app.py

import gradio as gr
import emoji
from simpletransformers.classification import ClassificationModel, ClassificationArgs

CLASS_NAMES = ['angry_face', 'crying_face', 'face_with_crossed-out_eyes', 'face_with_open_mouth', 'flushed_face', 'grinning_face_with_smiling_eyes', 'loudly_crying_face', 'pouting_face', 'slightly_smiling_face', 'smiling_face_with_smiling_eyes', 'sparkles', 'tired_face']

model_args = ClassificationArgs()
model_original = ClassificationModel(
    'auto',
    'xiongjie/face-expression-ja',
    use_cuda=True,
    args=model_args,
    num_labels=len(emoticon_dataset.CLASS_NAMES)
)

def predict_emoticon(text):
    return emoji.emojize(":" + model_onnx.predict([text])[0][0] + ":")

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()