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import os
os.system("pip install -U huggingface_hub")
from haystack.nodes import FARMReader
#question_reader_save_path = "/Users/svjack/temp/model/en_zh_question_reader_save_epc_2_spo"
question_reader_save_path = "en_zh_question_reader_save_epc_2_spo"
assert os.path.exists(question_reader_save_path)
en_zh_reader = FARMReader(model_name_or_path=question_reader_save_path, use_gpu=False,
num_processes = 0
)
def output_to_dict(output, trans_keys = ["answers"]):
non_trans_t2_list = list(filter(lambda t2: t2[0] not in trans_keys, output.items()))
trans_t2_list = list(map(lambda tt2: (
tt2[0],
list(map(lambda x: x.to_dict(), tt2[1]))
) ,filter(lambda t2: t2[0] in trans_keys, output.items())))
#return trans_t2_list
return dict(trans_t2_list + non_trans_t2_list)
'''
en_zh_reader.predict_on_texts(
question=x.replace("hp:", ""),
texts=[zh_question]
)
'''
import gradio as gr
example_sample = [
["NingBo" ,"宁波在哪个省份？"],
["province" ,"宁波在哪个省份？"],
["currency" ,"美国的通货是什么？"],
]
def demo_func(eng_span, zh_sentence):
assert type(eng_span) == type("")
assert type(zh_sentence) == type("")
output = en_zh_reader.predict_on_texts(
question=eng_span,
texts=[zh_sentence]
)
output = output_to_dict(output)
return output
demo = gr.Interface(
fn=demo_func,
inputs=[
gr.Text(label="English Span"),
gr.Text(label="Chinese Sentence"),
],
outputs="json",
title=f"Extract Similar Chinese Span by English From Chinese 🍔 demonstration",
examples=example_sample if example_sample else None,
cache_examples = False
)
demo.launch(server_name=None, server_port=None)
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