chat_history.append({"role": "assistant", "content": response})
+                    else:
+                        # Use AI chatbot
+                        response = st.session_state.chatbot.invoke({"question": user_input})
+                        answer = response.get("answer", "I couldn't generate a response.")
+                        st.session_state.chat_history.append({"role": "assistant", "content": answer})
+                    st.rerun()
+                except Exception as e:
+                    error_msg = f"Analysis Error: {str(e)}"
+                    st.session_state.chat_history.append({"role": "assistant", "content": error_msg})
+                    st.rerun()
 
 if __name__ == "__main__":
     main()