Add application file

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base")
+model = AutoModelForSeq2SeqLM.from_pretrained("quocanh944/viT5-med-qa")
+
+def generate_answer(question):
+    global model, tokenizer
+    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
+    attention_mask = inputs.attention_mask
+
+    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
+
+title = "Interactive demo: ViT5 with Medical Dataset"
+description = "Demo for ViT5 with Medical Dataset. The model is fine-tuned on a Vietnamese medical dataset. The model is able to answer questions related to medical knowledge. Please input your question in the textbox and click submit to get the answer."
+article = "This is a demo for ViT5 with Medical Dataset. The model is fine-tuned on a Vietnamese medical dataset. The model is able to answer questions related to medical knowledge. Please input your question in the textbox and click submit to get the answer."
+examples = ["Tôi bị đau tay thì nên làm gì?", "Covid-19 là gì?", "Tôi nên làm gì khi bị sùi mào gà?", "Tôi nên ăn gì để tăng cân?"]
+
+iface = gr.Interface(fn=generate_answer, 
+                     inputs=gr.Textbox(),
+                     outputs=gr.Textbox(),
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples)
+
+iface.launch()

requirements.txt

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+transformers==4.40.2
+torch==2.3.0