Update app.py

app.py

@@ -7,12 +7,15 @@ from gradio_agentchatbot import (
 )
 from dotenv import load_dotenv
 from langchain.agents import load_tools
+from langchain_demo import agent_executor as langchain_agent
+
 from pathlib import Path
 
 current_dir = Path(__file__).parent
 
 load_dotenv()
 
+
 # Import tool from Hub
 image_generation_tool = load_tool("m-ric/text-to-image")
 
@@ -34,9 +37,25 @@ def interact_with_agent(prompt, messages):
     yield messages
 
 
+async def interact_with_langchain_agent(prompt, messages):
+    messages.append(ChatMessage(role="user", content=prompt))
+    yield messages
+    async for chunk in langchain_agent.astream(
+        {"input": prompt}
+    ):
+        if "steps" in chunk:
+            for step in chunk["steps"]:
+                messages.append(ChatMessage(role="assistant", content=step.action.log,
+                                  thought_metadata={"tool_name": step.action.tool}))
+                yield messages
+        if "output" in chunk:
+            messages.append(ChatMessage(role="assistant", content=chunk["output"]))
+            yield messages
+
+
 with gr.Blocks() as demo:
     with gr.Tabs():
-        with gr.Tab("Demo"):
+        with gr.Tab("Transformers Demo"):
             gr.Markdown("# Chat with an LLM Agent 🤖 and see its thoughts 💭")
             chatbot = AgentChatbot(
                 label="Agent",
@@ -47,6 +66,17 @@ with gr.Blocks() as demo:
             )
             text_input = gr.Textbox(lines=1, label="Chat Message")
             text_input.submit(interact_with_agent, [text_input, chatbot], [chatbot])
+        with gr.Tab("Langchain Demo"):
+            gr.Markdown("# Chat with a LangChain Agent 🦜⛓️ and see its thoughts 💭")
+            chatbot_2 = AgentChatbot(
+                label="Agent",
+                avatar_images=[
+                    None,
+                    "https://em-content.zobj.net/source/twitter/141/parrot_1f99c.png",
+                ],
+            )
+            input_2 = gr.Textbox(lines=1, label="Chat Message")
+            input_2.submit(interact_with_langchain_agent, [input_2, chatbot_2], [chatbot_2])
         with gr.Tab("Docs"):
             gr.Markdown(Path(current_dir / "docs.md").read_text())