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import os
import gradio as gr
from transformers import Tool
from transformers.agents import (
ReactCodeAgent,
ReactJsonAgent,
HfApiEngine,
ManagedAgent,
stream_to_gradio,
)
from transformers.agents.search import DuckDuckGoSearchTool
import requests
from markdownify import markdownify as md
from requests.exceptions import RequestException
import re
import spaces
from huggingface_hub import login
# Read the Hugging Face API token from the environment variable
hf_token = os.getenv("HF_TOKEN")
# Authenticate with the Hugging Face API
login(token=hf_token)
class VisitWebpageTool(Tool):
"""
A tool to visit a webpage and return its content as a markdown string.
"""
name = "visit_webpage"
description = "Visits a webpage at the given URL and returns its content as a markdown string."
inputs = {
"url": {
"type": "text",
"description": "The URL of the webpage to visit.",
}
}
output_type = "text"
def forward(self, url: str) -> str:
"""
Fetch the webpage content and convert it to markdown.
"""
try:
response = requests.get(url)
response.raise_for_status()
markdown_content = md(response.text).strip()
markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)
return markdown_content
except RequestException as e:
return f"Error fetching the webpage: {str(e)}"
except Exception as e:
return f"An unexpected error occurred: {str(e)}"
# Initialize the LLM engine with the Hugging Face API token
llm_engine = HfApiEngine(model="meta-llama/Meta-Llama-3.1-70B-Instruct")
# Initialize the web agent with necessary tools and engine
web_agent = ReactJsonAgent(
tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
llm_engine=llm_engine,
max_iterations=10,
)
# Create a managed web agent
managed_web_agent = ManagedAgent(
agent=web_agent,
name="search_agent",
description="Runs web searches for you. Give it your query as an argument.",
)
# Initialize the manager agent with the managed web agent
manager_agent = ReactCodeAgent(
tools=[],
llm_engine=llm_engine,
managed_agents=[managed_web_agent],
additional_authorized_imports=["time", "datetime"],
)
@spaces.GPU(duration=120)
def interact_with_agent(task):
"""
Interact with the agent and stream the responses to Gradio.
"""
messages = []
messages.append(gr.ChatMessage(role="user", content=task))
yield messages
for msg in stream_to_gradio(manager_agent, task):
messages.append(msg)
yield messages + [
gr.ChatMessage(role="assistant", content="⏳ Task not finished yet!")
]
yield messages
# Create the Gradio interface
with gr.Blocks() as demo:
text_input = gr.Textbox(lines=1, label="Chat Message", value="How many years ago was Stripe founded?")
submit = gr.Button("Run multi-agent system!")
chatbot = gr.Chatbot(
label="Agent",
type="messages",
avatar_images=(
None,
"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png",
),
)
submit.click(interact_with_agent, [text_input], [chatbot])
if __name__ == "__main__":
demo.launch() |