app_beta.py

import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline


class EmotionClassifier:
    def __init__(self, model_name: str):
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.pipeline = pipeline(
            "text-classification",
            model=self.model,
            tokenizer=self.tokenizer,
            return_all_scores=True,
        )

    def predict(self, input_text: str):
        pred = self.pipeline(input_text)[0]
        result = {
            "Sadness 😭": pred[0]["score"],
            "Joy 😂": pred[1]["score"],
            "Love 😍": pred[2]["score"],
            "Anger 😠": pred[3]["score"],
            "Fear 😨": pred[4]["score"],
            "Surprise 😲": pred[5]["score"],
        }
        return result


def main():
    model = EmotionClassifier("bhadresh-savani/bert-base-uncased-emotion")
    iface = gr.Interface(
        fn=model.predict,
        inputs=gr.inputs.Textbox(
            lines=3,
            placeholder="Escribe una frase (en inglés) que contenga algún tipo de emoción",
            label="Texto de entrada",
        ),
        outputs="label",
        title="Clasificador de Emociones",
        examples=[
            "I get so down when I'm alone",
            "I believe that today everything will work out",
            "It was so dark there I was afraid to go",
            "I loved the gift you gave me",
            "I was very surprised by your presentation.",
        ],
    )

    iface.launch()


if __name__ == "__main__":
    main()