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import pandas as pd
import streamlit as st
from difflib import SequenceMatcher
ms = st.session_state
if "themes" not in ms:
ms.themes = {"current_theme": "light",
"refreshed": True,
"light": {"theme.base": "dark",
"theme.backgroundColor": "black",
"theme.primaryColor": "#c98bdb",
"theme.secondaryBackgroundColor": "#5591f5",
"theme.textColor": "white",
"theme.textColor": "white",
"button_face": "🌜"},
"dark": {"theme.base": "light",
"theme.backgroundColor": "white",
"theme.primaryColor": "#5591f5",
"theme.secondaryBackgroundColor": "#82E1D7",
"theme.textColor": "#0a1464",
"button_face": "🌞"},
}
def ChangeTheme():
previous_theme = ms.themes["current_theme"]
tdict = ms.themes["light"] if ms.themes["current_theme"] == "light" else ms.themes["dark"]
for vkey, vval in tdict.items():
if vkey.startswith("theme"): st._config.set_option(vkey, vval)
ms.themes["refreshed"] = False
if previous_theme == "dark": ms.themes["current_theme"] = "light"
elif previous_theme == "light": ms.themes["current_theme"] = "dark"
btn_face = ms.themes["light"]["button_face"] if ms.themes["current_theme"] == "light" else ms.themes["dark"]["button_face"]
st.button(btn_face, on_click=ChangeTheme)
if ms.themes["refreshed"] == False:
ms.themes["refreshed"] = True
st.rerun()
def read_csv_or_excel(file):
# Read CSV or Excel file
if file.name.endswith('.csv'):
return pd.read_csv(file)
elif file.name.endswith('.xlsx') or file.name.endswith('.xls'):
return pd.read_excel(file)
else:
raise ValueError("Unsupported file format. Only CSV and Excel files are supported.")
def find_exact_matches(df1, df2, column_name):
# Find rows with exact matches in the specified column
matches = pd.merge(df1, df2, on=column_name, how='inner')
return matches
def find_similar_texts(df1, df2, column_name, exact_matches, threshold=0.8):
# Find rows with similar texts in the specified column, excluding exact matches
similar_texts = []
for index1, row1 in df1.iterrows():
for index2, row2 in df2.iterrows():
if (index1, index2) not in exact_matches:
similarity = SequenceMatcher(None, str(row1[column_name]), str(row2[column_name])).ratio()
if similarity >= threshold:
similar_texts.append((index1, index2, row1[column_name], row2[column_name]))
return similar_texts
def main():
st.title("Item Comparison App")
# Upload files
st.header("Upload Files")
warehouse_file = st.file_uploader("Upload Warehouse Item Stocks (CSV or Excel)")
industry_file = st.file_uploader("Upload Industry Item Stocks (CSV or Excel)")
if warehouse_file is not None and industry_file is not None:
# Read files
warehouse_df = read_csv_or_excel(warehouse_file)
industry_df = read_csv_or_excel(industry_file)
# Get column names
warehouse_columns = warehouse_df.columns.tolist()
industry_columns = industry_df.columns.tolist()
# Select columns using dropdowns
st.header("Select Columns")
warehouse_column = st.selectbox("Choose column from warehouse item stocks:", warehouse_columns)
industry_column = st.selectbox("Choose column from industry item stocks:", industry_columns)
# Find exact matches
exact_matches = find_exact_matches(warehouse_df, industry_df, warehouse_column)
# Find similar texts
similar_texts = find_similar_texts(warehouse_df, industry_df, warehouse_column, exact_matches)
# Display results
st.header("Exact Matches")
st.write(exact_matches)
st.header("Similar Texts")
for text_pair in similar_texts:
st.write(f"Row {text_pair[0]} in warehouse item stocks is similar to Row {text_pair[1]} in industry item stocks:")
st.write(f"Warehouse: {text_pair[2]}")
st.write(f"Industry: {text_pair[3]}")
st.write("")
if __name__ == "__main__":
main()
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