from textblob import TextBlob

import gradio as gr

from mcp.client.stdio import StdioServerParameters
from smolagents import InferenceClientModel, CodeAgent, ToolCollection
from smolagents.mcp_client import MCPClient

def sentiment_analysis(text: str) -> dict:
    """
    Analyze the sentiment of the given text.

    Args:
        text (str): The text to analyze

    Returns:
        dict: A dictionary containing polarity, subjectivity, and assessment
    """
    blob = TextBlob(text)
    sentiment = blob.sentiment
    
    return {
        "polarity": round(sentiment.polarity, 2),  # -1 (negative) to 1 (positive)
        "subjectivity": round(sentiment.subjectivity, 2),  # 0 (objective) to 1 (subjective)
        "assessment": "positive" if sentiment.polarity > 0 else "negative" if sentiment.polarity < 0 else "neutral"
    }



mcp_client = MCPClient(
    {"url": "https://slimanemakh-mcp-course.hf.space/gradio_api/mcp/sse"}
)
tools = mcp_client.get_tools()

model = InferenceClientModel()
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.",
    messages=[],
)


# Launch the interface and MCP server
if __name__ == "__main__":
    demo.launch(mcp_server=True)