app.py

import gradio as gr
from gensim.models import KeyedVectors

def isNoneWords(word):
    if word is None or len(word)==0 or word not in model.key_to_index:
        return True
    else:
        return False

def top_similarity_route(word):
    if isNoneWords(word):
        return "word is null or not in model!"
    else:
        return {'word':word,'top_similar_words':model.similar_by_word(word, topn=20, restrict_vocab=None)}


if __name__ == '__main__':
    model = KeyedVectors.load_word2vec_format('tencent-ailab-embedding-zh-d100-v0.2.0-s.txt', binary=False)
    iface = gr.Interface(fn=top_similarity_route, inputs="text", outputs="text")
    iface.launch()