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| # Import necessary libraries | |
| import streamlit as st | |
| import pandas as pd | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| import numpy as np | |
| import joblib | |
| import base64 | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import re | |
| from PIL import Image | |
| # Load the trained Random Forest model and TF-IDF vectorizer | |
| rf_classifier = joblib.load('random_forest_model.pkl') | |
| vectorizer = joblib.load('tfidf_vectorizer.pkl') | |
| image1 = Image.open('image1.PNG') | |
| logo = Image.open('logo.png') | |
| hide_streamlit_style = """ | |
| <style> | |
| #MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hide_streamlit_style, unsafe_allow_html=True) | |
| def main(): | |
| st.title('Batch Product SKU Predictor') | |
| # Sidebar | |
| display_sidebar() | |
| # Main UI sections | |
| st.subheader('1. File Upload') | |
| uploaded_file = st.file_uploader("Choose a CSV or Excel file. Make sure the number of rows is less than 20,000.", type=['csv', 'xlsx']) | |
| if uploaded_file: | |
| st.success("File uploaded successfully!") | |
| st.subheader('2. Processing Data...') | |
| process_data(uploaded_file) | |
| else: | |
| st.info("Please upload a CSV or Excel file to get started.") | |
| def display_sidebar(): | |
| """Displays information on the sidebar.""" | |
| st.sidebar.image(logo, width=250) | |
| st.sidebar.header('About') | |
| st.sidebar.text('This app predicts product SKUs based\non uploaded data.') | |
| st.sidebar.subheader('Instructions:') | |
| st.sidebar.text('1. Upload your data file.') | |
| st.sidebar.text('2. Make sure your column name is\n"Product Name".') | |
| st.sidebar.image(image1, 'example') | |
| st.sidebar.text('3. Wait for processing.') | |
| st.sidebar.text('4. View and download the results.') | |
| # Function to transform product names into SKU names | |
| def transform_to_sku(product_name): | |
| if isinstance(product_name, str): | |
| # Remove unwanted characters | |
| product_name = product_name.replace('.', '').replace('@', '').replace('+', '') | |
| # Remove parentheses | |
| product_name = re.sub(r'\((.*?)\)', r'\1', product_name) | |
| # Insert hyphens between numbers and letters if there is no space | |
| product_name = re.sub(r'(\d+)([a-zA-Z])', r'\1-\2', product_name) | |
| product_name = re.sub(r'([a-zA-Z])(\d+)', r'\1-\2', product_name) | |
| # Split, join with hyphens, and convert to uppercase | |
| sku_name = '-'.join(product_name.split()).upper() | |
| # Collapse multiple hyphens into one | |
| sku_name = re.sub(r'-{2,}', '-', sku_name) | |
| else: | |
| sku_name = "UNKNOWN-SKU" | |
| return sku_name | |
| def process_file_upload(): | |
| """Handles the file upload and processing.""" | |
| uploaded_file = st.file_uploader("Choose a CSV or Excel file", type=['csv', 'xlsx']) | |
| if uploaded_file: | |
| st.write("File uploaded successfully. Processing...") | |
| process_data(uploaded_file) | |
| else: | |
| st.write("Awaiting file upload...") | |
| def process_data(uploaded_file): | |
| """Processes the uploaded file and displays the results.""" | |
| progress_bar = st.progress(0) | |
| try: | |
| data = load_data(uploaded_file) | |
| progress_bar.progress(25) | |
| product_vectors = preprocess_data(data) | |
| progress_bar.progress(50) | |
| data = predict_and_score(data, product_vectors) | |
| progress_bar.progress(75) | |
| display_results(data) | |
| progress_bar.progress(100) | |
| except Exception as e: | |
| st.write(f"⚠️ An error occurred: {str(e)}", color='red') | |
| def load_data(uploaded_file): | |
| """Loads the uploaded CSV or Excel file into a DataFrame.""" | |
| if uploaded_file.name.endswith('.csv'): | |
| return pd.read_csv(uploaded_file) | |
| else: | |
| return pd.read_excel(uploaded_file) | |
| def preprocess_data(data): | |
| """Preprocesses the data and returns product vectors.""" | |
| data['Product Name'].fillna("", inplace=True) | |
| return vectorizer.transform(data['Product Name']) | |
| def predict_and_score(data, product_vectors): | |
| """Predicts SKUs and calculates similarity scores.""" | |
| data['Predicted SKU'] = rf_classifier.predict(product_vectors) | |
| predicted_sku_vectors = vectorizer.transform(data['Predicted SKU'].astype(str)) | |
| similarity_scores = cosine_similarity(product_vectors, predicted_sku_vectors) | |
| # Update 'Predicted SKU' based on similarity score | |
| for i in range(similarity_scores.shape[0]): | |
| if similarity_scores[i][i] == 0: | |
| data.at[i, 'Predicted SKU'] = "-" | |
| # Create SKU suggestions based on similarity score | |
| data['SKU Suggestion'] = [ | |
| "Propose New SKU" if similarity_scores[i][i] < 0.5 else "No Action Needed" | |
| for i in range(similarity_scores.shape[0]) | |
| ] | |
| # Apply the transformation function to the 'Product Name' column to create a 'Transformed SKU' column | |
| data['SKU Suggestion'] = data.apply( | |
| lambda row: '-' if row['SKU Suggestion'] == "No Action Needed" else transform_to_sku(row['Product Name']), | |
| axis=1 | |
| ) | |
| return data | |
| def display_results(data): | |
| """Displays the processed data and a download link.""" | |
| st.subheader('3. Predicted Results') | |
| # Show a preview of the data with an option to view all | |
| num_rows = st.slider("Select number of rows to view", 5, len(data), 10) | |
| st.write(data.head(num_rows)) | |
| st.subheader('4. Download Results') | |
| st.markdown(get_table_download_link(data), unsafe_allow_html=True) | |
| def get_table_download_link(df): | |
| """Generates a download link for the DataFrame.""" | |
| csv = df.to_csv(index=False) | |
| b64 = base64.b64encode(csv.encode()).decode() | |
| href = f'<a href="data:file/csv;base64,{b64}" download="predicted_data.csv">Download CSV File</a>' | |
| return href | |
| if __name__ == "__main__": | |
| main() | |