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import gradio as gr
from xlm_emo.classifier import EmotionClassifier
io1 = 'huggingface/MilaNLProc/xlm-emo-t'
def emotionClassifier(text):
ec = EmotionClassifier()
translation = ec.predict([text])
res=translation[0]
return res
'''
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/xlm-emo-t")
model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/xlm-emo-t")
'''
inputs = gr.inputs.Textbox(label="Text")
outpts=gr.outputs.Textbox(label="Output")
#gr.Interface(emotionClassifier, inputs=inputs,outputs=outpts, title='Emotions Analyser',theme='peach').launch()
gr.Interface.load(io1, inputs=inputs, title="Emotions Analyser",theme='peach').launch(enable_queue=True)
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