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import gradio as gr
from transformers import load_tool, ReactCodeAgent, HfEngine, Tool
from gradio_agentchatbot import (
AgentChatbot,
stream_from_transformers_agent,
ChatMessage,
)
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")
search_tool = Tool.from_langchain(load_tools(["serpapi"])[0])
llm_engine = HfEngine("meta-llama/Meta-Llama-3-70B-Instruct")
# Initialize the agent with both tools
agent = ReactCodeAgent(
tools=[image_generation_tool, search_tool], llm_engine=llm_engine
)
def interact_with_agent(prompt, messages):
messages.append(ChatMessage(role="user", content=prompt))
yield messages
for msg in stream_from_transformers_agent(agent, prompt):
messages.append(msg)
yield 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("Transformers Demo"):
gr.Markdown("# Chat with an LLM Agent 🤖 and see its thoughts 💭")
chatbot = AgentChatbot(
label="Agent",
avatar_images=[
None,
"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png",
],
)
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())
if __name__ == "__main__":
demo.launch()
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