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from huggingface_hub import from_pretrained_fastai
import gradio as gr
# from fastai.vision.all import *
# from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
from transformers import pipeline
from transformers import Seq2SeqTrainer, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq
from transformers import AutoTokenizer
# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
repo_id = "islasher/mbart-spanishToQuechua"
# Definimos una función que se encarga de llevar a cabo las predicciones
# Cargar el modelo y el tokenizador
nombre_modelo = 'islasher/mbart-spanishToQuechua'
model = AutoModelForSeq2SeqLM.from_pretrained(nombre_modelo)
tokenizer = AutoTokenizer.from_pretrained(nombre_modelo)
import numpy as np
import evaluate
metric = evaluate.load("sacrebleu")
def postprocess_text(preds, labels):
preds = [pred.strip() for pred in preds]
labels = [[label.strip()] for label in labels]
return preds, labels
def compute_metrics(eval_preds):
preds, labels = eval_preds
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
result = metric.compute(predictions=decoded_preds, references=decoded_labels)
result = {"bleu": result["score"]}
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
result["gen_len"] = np.mean(prediction_lens)
result = {k: round(v, 4) for k, v in result.items()}
return result
#CAMBIAR LO QUE SE RETORNA Y PONER LO DEL DECODER.
def predict(frase):
#img = PILImage.create(img)
inputs = tokenizer(frase, return_tensors="pt")
outputs = model(**inputs)
trad = tokenizer.decode(outputs[0], skip_special_tokens=True)
return trad
# Creamos la interfaz y la lanzamos.
gr.Interface(fn=predict, inputs="text", outputs="text").launch(share=False)
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