app.py

import streamlit as st
from bokeh.models.widgets import Button
from bokeh.models import CustomJS
from streamlit_bokeh_events import streamlit_bokeh_events
# Load pandas
import pandas as pd
import numpy as np
from nltk.stem import SnowballStemmer



df = pd.read_excel('preguntas_qh_tags.xlsx',engine="openpyxl")


#var recognition = new webkitSpeechRecognition();
precission=50
precission = st.text_input('Input % of tags in sentence (0-100):') 

stt_button = Button(label="Speak")#, width=100)
#stt_button = st.button(label="Speak")


stt_button.js_on_event("button_click", CustomJS(code="""
    var recognition = new webkitSpeechRecognition();

    recognition.lang = "es-ES";
    recognition.continuous = true;
    recognition.interimResults = true;
 
    recognition.onresult = function (e) {
        var value = "";
        for (var i = e.resultIndex; i < e.results.length; ++i) {
            if (e.results[i].isFinal) {
                value += e.results[i][0].transcript;
            }
        }
        if ( value != "") {
            document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value}));
        }
    }
    recognition.start();
    """))


result = streamlit_bokeh_events(
    stt_button,
    events="GET_TEXT",
    key="listen",
    refresh_on_update=False,
    override_height=75,
    debounce_time=0)

#import QuirohelpTagsMethod as QHT
placeholder = st.empty()

# Replace the placeholder with some text:
placeholder.text("Escuchando...")



if result:
    if "GET_TEXT" in result:
        #result2=QHT.Look4tag(result.get("GET_TEXT"))
        placeholder.text("Entendido!")

        # Clear all those elements:
        placeholder.empty()
        
        st.write(result.get("GET_TEXT"))
        #st.write(result2)
        #st.write("PEDRO")
        spanish_stemmer = SnowballStemmer('spanish')
        query=(spanish_stemmer.stem(result.get("GET_TEXT")))
        list_words=[]
        st.text(query)
        textresult=str("")
        for index,row in df.iterrows():
            list_words=df.loc[index,'TAGS']
            list_words = list_words.translate({ ord("?"): None })
            list_words = list_words.translate({ ord("¿"): None })
            ls = list_words.split(",")
            ls = [string for string in ls if string != ""]
            number_of_tags=len(ls)
            found=True
            words_found=0


            

            for word in ls :
                if (spanish_stemmer.stem(word)) in query:# and found==True: 
                    found=True
                    words_found=words_found+1
                else:
                    found=False
            #if found==True:
            #IF WE FIND MORE THAN A HALF OF THE TAGS, THEN WE SHOW THE ANSWER (IT COULD BE MORE THAN JUST ONE ANSWER)
            if (words_found/number_of_tags)>(int(precission)/100):#0.5:
                st.text("##############################################################")
                st.text("Si has preguntado...\n")
                st.text(df.iloc[index,1])
                st.text("La respuesta es...\n")
                st.text(df.iloc[index,2])
                st.text("##############################################################")
                


# streamlit run C:\Users\15572890\Desktop\StockPrediction\Coding\CorrelacionStocks\APIS-StockPlatforms\APIs\QuiroHelpNLP\QuirohelpWeb.py