from fastai.text.all import * import gradio as gr from huggingface_hub import from_pretrained_fastai # Cargamos el learner repo_id = "ikerml/twitter_class" learner = from_pretrained_fastai(repo_id) # Definimos las etiquetas de nuestro modelo labels = ['❀','😍','πŸ˜‚','πŸ’•','πŸ”₯','😊','😎','✨','πŸ’™','😘','πŸ“·','πŸ‡ΊπŸ‡Έ','β˜€','πŸ’œ','πŸ˜‰','πŸ’―','😁','πŸŽ„','πŸ“Έ','😜'] example1 = "I like potatoe" example2 = "My house is on fire" example3 = "The other day, i eat a banana" # Definimos una funciΓ³n que se encarga de llevar a cabo las predicciones def predict(text): pred,pred_idx,probs = learner.predict(text) return {labels[i]: float(probs[i]) for i in range(len(labels))} # Creamos la interfaz y la lanzamos. gr.Interface(fn=predict, inputs=gr.inputs.Textbox(), outputs=gr.outputs.Label(),examples=[example1,example2,example3]).launch(share=False)