app.py

import os
import torch
import dash
import streamlit as st
import pandas as pd
import json
import random
import utils
import firebase_admin
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
from firebase_admin import credentials, firestore
from dotenv import load_dotenv
import plotly.graph_objects as go

import demo_section
import explore_data_section

load_dotenv()

if 'collect_data' not in st.session_state:
    st.session_state.collect_data = True

if 'user_id' not in st.session_state:
    st.session_state.user_id = random.randint(1, 9999999)

st.markdown("""
    # Machine-Based Item Desirability Ratings
    This web application accompanies the paper "*Expanding the Methodological Toolbox: Machine-Based Item Desirability Ratings as an Alternative to Human-Based Ratings*".
    
    *Hommel, B. E. (2023). Expanding the methodological toolbox: Machine-based item desirability ratings as an alternative to human-based ratings. Personality and Individual Differences, 213, 112307. https://doi.org/10.1016/j.paid.2023.112307*


    ## What is this research about?
    Researchers use personality scales to measure people's traits and behaviors, but biases can affect the accuracy of these scales. 
    Socially desirable responding is a common bias that can skew results. To overcome this, researchers gather item desirability ratings, e.g., to ensure that questions are neutral. 
    Recently, advancements in natural language processing have made it possible to use machines to estimate social desirability ratings, 
    which can provide a viable alternative to human ratings and help researchers, scale developers, and practitioners improve the accuracy of personality scales.  
""")

st.divider()
demo_section.show()
st.divider()
explore_data_section.show()