Update app.py

app.py

@@ -77,13 +77,9 @@ def update_model_dropdown(provider):
         return gr.Dropdown(choices=OPENROUTER_MODELS, value=OPENROUTER_MODELS[0], label="Target Engine Architecture")
 
 # --- Common LLM API Request Orchestrator ---
-def call_llm(provider, api_key, model_choice, system_prompt, user_message, chat_history_format=None):
+def call_llm(provider, api_key, model_choice, system_prompt, history_messages):
     messages = [{"role": "system", "content": system_prompt}]
-    
-    if chat_history_format:
-        messages.extend(chat_history_format)
-    else:
-        messages.append({"role": "user", "content": user_message})
+    messages.extend(history_messages)
 
     if provider == "Groq":
         client = Groq(api_key=api_key)
@@ -101,8 +97,6 @@ def call_llm(provider, api_key, model_choice, system_prompt, user_message, chat_
 
     elif provider == "OpenRouter":
         headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
-        
-        # Maps user-facing selection directly to their OpenRouter global endpoints
         openrouter_model_map = {
             "nvidia/nemotron-3.5-content-safety:free": "nvidia/nemotron-3.5-content-safety:free",
             "qwen/qwen3.7-plus": "qwen/qwen3.7-plus",
@@ -138,7 +132,8 @@ def execute_ai_query(provider, api_key, model_choice, user_question):
     )
 
     try:
-        sql_query = call_llm(provider, api_key, model_choice, system_prompt, user_question)
+        query_as_history = [{"role": "user", "content": user_question}]
+        sql_query = call_llm(provider, api_key, model_choice, system_prompt, query_as_history)
         sql_query = sql_query.replace("```sql", "").replace("```", "").replace("`", "").strip()
 
         conn = sqlite3.connect(DB_NAME)
@@ -148,13 +143,21 @@ def execute_ai_query(provider, api_key, model_choice, user_question):
     except Exception as e:
         return None, f"❌ Execution Failed: {str(e)}"
 
-# --- Tab 2: Conversation & Data Append Agent Core Logic ---
-def conversation_and_commit_agent(chat_history, provider, api_key, model_choice, user_msg):
-    if not api_key.strip():
-        chat_history.append({"role": "assistant", "content": "⚠️ Authentication missing. Please input your API Key on the left menu pane."})
-        return chat_history, ""
+# --- Tab 2: Decoupled Multi-Step Chatbot Logic ---
+def append_user_message(chat_history, user_msg):
     if not user_msg.strip():
         return chat_history, ""
+    # Instantly pushes user text to client viewport layout canvas
+    chat_history.append({"role": "user", "content": user_msg})
+    return chat_history, ""
+
+def generate_agent_response(chat_history, provider, api_key, model_choice):
+    if not chat_history:
+        return chat_history
+    
+    if not api_key.strip():
+        chat_history.append({"role": "assistant", "content": "⚠️ Authentication missing. Please input your API Key on the left menu pane."})
+        return chat_history
 
     system_prompt = (
         "You are a helpful conversational assistant and workflow coordinator for the Strides Pharma AI operational framework.\n"
@@ -168,14 +171,13 @@ def conversation_and_commit_agent(chat_history, provider, api_key, model_choice,
         "If some schema details are completely absent, converse politely with the client to verify those remaining parameters before adding the string tag marker."
     )
 
+    # Format pipeline matching exact schema constraints
     formatted_history = []
     for turn in chat_history:
         formatted_history.append({"role": turn["role"], "content": turn["content"]})
-    formatted_history.append({"role": "user", "content": user_msg})
 
     try:
-        raw_response = call_llm(provider, api_key, model_choice, system_prompt, "", chat_history_format=formatted_history)
-        
+        raw_response = call_llm(provider, api_key, model_choice, system_prompt, formatted_history)
         cleaned_response = raw_response
         database_committed_alert = ""
         
@@ -196,20 +198,17 @@ def conversation_and_commit_agent(chat_history, provider, api_key, model_choice,
                 conn.commit()
                 conn.close()
                 
-                database_committed_alert = f"\n\n⚙️ **[SYSTEM UPDATE]:** Successfully appended task '{data_payload.get('task_name')}' to the internal server database master record structure."
+                database_committed_alert = f"\n\n⚙️ **[SYSTEM UPDATE]:** Successfully appended task '{data_payload.get('task_name')}' to the database structural master records."
             except Exception as inner_err:
-                database_committed_alert = f"\n\n⚠️ **[SYSTEM NOTICE]:** Captured structure request token, but insertion execution aborted due to tracking parsing discrepancies: {str(inner_err)}"
+                database_committed_alert = f"\n\n⚠️ **[SYSTEM NOTICE]:** Captured token parameters, but update aborted due to structural parsing errors: {str(inner_err)}"
 
         final_display_text = cleaned_response + database_committed_alert
-        
-        chat_history.append({"role": "user", "content": user_msg})
         chat_history.append({"role": "assistant", "content": final_display_text})
-        return chat_history, ""
+        return chat_history
 
     except Exception as e:
-        chat_history.append({"role": "user", "content": user_msg})
         chat_history.append({"role": "assistant", "content": f"❌ API Connection Failure: {str(e)}"})
-        return chat_history, ""
+        return chat_history
 
 # --- Interface Layout Configuration ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
@@ -222,40 +221,52 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             provider_select = gr.Dropdown(choices=["Groq", "OpenRouter"], value="Groq", label="API Gateway Provider")
             token_input = gr.Textbox(label="User API Secret Key", type="password", placeholder="gsk_... or sk-or-...")
             
-            # This component gets updated dynamically by the event handler below
             model_select = gr.Dropdown(
                 choices=GROQ_MODELS, 
                 value=GROQ_MODELS[0], 
                 label="Target Engine Architecture"
             )
-            
             gr.Markdown("✨ **Global Platform Database State Schema:**\n- `phase` (Discovery, Data Prep, Modeling, Validation)\n- `task_name` (Structural workflow description string)\n- `owner` (Assigned personnel scientist name)\n- `timeline` (Expected operational delivery time frames)\n- `priority` (High, Medium, Low)")
             
         with gr.Column(scale=2):
             with gr.Tabs():
                 
+                # Tab 1: Decoupled Multi-Step Chatbot
                 with gr.TabItem("🤖 Interactive Data Contributor Chatbot"):
                     gr.Markdown("### Conversational Contributor Agent")
-                    gr.Markdown("Chat with this engine normally, or tell it to log a brand-new task assignment milestone directly into the active SQLite infrastructure.")
+                    gr.Markdown("Chat with this engine normally, or log a brand-new task assignment milestone directly into the active SQLite infrastructure.")
                     
                     chatbot_viewport = gr.Chatbot(type="messages", label="Operational History Workspace")
                     chat_input = gr.Textbox(placeholder="Say hello, or submit task details to log...", label="Your Message")
                     send_btn = gr.Button("Submit Message", variant="primary")
                     
+                    # Decoupled sequence logic prevents thread locking:
+                    # Step A: Push user input to UI frame immediately & clear the input box
+                    # Step B: Call backend API to obtain assistant response
                     send_btn.click(
-                        fn=conversation_and_commit_agent,
-                        inputs=[chatbot_viewport, provider_select, token_input, model_select, chat_input],
+                        fn=append_user_message,
+                        inputs=[chatbot_viewport, chat_input],
                         outputs=[chatbot_viewport, chat_input]
+                    ).then(
+                        fn=generate_agent_response,
+                        inputs=[chatbot_viewport, provider_select, token_input, model_select],
+                        outputs=[chatbot_viewport]
                     )
+                    
                     chat_input.submit(
-                        fn=conversation_and_commit_agent,
-                        inputs=[chatbot_viewport, provider_select, token_input, model_select, chat_input],
+                        fn=append_user_message,
+                        inputs=[chatbot_viewport, chat_input],
                         outputs=[chatbot_viewport, chat_input]
+                    ).then(
+                        fn=generate_agent_response,
+                        inputs=[chatbot_viewport, provider_select, token_input, model_select],
+                        outputs=[chatbot_viewport]
                     )
                 
+                # Tab 2: Natural-Language-to-SQL Inquisitor
                 with gr.TabItem("🔎 SQL Inquisitor Desk"):
                     gr.Markdown("### Natural Language SQL Query Engine")
-                    query_input = gr.Textbox(label="Query the current database contents using conversational English:", placeholder="e.g., Show me all records sorted by priority status")
+                    query_input = gr.Textbox(label="Query current database contents using conversational English:", placeholder="e.g., Show me all records sorted by priority status")
                     query_btn = gr.Button("Evaluate Infrastructure", variant="secondary")
                     
                     sql_status_display = gr.Markdown()
@@ -267,8 +278,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                         outputs=[output_data_table, sql_status_display]
                     )
 
-    # --- REACTIVE EVENT LISTENER ---
-    # Whenever the provider dropdown changes, change the choices of the model select dropdown
+    # Provider dropdown change handler
     provider_select.change(
         fn=update_model_dropdown,
         inputs=[provider_select],