Update pages/linkedin_extractor.py

pages/linkedin_extractor.py

@@ -459,34 +459,36 @@ def main():
             st.write("Chatbot Type:", "simple" if st.session_state.chatbot == "simple" else type(st.session_state.chatbot).__name__ if st.session_state.chatbot else "None")
             st.write("Chat History Length:", len(st.session_state.chat_history))
             st.write("Processing:", st.session_state.processing)
+
+    # Main content area - RESTRUCTURED LAYOUT
+    # First show extraction results
+    st.markdown("### 📊 Extraction Results")
     
-    # Main content area
-    col1, col2 = st.columns([1, 1])
+    if st.session_state.processing:
+        st.info("🔄 Processing LinkedIn data...")
     
-    with col1:
-        st.markdown("### 📊 Extraction Results")
+    elif st.session_state.extracted_data:
+        data = st.session_state.extracted_data
+        page_info = data['page_info']
+        content_blocks = data['content_blocks']
         
-        if st.session_state.processing:
-            st.info("🔄 Processing LinkedIn data...")
+        st.success("✅ Extraction Complete")
         
-        elif st.session_state.extracted_data:
-            data = st.session_state.extracted_data
-            page_info = data['page_info']
-            content_blocks = data['content_blocks']
-            
-            st.success("✅ Extraction Complete")
-            
-            # Display metrics
-            display_metrics(data)
-            
-            # Display page info
+        # Display metrics
+        display_metrics(data)
+        
+        # Display page info
+        col1, col2 = st.columns(2)
+        
+        with col1:
             st.markdown("#### 🏷️ Page Information")
             st.write(f"**Title:** {page_info['title']}")
             st.write(f"**URL:** {page_info['url']}")
             st.write(f"**Data Type:** {data['data_type'].title()}")
             st.write(f"**Content Blocks:** {len(content_blocks)}")
             st.write(f"**Extraction Time:** {data['extraction_time']}")
-            
+        
+        with col2:
             # Display sample content
             st.markdown("#### 📝 Sample Content")
             for i, block in enumerate(content_blocks[:3]):
@@ -495,94 +497,98 @@ def main():
             
             if len(content_blocks) > 3:
                 st.info(f"📄 And {len(content_blocks) - 3} more content blocks...")
-        
-        else:
-            st.info("""
-            👋 **Welcome to LinkedIn AI Analyzer!**
-            
-            **To get started:**
-            1. Select content type
-            2. Enter a LinkedIn URL or click a suggested company
-            3. Click "Extract & Analyze"
-            4. Chat with AI about the extracted content
-            
-            **Supported URLs:**
-            - 👤 Public Profiles
-            - 🏢 Company Pages  
-            - 📝 Public Posts
-            
-            **Features:**
-            - Content extraction
-            - Basic analysis
-            - Interactive chat
-            - Data insights
-            """)
     
-    with col2:
-        st.markdown("### 💬 AI Chat Analysis")
-        
-        has_extracted_data = st.session_state.extracted_data and st.session_state.extracted_data.get("status") == "success"
-        
-        if has_extracted_data:
-            st.success("💬 Chat ready! Ask questions about the LinkedIn 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 LinkedIn data...")
-            
-            if user_input:
-                # Add user message to history
-                st.session_state.chat_history.append({"role": "user", "content": user_input})
-                
-                # Generate response based on available capabilities
-                if st.session_state.chatbot == "simple" or st.session_state.chatbot is None:
-                    # Use simple analysis
-                    with st.spinner("🤔 Analyzing..."):
-                        response = simple_chat_analysis(user_input, st.session_state.extracted_data)
-                        st.session_state.chat_history.append({"role": "assistant", "content": response})
-                        st.rerun()
-                else:
-                    # Use AI chatbot
-                    with st.spinner("🤔 AI is analyzing..."):
-                        try:
-                            response = st.session_state.chatbot.invoke({"question": user_input})
-                            answer = response.get("answer", "I couldn't generate a response based on the available data.")
-                            st.session_state.chat_history.append({"role": "assistant", "content": answer})
-                            st.rerun()
-                        except Exception as e:
-                            error_msg = f"❌ AI Error: {str(e)}. Using simple analysis."
-                            simple_response = simple_chat_analysis(user_input, st.session_state.extracted_data)
-                            st.session_state.chat_history.append({"role": "assistant", "content": f"{error_msg}\n\n{simple_response}"})
-                            st.rerun()
+    else:
+        st.info("""
+        👋 **Welcome to LinkedIn AI Analyzer!**
+        
+        **To get started:**
+        1. Select content type
+        2. Enter a LinkedIn URL or click a suggested company
+        3. Click "Extract & Analyze"
+        4. Chat with AI about the extracted content
+        
+        **Supported URLs:**
+        - 👤 Public Profiles
+        - 🏢 Company Pages  
+        - 📝 Public Posts
+        
+        **Features:**
+        - Content extraction
+        - Basic analysis
+        - Interactive chat
+        - Data insights
+        """)
+
+    # Chat section - OUTSIDE of columns
+    st.markdown("---")
+    st.markdown("### 💬 AI Chat Analysis")
+    
+    has_extracted_data = st.session_state.extracted_data and st.session_state.extracted_data.get("status") == "success"
+    
+    if has_extracted_data:
+        st.success("💬 Chat ready! Ask questions about the LinkedIn 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 - only show when no chat history
+        if len(st.session_state.chat_history) == 0:
+            st.markdown("#### 💡 Try asking:")
+            suggestions = [
+                "Summarize the main information",
+                "What are the key highlights?",
+                "Analyze the professional focus",
+                "What insights can you extract?",
+                "Tell me about the experience"
+            ]
             
-            # Suggested questions
-            if len(st.session_state.chat_history) == 0:
-                st.markdown("#### 💡 Try asking:")
-                suggestions = [
-                    "Summarize the main information",
-                    "What are the key highlights?",
-                    "Analyze the professional focus",
-                    "What insights can you extract?",
-                    "Tell me about the experience"
-                ]
-                
-                for suggestion in suggestions:
+            cols = st.columns(len(suggestions))
+            for i, suggestion in enumerate(suggestions):
+                with cols[i]:
                     if st.button(suggestion, key=f"suggest_{suggestion}", use_container_width=True):
                         st.info(f"💡 Type in chat: '{suggestion}'")
+    
+    elif st.session_state.processing:
+        st.info("🔄 Extracting and processing LinkedIn data...")
+    
+    else:
+        st.info("🔍 Extract LinkedIn data to enable analysis")
+
+    # CHAT INPUT - MUST BE AT THE BOTTOM, OUTSIDE ANY CONTAINERS
+    if has_extracted_data:
+        user_input = st.chat_input("Ask about the LinkedIn data...")
         
-        elif st.session_state.processing:
-            st.info("🔄 Extracting and processing LinkedIn data...")
-        
-        else:
-            st.info("🔍 Extract LinkedIn data to enable analysis")
+        if user_input:
+            # Add user message to history
+            st.session_state.chat_history.append({"role": "user", "content": user_input})
+            
+            # Generate response based on available capabilities
+            if st.session_state.chatbot == "simple" or st.session_state.chatbot is None:
+                # Use simple analysis
+                with st.spinner("🤔 Analyzing..."):
+                    response = simple_chat_analysis(user_input, st.session_state.extracted_data)
+                    st.session_state.chat_history.append({"role": "assistant", "content": response})
+                    st.rerun()
+            else:
+                # Use AI chatbot
+                with st.spinner("🤔 AI is analyzing..."):
+                    try:
+                        response = st.session_state.chatbot.invoke({"question": user_input})
+                        answer = response.get("answer", "I couldn't generate a response based on the available data.")
+                        st.session_state.chat_history.append({"role": "assistant", "content": answer})
+                        st.rerun()
+                    except Exception as e:
+                        error_msg = f"❌ AI Error: {str(e)}. Using simple analysis."
+                        simple_response = simple_chat_analysis(user_input, st.session_state.extracted_data)
+                        st.session_state.chat_history.append({"role": "assistant", "content": f"{error_msg}\n\n{simple_response}"})
+                        st.rerun()
 
     # Features section
     st.markdown("---")