| import gradio as gr | |
| from transformers import set_seed | |
| from transformers import AutoTokenizer | |
| from transformers import AutoModelForSeq2SeqLM | |
| import torch | |
| import random | |
| import numpy as np | |
| seed = 42 | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| set_seed(seed) | |
| tokenizer = AutoTokenizer.from_pretrained("Oysiyl/elvish-translator-quenya-t5-base") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("Oysiyl/elvish-translator-quenya-t5-base") | |
| prefix = "translate English to Elvish: " | |
| def predict(text): | |
| inputs = tokenizer(prefix + text, return_tensors="pt").input_ids | |
| outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95) | |
| result = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return result | |
| demo = gr.Interface(title="English to Elvish translation!", | |
| description="<p style='text-align: center'>Provide English text and let's model try to guess the text in Elvish!</p>", | |
| article = "<p style='text-align: center'>Text Translation English -> Elvish | Demo Model</p>", | |
| fn=predict, inputs="text", outputs="text") | |
| demo.launch() | |