app.py

import requests
import streamlit as st
import streamlit.components.v1 as components
def query(payload):
    	response = requests.post(API_URL, headers=headers, json=payload)
    	return response.json()
    
def semanticComparativeClassification():
    st.write(st.session_state.mytext)
    
    #API_URL = "https://api-inference.huggingface.co/models/Maite89/Roberta_finetuning_semantic_similarity_stsb_multi_mt"
    API_URL = "https://api-inference.huggingface.co/models/symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli"
    headers = {"Authorization": "Bearer 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"}

    sentences =  [
    			"Conoces Lya2",
    			"He perdido la contraseña",
    			"Calendario de eventos",
    			"Cambios dobles",
                "Cambios simples",
                "Hola me estás saludando",
                "Adiós te despides"
    		]
     	
    output = query({
    	"inputs": {
            "wait_for_model" : True,
    		"source_sentence": st.session_state.mytext,
    		"sentences": sentences
    	},
    })
    index = 0
    sentenceindex = -1
    max = 0
    for i in output:
        st.write(i) 
        if i>max:
            sentenceindex = index
        index = index + 1

    st.write("Hablamos de ", sentences[sentenceindex]) 
    
    return output


#x = st.slider('Select a value')
#st.write(x, 'squared is', x * x)
 


st.title('Reconocimiento semántico')
title = st.text_input('Pregunta', '¿Conoces Lya2?', on_change=semanticComparativeClassification,key='mytext')
st.write('La pregunta es ', st.session_state.mytext)