update app.py

app.py

@@ -1,11 +1,16 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import gradio as gr     
+import gradio as gr
+from gradio.mix import Parallel, Series
+from transformers import pipeline    
+
+envit5_translater = pipeline("translation", model="VietAI/envit5-translation")
 
 def envit5_translation(text):
-    inputs = [f"en: {text}"]
-    outputs = model.generate(tokenizer(inputs, return_tensors="pt", padding=True).input_ids, max_length=512)
-    results = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-    return results[0][4:]
+    res = envit5_translater(
+        text,
+        max_length=512,
+        early_stopping=True,
+    )[0]['translation_text'][3:]
+    return res
 
 def my_translation(text):
     return "My Translation"
@@ -20,20 +25,27 @@ def translation(text):
 
     return (output1, output2, output3)
 
-if __name__ == "__main__":
-    model_name = "VietAI/envit5-translation"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-    inputs = [
-    "textbox"
-    ]
-
-    with gr.Blocks() as app:
-        gr.Interface(
-            fn=translation,
-            inputs=inputs,
-            outputs=["textbox", "textbox", "textbox"]
-            )
-
-    app.launch(enable_queue=True)
+description = """
+<p>
+<center>
+Multi-domain Translation Between English and Vietnamese
+Using VietAI Translation
+</center>
+</p>
+"""
+examples = [
+    ["Dear God, thank you for granting us the evergreen garden of this world", "en->vi"],
+    ["Thuốc này đã bị cấm sử dụng trong ngành thú y tại Ấn Độ.", "vi->en"]
+]
+
+iface = gr.Interface(
+    fn=translation,
+    title="Co Gai Mo Duong",
+    description=description,
+    examples=examples,
+    inputs=[
+        gr.inputs.Textbox(lines=5, placeholder="Enter text (maximum 5 lines)", label="Input"),
+    ],
+    outputs=["text", "text", "text"])
+
+iface.launch(enable_queue=True)