Add application file

requirements.txt

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+gradio[mcp]
+smolagents[mcp]

server.py

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+import gradio as gr
+import os
+
+from mcp import StdioServerParameters
+from smolagents import InferenceClientModel, CodeAgent, ToolCollection, MCPClient
+from smolagents import LiteLLMModel
+
+
+try:
+    mcp_client = MCPClient(
+        {"url": "http://localhost:7860/gradio_api/mcp/sse"} # This is the MCP Server we created in the previous section
+    )
+    tools = mcp_client.get_tools()
+
+    model = InferenceClientModel(token=os.getenv("HUGGINGFACE_API_TOKEN"))
+    # model = LiteLLMModel(
+    #     model_id="gemini/gemini-2.5-flash-preview-05-20",  # Gemini model name for LiteLLM
+    #     temperature=0.2,
+    #     api_key=os.environ["GOOGLE_API_KEY"]               # Must have access to Gemini via Vertex AI
+    # )
+    agent = CodeAgent(tools=[*tools], model=model)
+
+    demo = gr.ChatInterface(
+        fn=lambda message, history: str(agent.run(message)),
+        type="messages",
+        examples=["Prime factorization of 68"],
+        title="Agent with MCP Tools",
+        description="This is a simple agent that uses MCP tools to answer questions.",
+    )
+
+    demo.launch()
+finally:
+    mcp_client.disconnect()