app.py

import json
import gradio as gr
from textblob import TextBlob

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

    Args:
        text (str): The text to analyze

    Returns:
        str: A JSON string containing polarity, subjectivity, and assessment
    """
    blob = TextBlob(text)
    sentiment = blob.sentiment
    
    result = {
        "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"
    }
    
    return json.dumps(result)

# Create the Gradio interface
demo = gr.Interface(
    fn=sentiment_analysis,
    inputs=gr.Textbox(placeholder="Enter text to analyze..."),
    outputs=gr.Textbox(),  
    title="Text Sentiment Analysis",
    description="Analyze the sentiment of text using TextBlob"
)

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

"""Understanding the Code
Let’s break down the key components:

1. Function Definition:

The sentiment_analysis function takes a text input and returns a dictionary
It uses TextBlob to analyze the sentiment
The docstring is crucial as it helps Gradio generate the MCP tool schema
Type hints (str and dict) help define the input/output schema

2. Gradio Interface:

gr.Interface creates both the web UI and MCP server
The function is exposed as an MCP tool automatically
Input and output components define the tool’s schema
The JSON output component ensures proper serialization

3. MCP Server:

Setting mcp_server=True enables the MCP server
The server will be available at http://localhost:7860/gradio_api/mcp/sse
You can also enable it using the environment variable:
Copied
"""