Update app.py

app.py

@@ -311,29 +311,47 @@ async def multi_agent_conversation(task_message: str, log_queue: queue.Queue, ap
 # -------------------- Process Generator and Human Input --------------------
 def process_conversation_generator(task_message: str, api_key: str, human_in_the_loop_event: threading.Event, human_input_queue: queue.Queue) -> Generator[str, None, None]:
     """
-    Wraps the conversation and yields log messages. Handles human input.
+    Wraps the conversation, yields log messages, and handles human input within a single thread.
     """
     log_q: queue.Queue = queue.Queue()
 
-    def run_conversation() -> None:
-        asyncio.run(multi_agent_conversation(task_message, log_q, api_key, human_in_the_loop_event, human_input_queue))
-
-    thread = threading.Thread(target=run_conversation)
-    thread.start()
+    # Run the multi-agent conversation *synchronously* within this function.
+    asyncio.run(multi_agent_conversation(task_message, log_q, api_key, human_in_the_loop_event, human_input_queue))
 
+    # Process the log queue and handle human-in-the-loop
     final_result = None
-    while thread.is_alive() or not log_q.empty():
+    while True:  # Loop indefinitely to handle multiple potential human feedback requests.
         try:
-            msg = log_q.get(timeout=0.1)
+            msg = log_q.get_nowait()  # Non-blocking get from the log queue.
             if isinstance(msg, tuple) and msg[0] == "result":
                 final_result = msg[1]
-                yield "Conversation complete."
+                yield "Conversation complete."  # Indicate completion.
+                break  # Exit the loop after processing the final result.
             else:
-                yield msg
+                yield msg  # Yield the log message.
         except queue.Empty:
-            continue
+            pass  # No log message available, continue checking for human input.
+
+
+        if human_in_the_loop_event.is_set():
+            yield "Waiting for human feedback..."  # Indicate waiting state.
+            try:
+                feedback_request = human_input_queue.get(
+                    timeout=0.1)  # Get the context/question for feedback.
+                human_interface = get_human_feedback(feedback_request)
+                yield gr.Textbox.update(visible=False), gr.update(visible=True)
+                human_feedback = human_input_queue.get(
+                    timeout=300)  # Wait (block) for human feedback, with a timeout.
+                human_in_the_loop_event.clear()  # Reset the event after getting feedback.
+                yield gr.Textbox.update(visible=True), human_interface.close()  # Hide feedback UI.
+
+
+            except queue.Empty:
+                pass
+        # Add a small sleep to avoid busy-waiting and reduce CPU usage.
+        time.sleep(0.1)
+
 
-    thread.join()
     if final_result:
         conv_text = "\n=== Conversation ===\n"
         for entry in final_result:
@@ -372,48 +390,7 @@ def multi_agent_chat(message: str, history: List[Any], openai_api_key: str = Non
     human_in_the_loop_event = threading.Event()
     human_input_queue = queue.Queue()  # Use a single queue for both requests and responses
 
-    # Start the conversation in a separate thread
-    conversation_thread = threading.Thread(
-        target=lambda: asyncio.run(
-            multi_agent_conversation(message, queue.Queue(), openai_api_key, human_in_the_loop_event, human_input_queue)
-        )
-    )
-    conversation_thread.start()
-
-
-    log_queue = queue.Queue()  # Local log queue for this chat instance
-    asyncio.run(multi_agent_conversation(message, log_queue, openai_api_key, human_in_the_loop_event, human_input_queue))
-
-
-
-    while conversation_thread.is_alive() or not log_queue.empty() or human_in_the_loop_event.is_set():
-        # Yield log messages
-        try:
-            log_message = log_queue.get_nowait()  # Non-blocking get
-            if isinstance(log_message, tuple) and log_message[0] == "result":
-                final_result_text = "\n=== Conversation ===\n"
-                for entry in log_message[1]:
-                  final_result_text+= f"[{entry['agent']}]: {entry['message']}\n\n"
-                yield final_result_text
-            else:
-              yield log_message
-        except queue.Empty:
-            pass
-
-        # Handle human feedback requests
-        if human_in_the_loop_event.is_set():
-            yield "Waiting for human feedback..."
-            try:
-                feedback_request = human_input_queue.get(timeout=0.1) # Get context for the request.
-                human_interface = get_human_feedback(feedback_request) #Show context in the input box
-                yield gr.Textbox.update(visible=False), gr.update(visible=True) # Show the human feedback interface.
-                human_in_the_loop_event.wait() # Wait until the event is cleared
-
-            except queue.Empty:
-                pass
-        await asyncio.sleep(0.1)  # Prevent busy-waiting
-
-    conversation_thread.join()
+    yield from process_conversation_generator(message, openai_api_key, human_in_the_loop_event, human_input_queue)
 
 
 # -------------------- Launch the Chatbot --------------------
@@ -437,4 +414,6 @@ dummy_iface = gr.Interface(lambda x:x, "textbox", "textbox")
 
 if __name__ == "__main__":
     demo = gr.TabbedInterface([iface, dummy_iface], ["Chatbot", "Dummy"])
-    demo.launch(share=True)
+    demo.launch(share=True)
+
+import time #Import the time module