app.py

import gradio as gr
import os
from smolagents import CodeAgent, InferenceClientModel
from smolagents.mcp_client import MCPClient

# Initialize the model using Hugging Face's serverless inference
# Ensure you have an HF_TOKEN set in your Space's Secrets
model = InferenceClientModel("meta-llama/Llama-3.2-3B-Instruct")

mcp_client = None

try:
    # Initialize the MCP Client pointing to the Gradio SSE endpoint
    mcp_client = MCPClient(
        {"url": "https://abidlabs-mcp-tools2.hf.space/gradio_api/mcp/sse"}
    )

    # Retrieve tools from the MCP server
    tools = mcp_client.get_tools()
    
    # Create the agent with the retrieved tools
    agent = CodeAgent(tools=[*tools], model=model, add_base_tools=True)

    def call_agent(message, history):
        try:
            # Run the agent and return the final answer as a string
            response = agent.run(message)
            return str(response)
        except Exception as error:
            return f"Agent Error: {str(error)}"

    # Set up the Gradio Chat Interface
    demo = gr.ChatInterface(
        fn=call_agent,
        type="messages",
        examples=["What is the prime factorization of 2026?"],
        title="SKT AI: Agent with MCP Tools",
        description="This agent uses the Model Context Protocol (MCP) to access external tools.",
    )

except Exception as e:
    print(f"Initialization Error: {e}")
    # Fallback demo in case of connection issues
    demo = gr.Interface(fn=lambda x: f"Initialization failed: {e}", inputs="text", outputs="text")

if __name__ == "__main__":
    try:
        demo.launch()
    finally:
        if mcp_client is not None:
            mcp_client.stop()