App.py and Requirements

Author: carlosmbe

app.py

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+import streamlit as st
+import pandas as pd
+from functools import reduce
+from collections import Counter
+from io import BytesIO
+
+st.header('Spreadsheet Query UI')
+
+# Initialize the session state
+if 'uploaded_files' not in st.session_state:
+    st.session_state['uploaded_files'] = {}
+
+# Use st.expander for file management section
+with st.expander('File Management'):
+    uploaded_file = st.file_uploader("Choose an Excel file", type=['xls', 'xlsx', 'xlsm', 'xlsb'])
+
+    # Load data from uploaded Excel files into a dictionary of DataFrames
+    if uploaded_file is not None:
+        xls = pd.ExcelFile(BytesIO(uploaded_file.read()))
+        filepaths = {uploaded_file.name: xls}
+        st.session_state['uploaded_files'].update(filepaths)
+
+    # Allow user to remove files
+    if st.session_state['uploaded_files']:
+        remove_file = st.selectbox('Select a file to remove', list(st.session_state['uploaded_files']))
+        if st.button('Remove File'):
+            st.session_state['uploaded_files'].pop(remove_file)
+
+data = {}
+original_column_names = {}
+
+for path, xls in st.session_state['uploaded_files'].items():
+    for sheet_name in xls.sheet_names:
+        df = xls.parse(sheet_name, dtype=str)  # Ensure all data is loaded as strings
+
+        # Replace spaces with underscores and remove special characters
+        original_column_names.update({col.lower().replace(' ', '_').replace(r'\W', ''): col for col in df.columns})
+        df.columns = df.columns.str.replace(' ', '_')
+        df.columns = df.columns.str.replace(r'\W', '')
+        df.columns = df.columns.str.lower()
+
+        data[f'{path}_{sheet_name}'] = df
+
+# Gather all column names across all DataFrames
+column_names = [col for df in data.values() for col in df.columns]
+
+# Count the frequency of each column name
+counter = Counter(column_names)
+common_columns = [name for name, count in counter.items() if count > 2]
+rare_columns = [name for name, count in counter.items() if count == 2]
+unique_columns = [name for name, count in counter.items() if count == 1]
+
+st.subheader('Pick Terms to Search For')
+
+# Generate column checkboxes under their respective subheaders
+st.markdown('### Common Fields')
+selected_common_columns = [column for column in common_columns if st.checkbox(original_column_names[column], key=f'common_{column}')]
+
+with st.expander("Rare Fields"):
+    selected_rare_columns = [column for column in rare_columns if st.checkbox(original_column_names[column], key=f'rare_{column}')]
+
+with st.expander("Unique Fields"):
+    selected_unique_columns = [column for column in unique_columns if st.checkbox(original_column_names[column], key=f'unique_{column}')]
+
+# Combine all selected columns
+selected_columns = selected_common_columns + selected_rare_columns + selected_unique_columns
+
+# Generate dropdowns for selected columns
+selected_values = {}
+for column in selected_columns:
+    options = list(set([str(val) for df in data.values() if column in df.columns for val in df[column].dropna().unique()]))
+    selected_values[column] = st.selectbox(f"{original_column_names[column]} values", options, key=f'selectbox_{column}')
+
+# Select logical operator
+logical_operator = st.selectbox("Logical Operator", options=["And", "Or"], index=0)
+view_all_columns = st.checkbox('View all columns in results')
+
+if st.button("🔍 Search 🔎"):
+    results = []
+    # Iterate through all DataFrames and filter rows
+    for name, df in data.items():
+        if logical_operator == "Or":
+            conditions = [df[column] == selected_values[column] for column in selected_values if column in df.columns]
+            if conditions:  # if there is at least one condition
+                matched_df = df[reduce(lambda a, b: a | b, conditions)]
+                if not matched_df.empty:
+                    results.append({'name': name, 'data': matched_df})
+        else:  # "And" operator
+            conditions = [df[column] == selected_values[column] for column in selected_values if column in df.columns]
+            if conditions:  # if there is at least one condition
+                matched_df = df[reduce(lambda a, b: a & b, conditions)]
+                if not matched_df.empty:
+                    results.append({'name': name, 'data': matched_df})
+
+    # If there are any results, display them
+    if results:
+        st.subheader('Global Search Results')
+        for result in results:
+            st.markdown(f'### {result["name"]}')
+            # Display all columns if the checkbox is checked, else display the first three columns
+            st.dataframe(result['data'] if view_all_columns else result['data'].iloc[:, :3])
+    else:
+        st.write('No results found')
+

requirements.txt

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+streamlit~=1.24.1
+pandas~=2.0.3
+openpyxl~=3.1.2