Create app.py

app.py

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+import gradio as gr
+import transformers
+
+encoder_model_name = "cl-tohoku/bert-base-japanese-v2"
+decoder_model_name = "skt/kogpt2-base-v2"
+src_tokenizer = transformers.BertJapaneseTokenizer.from_pretrained(encoder_model_name)
+trg_tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained(decoder_model_name)
+model = transformers.EncoderDecoderModel.from_pretrained("sappho192/aihub-ja-ko-translator")
+
+
+def translate(text_src):
+    embeddings = src_tokenizer(text_src, return_attention_mask=False, return_token_type_ids=False, return_tensors='pt')
+    embeddings = {k: v for k, v in embeddings.items()}
+    # using default generation method: GreedySearch, No LogitsProcessor
+    output = model.generate(**embeddings)[0, 1:-1]
+    text_trg = trg_tokenizer.decode(output.cpu())
+    return text_trg
+
+
+def endpoint(sentence):
+    return translate(sentence)
+
+
+# demo = gr.Interface(fn=endpoint, inputs="text", outputs="text")
+with gr.Blocks() as demo:
+    input = gr.Textbox(label="Sentence")
+    output = gr.Textbox(label="Result")
+    btn = gr.Button(value="Submit")
+    btn.click(endpoint, inputs=[input], outputs=[output])
+    
+    gr.Markdown("## Examples")
+    gr.Markdown(
+    """
+    Translated result can be wrong or containg misleading content.
+    번역된 결과는 정확하지 않을 수 있으며, 적절치 않은 표현을 포함할 수 있습니다.
+    """)
+    gr.Examples(
+        [["試験前に緊張したあまり、熱がでてしまった。"], 
+         ["山田は英語にかけてはクラスの誰にも負けない。"],
+         ["この本によれば、最初の人工橋梁は新石器時代にさかのぼるという。"]],
+        [input],
+        output,
+        endpoint,
+        cache_examples=False
+    )
+    
+if __name__ == "__main__":
+    demo.launch(share=True)