import torch import gradio as gr from transformers import T5ForConditionalGeneration, T5Tokenizer import os #import whisper import matplotlib as plt # whisper_model = whisper.load_model('large-v2') # Whisper 모델을 불러오기 path = "Hyeonsieun/NTtoGT_7epoch" tokenizer = T5Tokenizer.from_pretrained(path) model = T5ForConditionalGeneration.from_pretrained(path) def do_correction(text): input_text = f"translate the text pronouncing the formula to a LaTeX equation: {text}" inputs = tokenizer.encode( input_text, return_tensors='pt', max_length=325, padding='max_length', truncation=True ) # Get correct sentence ids. corrected_ids = model.generate( inputs, max_length=325, num_beams=5, # `num_beams=1` indicated temperature sampling. early_stopping=True ) # Decode. corrected_sentence = tokenizer.decode( corrected_ids[0], skip_special_tokens=False ) return corrected_sentence gr.Interface(fn=do_correction, inputs="text", outputs="text").launch()