| import gradio as gr | |
| from transformers import pipeline | |
| classifier3 = pipeline('text-classification', model='nlmaldonadog/clasificador-rotten-tomatoes-bert-base-uncased') | |
| classifier4 = pipeline('text-classification', model='nlmaldonadog/clasificador-rotten-tomatoes-xlnet-base-cased') | |
| def val_label(lab): | |
| if lab == "LABEL_1": | |
| return "Positive" | |
| return "Negative" | |
| def predict(text): | |
| m3 = classifier3(text)[0] | |
| prediction = f"Model 3: {val_label(m3['label'])} with {m3['score']} of confidence.\nModel 4: {val_label(m4['label'])} with {m4['score']} of confidence." | |
| return prediction | |
| ifg = gr.Interface(fn=predict, inputs=[gr.Textbox(placeholder='Escribe aquí...')], outputs="text") | |
| ifg.launch(share=True) |