Update pages/linkedin_extractor.py

pages/linkedin_extractor.py

@@ -353,11 +353,20 @@ def main():
                 st.session_state.processing = False
         
         # Chat management
-        if st.session_state.chatbot and st.session_state.extracted_data:
+        if st.session_state.extracted_data and st.session_state.extracted_data.get("status") == "success":
             st.markdown("---")
             st.subheader("💬 Chat Management")
             if st.button("🗑️ Clear Chat History", type="secondary", use_container_width=True):
                 clear_chat_history()
+        
+        # Debug info (optional)
+        if st.checkbox("🔧 Show Debug Info", False):
+            st.markdown("### Debug Information")
+            st.write("Extracted Data:", st.session_state.extracted_data is not None)
+            st.write("Vectorstore:", st.session_state.vectorstore is not None)
+            st.write("Chatbot:", st.session_state.chatbot is not None)
+            st.write("Chat History Length:", len(st.session_state.chat_history))
+            st.write("Processing:", st.session_state.processing)
     
     # Main content area
     col1, col2 = st.columns([1, 1])
@@ -422,53 +431,93 @@ def main():
     with col2:
         st.markdown("### 💬 AI Chat Analysis")
         
-        if st.session_state.chatbot and st.session_state.extracted_data:
-            # Display chat history
-            for i, chat in enumerate(st.session_state.chat_history):
-                if chat["role"] == "user":
-                    st.markdown(f"**👤 You:** {chat['content']}")
-                elif chat["role"] == "assistant":
-                    st.markdown(f"**🤖 AI:** {chat['content']}")
-            
-            # Chat input
-            user_input = st.chat_input("Ask about the LinkedIn data...")
+        # Check if we have everything needed for chat
+        has_extracted_data = st.session_state.extracted_data and st.session_state.extracted_data.get("status") == "success"
+        has_vectorstore = st.session_state.vectorstore is not None
+        
+        if has_extracted_data and has_vectorstore:
+            # Initialize chatbot if not exists
+            if st.session_state.chatbot is None:
+                with st.spinner("🔄 Initializing AI Chat..."):
+                    st.session_state.chatbot = create_chatbot(st.session_state.vectorstore)
+                    if st.session_state.chatbot:
+                        st.success("✅ AI Chat ready!")
+                    else:
+                        st.error("❌ Failed to initialize AI chat")
             
-            if user_input:
-                # Add user message
-                st.session_state.chat_history.append({"role": "user", "content": user_input})
+            # Display chat interface when chatbot is ready
+            if st.session_state.chatbot:
+                st.success("💬 Chat with Mistral AI about the LinkedIn data!")
                 
-                # Generate AI response
-                with st.spinner("🤔 Mistral 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"❌ Error generating response: {str(e)}"
-                        st.session_state.chat_history.append({"role": "assistant", "content": error_msg})
-                        st.rerun()
-            
-            # Suggested questions
-            if not st.session_state.chat_history:
-                st.markdown("#### 💡 Suggested Questions")
-                suggestions = [
-                    "Summarize the main information from this page",
-                    "What are the key highlights or achievements?",
-                    "Analyze the business or professional focus",
-                    "What insights can you extract from this content?",
-                    "Provide a comprehensive overview"
-                ]
+                # 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 - ALWAYS VISIBLE when ready
+                user_input = st.chat_input("Ask about the LinkedIn data...")
+                
+                if user_input:
+                    # Add user message to history and display
+                    st.session_state.chat_history.append({"role": "user", "content": user_input})
+                    
+                    # Generate AI response
+                    with st.spinner("🤔 Mistral 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"❌ Error generating response: {str(e)}"
+                            st.session_state.chat_history.append({"role": "assistant", "content": error_msg})
+                            st.rerun()
                 
-                for suggestion in suggestions:
-                    if st.button(suggestion, key=f"suggest_{suggestion}", use_container_width=True):
-                        st.info(f"💡 Try asking: '{suggestion}'")
+                # 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 from this LinkedIn page",
+                        "What are the key highlights or achievements mentioned?",
+                        "Analyze the professional focus and expertise",
+                        "What insights can you extract from this content?",
+                        "Provide a comprehensive overview of this profile"
+                    ]
+                    
+                    for suggestion in suggestions:
+                        if st.button(suggestion, key=f"suggest_{suggestion}", use_container_width=True):
+                            st.info(f"💡 Type this in the chat: '{suggestion}'")
+            
+        elif has_extracted_data and not has_vectorstore:
+            st.warning("🔄 Processing data for AI analysis...")
+            # Try to process the data
+            with st.spinner("Preparing data for AI chat..."):
+                vectorstore, chunks = process_extracted_data(st.session_state.extracted_data)
+                if vectorstore:
+                    st.session_state.vectorstore = vectorstore
+                    st.rerun()
+                else:
+                    st.error("❌ Failed to process data for AI analysis")
+        
+        elif st.session_state.processing:
+            st.info("🔄 Extracting and processing LinkedIn data...")
         
-        elif st.session_state.extracted_data:
-            st.info("💬 Start a conversation with the AI assistant")
         else:
-            st.info("🔍 Extract LinkedIn data to enable AI analysis")
+            st.info("""
+            🔍 **Extract LinkedIn data to enable AI analysis**
+            
+            Once data is extracted, you can:
+            - Ask questions about the content
+            - Get summaries and insights  
+            - Analyze professional information
+            - Extract key achievements
+            - Discuss career highlights
+            """)
 
     # Features section
     st.markdown("---")