from app.model_loader import model_registry

class TextPredictor:

    def __init__(self):
        model_registry.initialize()
        self.sentiment_model = model_registry.get("sentiment")
        self.emotion_model = model_registry.get("emotion")


        self.emotion_emojis = {
            "joy": "😊",
            "anger": "😠",
            "sadness": "😞",
            "fear": "😨",
            "love": "❤️",
            "surprise": "😲",
            "disgust": "🤢",
            "neutral": "😐"
        }

    def predict(self, text: str):

        if not text or not isinstance(text, str):
            raise ValueError("Input text must be a non-empty string.")


        cleaned_text = text.strip()

        sentiment_result = self.sentiment_model(cleaned_text)[0]
        sentiment_label = sentiment_result["label"].capitalize()

        emotion_result = self.emotion_model(cleaned_text)[0]
        emotion_label = emotion_result["label"].lower()
        emotion_with_emoji = f"{emotion_label} {self.emotion_emojis.get(emotion_label, '')}"

        result = {
            "input_text": text,
            "cleaned_text": cleaned_text,
            "sentiment": sentiment_label,
            "emotion": emotion_with_emoji
        }

        return result

text_predictor = TextPredictor()