import gradio as gr
from sentence_transformers import SentenceTransformer

model_Q = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q')

def getVectors(sentences):
            vectors = []
            splitSentences = sentences.split('ZZZ')
            #for sentence in sentences:
            #            vectors.append(model_Q.encode(sentence))
            #return vectors
            return splitSentences
            
interface = gr.Interface(fn = getVectors,
inputs = "text",
outputs = ['text'],
title = 'get vectors',
description = 'get vectors for search')

interface.launch(inline = False)