app.py

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()