Create app.py

app.py

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+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'))
+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()