Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# Load model and tokenizer
+model_name = "VietAI/envit5-translation"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+def translate(text, source_lang):
+    """Translate text based on the source language."""
+    input_text = f"{source_lang}: {text}"
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True).input_ids.to('cpu')
+    outputs = model.generate(inputs, max_length=512)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create UI
+demo = gr.Interface(
+    fn=translate,
+    inputs=[gr.Textbox(label="Input Text"), gr.Radio(["vi", "en"], label="Source Language")],
+    outputs=gr.Textbox(label="Translated Text"),
+    title="VietAI Translation",
+    description="Translate between Vietnamese and English using envit5-translation model."
+)
+
+# Launch app
+demo.launch()