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# import gradio as gr
# from transformers import pipeline
# classifier = pipeline('text-classification', model='el-filatova/clasificador-tweet-sentiment')
# def predict(text):
# return classifier(text)
# iface = gr.Interface(fn=predict, inputs=[gr.Textbox(value="ah, what a pang of aching sharp surprise")], outputs="text")
# iface.launch()
import gradio as gr
from transformers import pipeline
classifier = pipeline('text-classification', model='el-filatova/clasificador-tweet-sentiment')
def predict(text):
prediction = classifier(text)
score = int(round(prediction[0]['score'] * 100))
if prediction[0]['label'] == "LABEL_0":
output = f"This tweet carries a negative sentiment with a confidence level of {score}%."
elif prediction[0]['label'] == "LABEL_1":
output = f"This tweet carries a neutral sentiment with a confidence level of {score}%."
else:
output = f"This tweet carries a positive sentiment with a confidence level of {score}%."
return output
iface = gr.Interface(fn=predict, inputs=[gr.Textbox(value="ah, what a pang of aching sharp surprise")], outputs="text")
iface.launch()
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