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| import modules | |
| import streamlit as st | |
| from streamlit_extras.let_it_rain import rain | |
| # Options | |
| DISCLAIMER = """ | |
| *This app processes data using 2-anonymity, an implementation of the k-anonymity framework. While this is a great start to anonymizing your data, it is by no means perfect, and should be used with caution. For example, some sets of sensitive features which may clearly be identified by a human could be missed by our algorithm. Please keep this in mind.* | |
| """ | |
| K = 2 | |
| # Page Config | |
| st.set_page_config(layout="wide") | |
| ### FILE LOADER for sidebar | |
| with st.sidebar: | |
| st.header("🕵️ 2anonymity") | |
| st.markdown("*Clean and anonymize data*") | |
| with st.container() as upload: | |
| file = st.file_uploader(f"Upload dataset:", type=modules.SUPPORTED_TYPES, label_visibility="collapsed") | |
| df, (filename, extension), result = modules.load_file(file) | |
| ### MAIN | |
| if df is None: # Await file to be uploaded | |
| rain("🤠") | |
| else: | |
| ### PRE-TRANSFORM features for sidebar | |
| with st.sidebar: | |
| # Options for data loading | |
| with st.container() as loading_options: | |
| st.markdown("### Data loading options:") | |
| remove_duplicates = st.checkbox("Remove duplicate rows", value=True) | |
| drop_missing = st.checkbox("Remove rows with missing values", value=False) | |
| # Options for data optimization | |
| with st.container() as anonymizing_options: | |
| st.markdown("### Anonymizing options:") | |
| max_categorical_size = st.slider("Categorical Variable Threshold", min_value=2, max_value=200, value=50, step=1) | |
| bin_size = st.slider("Bin Size", min_value=2, max_value=200, value=20, step=1) | |
| redaction_selection = st.selectbox("Redaction strength", ["Low", "Medium", "High", "Extreme"]) | |
| sensitivity_minimum = {"Low": 2, "Medium": 4, "High": 6, "Extreme": 12}[redaction_selection] | |
| ### DATA PREVIEW AND TRANSFORM | |
| # Preview data before transform | |
| with st.container() as before_data: | |
| s = df.style | |
| s = s.set_properties(**{'background-color': '#fce4e4'}) | |
| st.dataframe(s) | |
| # Transform data | |
| df = modules.data_cleaner(df, drop_missing, remove_duplicates) | |
| df, unprocessed = modules.data_anonymizer(df, K, max_categorical_size, bin_size, sensitivity_minimum) | |
| # Preview data after before_data | |
| with st.container() as after_data: | |
| s = df.style | |
| s = s.set_properties(**{'background-color': '#e4fce4'}) | |
| st.dataframe(s) | |
| ### POST-TRANSFORM features for sidebar | |
| with st.sidebar: | |
| # Options for download | |
| with st.container() as download_header: | |
| st.markdown("### Download options:") | |
| output_extension = st.selectbox("File type", [".csv", ".json", ".xlsx"]) | |
| if unprocessed: st.markdown(f"Error encountered when processing columns {str(unprocessed)}") | |
| # Prepare file for download | |
| with st.container() as downloader: | |
| if output_extension == ".csv": output_file = df.to_csv().encode("utf-8") | |
| elif output_extension == ".json": output_file = df.to_json().encode("utf-8") | |
| elif output_extension == ".xlsx": output_file = df.to_excel().encode("utf-8") | |
| output_filename = f"""{filename.split(".")[:-1][0]}-clean{output_extension}""" | |
| st.download_button("Download", output_file, file_name=output_filename) | |
| # Add a disclaimer for data security | |
| with st.container() as disclaimer: | |
| st.markdown( | |
| f""" | |
| Disclaimer: | |
| {DISCLAIMER} | |
| """ | |
| ) | |
| # Attribution | |
| st.sidebar.markdown("Created by team #2hack2furious for the hackthethreat2023") |