app.py

import json
import gradio as gr
from textblob import TextBlob

def call_model(text: str, model_type: str = "textblob"):
    """
    Return raw sentiment analysis output from selected model.
    """
    if model_type == "textblob":
        blob = TextBlob(text)
        return blob.sentiment  # returns namedtuple(polarity, subjectivity)

    elif model_type == "transformer":
        # Placeholder for future integration
        return {"label": "POSITIVE", "score": 0.98}

    else:
        raise ValueError(f"Unsupported model type: {model_type}")
    

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
    """
    sentiment = call_model(text, model_type="textblob")
    
    # Handle TextBlob response (namedtuple)
    if isinstance(sentiment, tuple):  # Simple check for TextBlob style
        polarity = round(sentiment.polarity, 2)
        subjectivity = round(sentiment.subjectivity, 2)
        assessment = (
            "positive" if polarity > 0 else
            "negative" if polarity < 0 else
            "neutral"
        )
        result = {
            "polarity": polarity,
            "subjectivity": subjectivity,
            "assessment": assessment
        }
    else:
        # Future: handle ML-based sentiment output
        result = sentiment

    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(),  # Changed from gr.JSON() to 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)