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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()
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