Create app.py

app.py

@@ -0,0 +1,29 @@
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+config = PeftConfig.from_pretrained("kietnt0603/randeng-t5-vta-qa-lora")
+model = AutoModelForSeq2SeqLM.from_pretrained("IDEA-CCNL/Randeng-T5-784M-QA-Chinese")
+model = PeftModel.from_pretrained(model, "kietnt0603/randeng-t5-vta-qa-lora")
+
+tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Randeng-T5-784M-QA-Chinese")
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+def predict(text):
+    input_ids = tokenizer(text, max_length=156, return_tensors="pt", padding="max_length", truncation=True).input_ids.to(device)
+    outputs = model.generate(input_ids=input_ids, max_new_tokens=528, do_sample=True)
+    pred = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
+    return pred
+
+title = 'VTA-QA Demo'
+article = "Loaded model from https://huggingface.co/kietnt0603/randeng-t5-vta-qa-lora"
+# Create the Gradio interface
+iface = gr.Interface(fn=predict,
+                     inputs="textbox",
+                     outputs="textbox",
+                     title=title,
+                     article=article)
+
+# Launch the interface
+iface.launch()