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# Helsinki-NLP/opus-mt-zh-en
import gradio as gr
from transformers import pipeline
import os
# def test(text):
# return text
# inputs=gr.TextArea(lines=10,label='请输入英文字段名',placeholder='以换行\\n为分隔符')
# outputs=gr.TextArea(lines=10,label='转译结果')
# interface = gr.Interface(fn=test, inputs=inputs, outputs=outputs)
# interface.launch()
# translator = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained"), max_time=5)
# # list-list
# def en2zh(EnNames):
# pres=[]
# for name in EnNames:
# pre = translator(name[0].replace('_',' '), )[0]["translation_text"]
# pres.append([pre])
# print(name,pre)
# return pres
# interface = gr.Interface(fn=en2zh, inputs="list", outputs="list")
# interface.launch()
translator = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained"), max_time=5)
translator_1 = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained_1"), max_time=5)
translator_2 = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained_2"), max_time=5)
translator_3 = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained_3"), max_time=5)
translator_4 = pipeline("translation", model=os.path.join(os.getcwd(),"test-ml-trained_4"), max_time=5)
# text-text
def en2zh(text):
pres=[]
enNames=text.split('\n')
for name in enNames:
pre = translator(name.replace('_',' '), )[0]["translation_text"]
pre = pre+'\n'+ translator_1(name.replace('_',' '), )[0]["translation_text"]
pre = pre+'\n'+ translator_2(name.replace('_',' '), )[0]["translation_text"]
pre = pre+'\n'+ translator_3(name.replace('_',' '), )[0]["translation_text"]
pre = pre+'\n'+ translator_4(name.replace('_',' '), )[0]["translation_text"]
pres.append(pre)
print(name,pre)
return '\n\n'.join(pres)
inputs=gr.TextArea(lines=10,label='请输入英文字段名（若输入多个，请用换行分隔）',placeholder='以换行\\n为分隔符')
outputs=gr.TextArea(lines=10,label='参考转译结果')
interface = gr.Interface(fn=en2zh, inputs=inputs, outputs=outputs)
interface.launch() |