text_generator.py

from transformers import T5Tokenizer, T5ForConditionalGeneration

model_source = "rfahlevih/t5-small-finetuned-resume-text-generation"
tokenizer = T5Tokenizer.from_pretrained(model_source)
model = T5ForConditionalGeneration.from_pretrained(model_source)

def generate_text(input_text):
    input_ids = tokenizer(input_text, return_tensors='pt', truncation=True, padding="max_length", max_length=512).input_ids
    outputs = model.generate(input_ids, max_length=512, num_beams=4, early_stopping=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)