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| import os | |
| import streamlit as st | |
| import yaml | |
| import logging | |
| import pandas as pd | |
| from cryptography.fernet import Fernet, InvalidToken | |
| from dotenv import load_dotenv | |
| from io import StringIO | |
| import modeling | |
| def df_to_csv(df): | |
| csv = StringIO() | |
| df.to_csv(csv, index=True) | |
| csv.seek(0) | |
| csv_data = csv.getvalue() | |
| return(csv_data) | |
| def dict_to_yaml(data): | |
| return yaml.dump(data, default_flow_style=False) | |
| def yaml_to_dict(yaml_str): | |
| return yaml.safe_load(yaml_str) | |
| def initialize(): | |
| logging.basicConfig(level=logging.INFO) | |
| load_dotenv() | |
| st.session_state.setdefault('model_names', ['SurveyBot3000', 'PsiSent', 'all_mpnet_base_v2']) | |
| st.session_state.setdefault('input_model_name', st.session_state['model_names'][0]) | |
| st.session_state.setdefault('loaded_model_name', None) | |
| st.session_state.setdefault('search_query', None) | |
| st.session_state.setdefault('db', None) | |
| st.session_state.setdefault('search_results', pd.DataFrame()) | |
| st.session_state.setdefault('explore_plot', None) | |
| st.session_state.setdefault('is_authenticated', False) | |
| with open('config.yaml', 'r') as stream: | |
| st.session_state['config'] = yaml.safe_load(stream) | |
| if os.environ.get('encryption_key'): | |
| encryption_key = os.environ.get('encryption_key') | |
| st.session_state.setdefault('encryption_key', encryption_key) | |
| # st.session_state.setdefault('encryption_key', None) | |
| else: | |
| st.session_state.setdefault('encryption_key', None) | |
| def main(): | |
| st.set_page_config(page_title='Synth-Net') | |
| st.markdown("# The Synthetic Nomological Net") | |
| st.markdown(""" | |
| Psychological science is experiencing rapid growth in constructs and measures, partly due to refinement and new research areas, | |
| but also due to excessive proliferation. This proliferation, driven by academic incentives for novelty, may lead to redundant | |
| constructs with different names (jangle fallacy) and seemingly similar constructs with little content overlap (jingle fallacy). | |
| This web application uses state-of-the-art models and methods in natural language processing to search for semantic overlap in measures. | |
| It analyzes textual data from over 21,000 scales (containing more than 330,000 items) in an effort to reduce redundancies in measures used in the behavioral sciences. | |
| """, unsafe_allow_html=True) | |
| placeholder_authentication = st.empty() | |
| placeholder_demo = st.empty() | |
| if st.session_state['is_authenticated']: | |
| show_demo(placeholder_demo) | |
| else: | |
| show_authentication(placeholder_authentication) | |
| st.markdown(""" | |
| - 📖 **Preprint (Open Access)**: NA | |
| - 🖊️ **Cite**: NA | |
| - 🌐 **Project website**: NA | |
| - 💾 **Data**: NA | |
| - #️⃣ **Social Media**: NA | |
| The web application is maintained by [magnolia psychometrics](https://www.magnolia-psychometrics.com/). | |
| """, unsafe_allow_html=True) | |
| def show_authentication(placeholder): | |
| with placeholder: | |
| with st.container(): | |
| with st.form("authentication_form"): | |
| st.markdown(""" | |
| ## Authentication | |
| This app is a research preview and requires authentication. | |
| All data is encrypted. Please use your 32-byte encryption key to proceed! | |
| """) | |
| st.text_input( | |
| label="🔑 Encryption key", | |
| value="", | |
| max_chars=None, | |
| key='encryption_key', | |
| placeholder="A URL-safe base64-encoded 32-byte key" | |
| ) | |
| submitted = st.form_submit_button( | |
| label="Authenticate", | |
| type="primary", | |
| use_container_width=True | |
| ) | |
| if submitted: | |
| try: | |
| modeling.load_db() | |
| st.rerun() | |
| except InvalidToken: | |
| error = f"Error: The encryption key you have entered is invalid (**{st.session_state['encryption_key']}**)!" | |
| st.error(body=error, icon="🔑") | |
| logging.error(error) | |
| st.session_state['is_authenticated'] = False | |
| return | |
| except ValueError as error: | |
| st.error(body=error, icon="🔑") | |
| logging.error(error) | |
| st.session_state['is_authenticated'] = False | |
| return | |
| def show_demo(placeholder): | |
| with placeholder: | |
| with st.container(): | |
| st.divider() | |
| st.markdown(""" | |
| ## Try it yourself! | |
| Define a scale by entering individual items in YAML format. | |
| After form submission, a vector representation for the scale is calculated using the selected encoder model. | |
| Cosine similarities between this vector and the representations of existing scales are then computed. | |
| The resulting table outputs measures with high semantic overlap. | |
| """) | |
| if st.session_state['loaded_model_name'] is not None: | |
| input_model_index = st.session_state['model_names'].index(st.session_state['input_model_name']) | |
| else: | |
| input_model_index = 0 | |
| st.selectbox( | |
| label="Select model", | |
| options=st.session_state['model_names'], | |
| index=input_model_index, | |
| placeholder="Choose a model", | |
| key='input_model_name' | |
| ) | |
| tab1, tab2 = st.tabs(["🔎 Search for scales", "🕸️ Explore the synthetic nomological net"]) | |
| with tab1: | |
| if 'input_items' not in st.session_state: | |
| st.session_state['input_items'] = dict_to_yaml(st.session_state['config']['input_items']) | |
| with st.form("submission_form"): | |
| st.text_area( | |
| label="Search for similar measures by entering items that constitute the scale (YAML-Formatted):", | |
| height=175, | |
| key='input_items' | |
| ) | |
| submitted = st.form_submit_button( | |
| label="Search Synth-Net", | |
| type="primary", | |
| use_container_width=True | |
| ) | |
| if submitted: | |
| try: | |
| st.session_state['search_query'] = yaml_to_dict(st.session_state['input_items']) | |
| except yaml.YAMLError as e: | |
| st.error(f"Yikes, you better get your YAML straight! Check https://yaml.org/ for help! \n {e}") | |
| return | |
| no_model = st.session_state.get('model') is None | |
| swap_model = st.session_state.get('input_model_name') != st.session_state['loaded_model_name'] | |
| if swap_model or no_model: | |
| modeling.load_db() | |
| modeling.load_model() | |
| modeling.search() | |
| with st.container(): | |
| if not st.session_state['search_results'].empty: | |
| with st.spinner('Rendering search results...'): | |
| df = st.session_state['search_results'].style.format({ | |
| 'Match': '{:.2f}'.format, | |
| 'Scale': str.capitalize, | |
| 'Instrument': str.capitalize, | |
| }) | |
| st.dataframe(df, use_container_width=True, hide_index=True) | |
| with tab2: | |
| with st.container(): | |
| modeling.explore() | |
| if st.session_state['explore_plot']: | |
| st.plotly_chart( | |
| figure_or_data=st.session_state['explore_plot'], | |
| use_container_width=True | |
| ) | |
| # if not st.session_state['search_results'].empty: | |
| # st.download_button( | |
| # label="Download References", | |
| # data=df_to_csv(st.session_state['search_results']), | |
| # file_name='scored_survey_responses.csv', | |
| # mime='text/csv', | |
| # use_container_width=True | |
| # ) | |
| if __name__ == '__main__': | |
| initialize() | |
| main() |