| # import gradio as gr | |
| # from transformers import pipeline | |
| # classifier = pipeline('text-classification', model='nlmaldonadog/clasificador-rotten-tomatoes-xlnet-base-cased') | |
| # def predict(text): | |
| # prediction = classifier(text) | |
| # return prediction[0] | |
| # iface = gr.Interface(fn=predict, inputs=[gr.Textbox(placeholder='Escribe aquí...')], outputs="text") | |
| # iface.launch(share=True) | |
| from huggingface_hub import from_pretrained_fastai | |
| import gradio as gr | |
| from fastai.text.all import * | |
| # Cargar el primer modelo | |
| repo_id1 = "nlmaldonadog/AWD_LSTM_P7" | |
| learner1 = from_pretrained_fastai(repo_id1) | |
| labels1 = learner1.dls.vocab | |
| def predict1(text): | |
| pred,pred_idx,probs = learner1.predict(text) | |
| return str({labels1[i]: float(probs[i]) for i in range(len(labels1))}) | |
| texto = gr.Textbox(placeholder='Escribe aquí...') | |
| # Creamos las interfaces y las lanzamos. | |
| gr.Interface(fn=predict1, inputs=[texto], outputs="text").launch(share=True) |