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() ordered_multifunction_list = ["Antifogging", "Self-cleaning", "Antireflective", "Antibacterial", "Anti-icing", "Antiwetting", "Large FOV", "Fast motion detection", "Structural color", "Droplet directional migration", "Anti-drag", "Water collection", "Self-propelled actuator"] other_multifunction_list = [x for x in multifunction_list if x not in ordered_multifunction_list] ordered_multifunction_list.extend(other_multifunction_list) # Not use def show_res_link(row_idx): res_link = df.iloc[row_idx]['Res link'] st.sidebar.info(f'Resource Link: {res_link}') # Draw the canvas 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 link"], default=["Bionic prototype", "Multifunction", "Method", "Materials", "Res link"], help="Select columns to display in the search results", key="display_columns", ) st.title("Bionic Path Selection") st.multiselect( label="Multifunction", options=ordered_multifunction_list, default=[], help="Select the multifunction to display in the search results (Maximum is 2)", placeholder="Select the multifunction to display in the search results (Maximum is 2)", key="multifunction_option", max_selections=2 ) 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 # Reset the index filtered_df = filtered_df.reset_index(drop=True) filtered_df.index = range(1, len(filtered_df)+1) st.dataframe( filtered_df[st.session_state.display_columns].head(st.session_state.limit), column_config={ "Res link": st.column_config.LinkColumn( "Resource", display_text=None ) } )