app.py

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)