vocca_ai/sentiment.py

from transformers import pipeline

# Load a better sentiment model
sentiment_model = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")

def analyze_sentiment(text):
    """
    Uses a specialized sentiment model better suited for medical text.
    """
    result = sentiment_model(text)[0]["label"]

    if result.lower() == "positive":
        return "Positive"
    elif result.lower() == "negative":
        return "Concerned"  # Replace "Negative" with a more medically appropriate term
    return "Neutral"

# Example Usage
if __name__ == "__main__":
    sample_text = "I've been feeling really weak and dizzy for the past few days."
    print(f"Sentiment: {analyze_sentiment(sample_text)}")