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07dea3b 17fd164 1ef9b46 ab5957c 1ef9b46 ab5957c 17fd164 5afd05c 4cd4269 5afd05c 7f569cf 5afd05c 685b513 5afd05c 17fd164 bfe138c 07dea3b 17fd164 cb1c654 07dea3b 93b5658 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import json
import gradio as gr
import os
from mcp import StdioServerParameters
from smolagents import InferenceClientModel, CodeAgent, ToolCollection, MCPClient
with MCPClient(
{"url": "https://mdeevan-mcp-sentiment.hf.space/gradio_api/mcp/sse"}
) as tools:
# Tools from the remote server are available
print("\n".join(f"{t.name}: {t.description}" for t in tools))
mcp_client = MCPClient(
{"url": "https://mdeevan-mcp-sentiment.hf.space/gradio_api/mcp/sse"} # This is the MCP Client we created in the previous section
)
tools = mcp_client.get_tools()
model = InferenceClientModel(token=os.getenv("HF_TOKEN"))
agent = CodeAgent(tools=[*tools], model=model)
demo = gr.ChatInterface(
fn=lambda message, history: str(agent.run(message)),
type="messages",
examples=["today weather is nice"],
title="Agent with MCP Tools",
description="This is a simple agent that uses MCP tools to answer questions."
)
# if __name__ == "__main__":
demo.launch()
# Create gradio interface
# demo = gr.Interface(
# fn = sentiment_analysis,
# inputs = gr.Textbox(placeholder="Enter trext to analyze sentiments ..."),
# outputs = gr.Textbox(),
# title = "Text Sentiment Analysis",
# description = "Analyze the sentiment of text using TextBlob"
# )
# # Launch the interface and MCP server
# demo.launch(mcp_server=True) |