| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base") | |
| model_file = hf_hub_download(repo_id="tuongvxx1/medBot", filename="medicalBot_ver4.pth") | |
| state_dict = torch.load(model_file, map_location=torch.device('cpu')) | |
| from collections import OrderedDict | |
| new_state_dict = OrderedDict() | |
| for k, v in state_dict.items(): | |
| name = k.replace('module.', '') | |
| new_state_dict[name] = v | |
| model.load_state_dict(new_state_dict) | |
| model.to(device) | |
| def generate_answer(question, model, tokenizer, device): | |
| model.eval() | |
| input_text = "hỏi: " + question | |
| inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True, padding="max_length") | |
| input_ids = inputs.input_ids.to(device) | |
| attention_mask = inputs.attention_mask.to(device) | |
| with torch.no_grad(): | |
| outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_length=128, num_beams=4, early_stopping=True) | |
| answer = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return answer | |
| def run(ques): | |
| return generate_answer(ques, model, tokenizer, device) | |
| demo = gr.Interface(fn=run, inputs=gr.Textbox(label="Nhập câu hỏi"), outputs=gr.Textbox(label="Câu trả lời")) | |
| demo.launch() |