add: hacky agentic chatbot

agent_chat_example.py

@@ -65,13 +65,13 @@ image_generation_tool = Tool.from_space(  # type: ignore
     api_name="/infer",
 )
 
-testing_tool = GenerateQuestionsTool()
+# testing_tool = GenerateQuestionsTool()
 
-question_generator = Tool.from_gradio(testing_tool)
+# question_generator = Tool.from_gradio(testing_tool)
 
 llm_engine = HfApiEngine("Qwen/Qwen2.5-Coder-32B-Instruct")
 # Initialize the agent with both tools and engine
-agent = ReactCodeAgent(tools=[question_generator], llm_engine=llm_engine)
+agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)
 
 
 def interact_with_agent(prompt, history):

sample_chat.py

@@ -1,12 +1,19 @@
 import gradio as gr
 from gradio import ChatMessage
 import time
+import asyncio
+from functools import partial
+import random
+import logging
 
-sleep_time = 0.2
+logging.basicConfig(level=logging.INFO)
+
+sleep_time = random.randint(1, 3)
 
 # ... existing code ...
 
-def get_client_information_questions():
+async def get_client_information_questions():
+    await asyncio.sleep(10)  # Convert to async sleep
     """Return client information gathering questions."""
     return """
     # Client Information Gathering Questions
@@ -33,13 +40,16 @@ def get_client_information_questions():
     11. Are there any parts of the process that are particularly time-consuming/ prone to error?
     """
 
-def simulate_thinking_chat(message, history):
+async def simulate_thinking_chat(message, history):
+    logging.info(f"Received message: {message}")
+    logging.info(f"Initial history: {history}")
     start_time = time.time()
     response = ChatMessage(
         content="",
         metadata={"title": "_Thinking_ step-by-step", "id": 0, "status": "pending"}
     )
-    yield response
+    yield response, ""
+    # yield response
 
     thoughts = [
         "First, I need to understand the core aspects of the query...",
@@ -48,43 +58,57 @@ def simulate_thinking_chat(message, history):
         "Finally, structuring the response for clarity and completeness..."
     ]
 
+    # Create a task for getting client information
+    client_info_task = asyncio.create_task(get_client_information_questions())
+    
+    # Keep showing thoughts until client_info_task is done
     accumulated_thoughts = ""
-    for thought in thoughts:
-        time.sleep(sleep_time)
+    thought_index = 0
+    while not client_info_task.done():
+        thought = thoughts[thought_index % len(thoughts)]
+        thought_index += 1
+        
+        await asyncio.sleep(sleep_time)
         accumulated_thoughts += f"- {thought}\n\n"
         response.content = accumulated_thoughts.strip()
-        yield response
+        yield response, ""
+        # yield response
+
+    # Get the result from the completed task
+    client_info = await client_info_task
 
     response.metadata["status"] = "done"
     response.metadata["duration"] = time.time() - start_time
-    yield response
+    yield response, ""
+    # yield response
 
-    # Prepare the final response list
     response_list = [
         response,
-        ChatMessage(
-            content=get_client_information_questions()
-        )
+        ChatMessage(content=client_info)
     ]
     print(f"Message: {message},\n Len: {len(history)}, \nHistory: {history}")
 
-    # Print the response list to the console
-    # print(response_list)
-
-    yield response_list
-
-chatbot = gr.Chatbot(height=650 ,elem_classes=["chatbot-container"])
+    # yield response_list
+    yield response_list, client_info
 
+chatbot = gr.Chatbot(height=650 ,elem_classes=["chatbot-container"], label="Project Questions")
 
 with gr.Blocks(fill_height=True) as demo:
-    gr.ChatInterface(
-        simulate_thinking_chat,
-        title="Thinking LLM Chat Interface 🤔",
-        type="messages",
-        fill_height=True,
-        chatbot= chatbot,
-        # show_progress= 'minimal',
-        # save_history= True
-    )
+    with gr.Row():
+        with gr.Column(scale=1):
+            # output = gr.Textbox(label="Output")
+            current_question = gr.Textbox(label="Edit Area", lines=30)
+            # submit_btn = gr.Button("Submit")
+            # clear_btn = gr.Button("Clear Chat")
+        with gr.Column(scale=1):
+            gr.ChatInterface(
+                simulate_thinking_chat,
+                chatbot= chatbot,
+                type="messages",
+                fill_height=True,
+                additional_outputs= [current_question]
+                # show_progress= 'minimal',
+                # save_history= True
+            )
 
 demo.launch()