| import gradio as gr | |
| import torch | |
| import os | |
| from transformers import AutoTokenizer, T5ForConditionalGeneration | |
| model_id = 'ksabeh/gavi' | |
| max_input_length = 512 | |
| max_target_length = 10 | |
| auth_token = os.environ.get('TOKEN') | |
| model = T5ForConditionalGeneration.from_pretrained(model_id, use_auth_token=auth_token) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=auth_token) | |
| def predict(title, category): | |
| input = f"{title} <hl> {category} <hl>" | |
| model_input = tokenizer(input, max_length=max_input_length, truncation=True, | |
| padding="max_length") | |
| model_input = {k:torch.unsqueeze(torch.tensor(v),dim=0) for k,v in model_input.items()} | |
| predictions = model.generate(**model_input, num_beams=8, do_sample=True, max_length=10) | |
| return tokenizer.batch_decode(predictions, skip_special_tokens=True)[0] | |
| iface = gr.Interface( | |
| predict, | |
| inputs=["text", "text"], | |
| outputs=['text'], | |
| title="Attribute Generation", | |
| ) | |
| iface.launch() |