First commit

app.py

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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))

requirements.txt

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+pandas
+streamlit
+openpyxl