29 lines
1.1 KiB
Python
29 lines
1.1 KiB
Python
import gradio as gr
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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tokenizer = T5Tokenizer.from_pretrained("t5-large")
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model = T5ForConditionalGeneration.from_pretrained("t5-large")
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def translation(english, language):
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if language == 'German':
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input_ids = tokenizer("translate English to German: " + english, return_tensors="pt").input_ids
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elif language == 'French':
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input_ids = tokenizer("translate English to French: " + english, return_tensors="pt").input_ids
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else:
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input_ids = tokenizer("translate English to Romanian: " + english, return_tensors="pt").input_ids
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outputs = model.generate(input_ids)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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demo = gr.Interface(fn=translation,
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inputs=['text', gr.inputs.Radio(['German','French','Romanian'], type='value', default='German', label='language')],
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outputs='text',
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title = "翻译"
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)
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if __name__ == "__main__":
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demo.queue(concurrency_count=3)
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demo.launch(server_name = "0.0.0.0", server_port = 7028)
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