mcp-sentiment / app.py
monochandan
Initial Commit
8a0df75
import gradio as gr
from textblob import TextBlob
def sentiment_analysis(text: str) -> dict: # Type hints (str and dict) help define the input/output schema
# The docstring is crucial as it helps Gradio generate the MCP tool schema
# This helps Gradio generate accurate MCP tool schemas
"""
Analyze the sentiment of the give text.txt
Args:
text (str): The text to analyze
Returns:
dict: A dictionary contains polarity, subjectivity, and assessment
"""
blob = TextBlob(text)
sentiment = blob.sentiment
return {
"polarity": round(sentiment.polarity, 2),
"subjectivity": round(sentiment.subjectivity, 2),
"assessment": "positivs" if sentiment.polarity > 0 else "negative" if sentiment.polarity < 0 else "neutral" # if elif else
}
# gradio interface
demo = gr.Interface( # gr.interafce creaets both the web UI and MCP server
fn=sentiment_analysis, # function as mcp tool automatically
inputs=gr.Textbox(placeholder="Enter a text to analyze"), # define the tools schema
outputs=gr.JSON(), # defin the tools schema, JSON output component ensure proper serialization.
title="Sentiment Analysis",
description="Text sentiemnt with Text Blob"
)
if __name__ == "__main__":
demo.launch(mcp_server=True) # enables the mcp server