app.py

import gradio as gr
from transformers import pipeline

# Load pretrained sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis")

def sentiment_analysis(text: str) -> dict:
    """
    Analyze sentiment using a transformer model, without lexicon.

    Args:
        text (str): The input text.

    Returns:
        dict: Polarity (mapped from model confidence), assessment, and subjectivity (heuristic).
    """
    result = sentiment_pipeline(text)[0]
    
    label = result['label']
    score = result['score']

    polarity = score if label == "POSITIVE" else -score
    assessment = "positive" if polarity > 0.2 else "negative" if polarity < -0.2 else "neutral"
    
    # Heuristic for subjectivity: high confidence implies more subjective
    subjectivity = round(score, 2)

    return {
        "polarity": round(polarity, 2),
        "subjectivity": subjectivity,
        "assessment": assessment
    }

# Gradio Interface
demo = gr.Interface(
    fn=sentiment_analysis,
    inputs=gr.Textbox(placeholder="Enter text to analyze...", lines=4),
    outputs=gr.JSON(),
    title="Transformer-Based Sentiment Analysis",
    description="Analyze sentiment using a pre-trained BERT model (no lexicons or rule-based logic)."
)

# Run on Gradio MCP
if __name__ == "__main__":
    demo.launch(mcp_server=True, share=True)