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import gradio as gr |
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from transformers import pipeline |
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import os |
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translator_plus = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained+"), max_time=5) |
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translator_plus_1 = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained+_1"), max_time=5) |
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translator_plus_2 = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained+_2"), max_time=5) |
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translator_plus_3 = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained+_3"), max_time=5) |
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translator_plus_4 = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained+_4"), max_time=5) |
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def en2zh_plus(text): |
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pres=[] |
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enNames=text.split('\n') |
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for name in enNames: |
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pre = translator_plus(name.replace('_',' '), )[0]["translation_text"] |
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pre = pre+'\n'+ translator_plus_1(name.replace('_',' '), )[0]["translation_text"] |
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pre = pre+'\n'+ translator_plus_2(name.replace('_',' '), )[0]["translation_text"] |
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pre = pre+'\n'+ translator_plus_3(name.replace('_',' '), )[0]["translation_text"] |
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pre = pre+'\n'+ translator_plus_4(name.replace('_',' '), )[0]["translation_text"] |
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pres.append(pre) |
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print(name,pre) |
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return '\n'.join(pres) |
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inputs=gr.TextArea(lines=10,label='请输入英文表名和英文字段名',placeholder='表明和字段名之间用英文冒号:分隔，每组输入以换行\\n为分隔') |
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outputs=gr.TextArea(lines=10,label='参考转译结果') |
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interface_plus = gr.Interface(fn=en2zh_plus, inputs=inputs, outputs=outputs) |
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interface_plus.launch() |
