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| import streamlit as st | |
| import pandas as pd | |
| # 1. Loading the dataset | |
| df = pd.read_excel("data/table.xlsx") | |
| # 2. Preprocessing the dataset | |
| df["Bionic prototype"] = df["Bionic prototype"].str.strip() | |
| df["Materials"] = df["Materials"].str.split(";").apply(lambda x: [material.strip() for material in x]) | |
| df["Method"] = df["Method"].str.split(";").apply(lambda x: [method.strip() for method in x]) | |
| df["Multifunction"] = df["Multifunction"].str.split(";").apply(lambda x: [multifunction.strip() for multifunction in x]) | |
| # 3. Saving the processed dataset | |
| # df.to_excel("data/filtered_table.xlsx", index=False) | |
| # 4. Extracting a unique list for each column | |
| bionic_prototype_list = df["Bionic prototype"].unique() | |
| method_list = df["Method"].explode().unique() | |
| multifunction_list = df["Multifunction"].explode().unique() | |
| bionic_prototype_list.sort() | |
| method_list.sort() | |
| multifunction_list.sort() | |
| with st.sidebar: | |
| st.slider( | |
| label="Search results limit", | |
| min_value=1, | |
| max_value=50, | |
| value=20, | |
| step=1, | |
| key="limit", | |
| help="Limit the number of search results", | |
| ) | |
| st.multiselect( | |
| label="Display columns", | |
| options=["Bionic prototype", "Multifunction", "Method", "Materials", "Res"], | |
| default=["Bionic prototype", "Multifunction", "Method", "Materials", "Res"], | |
| help="Select columns to display in the search results", | |
| key="display_columns", | |
| ) | |
| st.title("Bionic Path Selection") | |
| st.multiselect( | |
| label="Multifunction", | |
| options=multifunction_list, | |
| default=[], | |
| help="Select the multifunction to display in the search results", | |
| placeholder="Select the multifunction to display in the search results", | |
| key="multifunction_option" | |
| ) | |
| st.session_state.disabled = False if len(st.session_state.multifunction_option) > 0 else True | |
| left_col, right_col = st.columns(2) | |
| with left_col: | |
| st.selectbox( | |
| label="Bionic prototype", | |
| options=bionic_prototype_list, | |
| help="Select the bionic prototype to display in the search results", | |
| placeholder="Select the bionic prototype to display in the search results", | |
| index=None, | |
| key="bionic_prototype_option", | |
| disabled=st.session_state.disabled | |
| ) | |
| with right_col: | |
| st.multiselect( | |
| label="Method", | |
| options=method_list, | |
| default=[], | |
| help="Select the method to display in the search results", | |
| placeholder="Select the method to display in the search results", | |
| key="method_option", | |
| disabled=st.session_state.disabled | |
| ) | |
| search = st.button("Search") | |
| if search: | |
| multifunction_option = st.session_state.multifunction_option | |
| bionic_prototype_option = st.session_state.bionic_prototype_option | |
| method_option = st.session_state.method_option | |
| # Filter the multifunction column | |
| filtered_df = df[ | |
| df["Multifunction"].apply(lambda x: all(multifunction in x for multifunction in multifunction_option))] | |
| # Filter the bionic prototype column | |
| filtered_df = filtered_df[filtered_df["Bionic prototype"] == bionic_prototype_option] \ | |
| if (bionic_prototype_option is not None and not st.session_state.disabled and not filtered_df.empty) \ | |
| else filtered_df | |
| # Filter the method column | |
| filtered_df = filtered_df[filtered_df["Method"].apply(lambda x: any(method in x for method in method_option))] \ | |
| if (len(method_option) > 0 and not st.session_state.disabled and not filtered_df.empty) \ | |
| else filtered_df | |
| st.dataframe(filtered_df[st.session_state.display_columns].head(st.session_state.limit)) | |