Create app.py

app.py

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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()