import pandas as pd import streamlit as st import numpy as np import pickle # Load the saved components: with open("rf_model.pkl", "rb") as f: components = pickle.load(f) # Extract the individual components num_imputer = components["num_imputer"] cat_imputer = components["cat_imputer"] encoder = components["encoder"] scaler = components["scaler"] dt_model = components["models"] st.image("https://pbs.twimg.com/media/DywhyJiXgAIUZej?format=jpg&name=medium") st.title("Sales Prediction App") st.caption("This app predicts sales patterns of Corporation Favorita over time in different stores in Ecuador based on the inputs.") # Sidebar with input field descriptions st.sidebar.header("Description of The Required Input Fields") st.sidebar.markdown("**Store Number**: The number of the store.") st.sidebar.markdown("**Product Family**: Product Family such as 'AUTOMOTIVE', 'BEAUTY', etc. " "Details:\n" " - **AUTOMOTIVE**: Products related to the automotive industry.\n" " - **BEAUTY**: Beauty and personal care products.\n" " - **CELEBRATION**: Products for celebrations and special occasions.\n" " - **CLEANING**: Cleaning and household maintenance products.\n" " - **CLOTHING**: Clothing and apparel items.\n" " - **FOODS**: Food items and groceries.\n" " - **GROCERY**: Grocery products.\n" " - **HARDWARE**: Hardware and tools.\n" " - **HOME**: Home improvement and decor products.\n" " - **LADIESWEAR**: Women's clothing.\n" " - **LAWN AND GARDEN**: Lawn and garden products.\n" " - **LIQUOR,WINE,BEER**: Alcoholic beverages.\n" " - **PET SUPPLIES**: Products for pets and animals.\n" " - **STATIONERY**: Stationery and office supplies.") st.sidebar.markdown("**Number of Items on Promotion**: Number of items on promotion within a particular shop.") st.sidebar.markdown("**City**: City where the store is located.") st.sidebar.markdown("**Cluster**: Cluster number which is a grouping of similar stores.") st.sidebar.markdown("**Transactions**: Number of transactions.") st.sidebar.markdown("**Crude Oil Price**: Daily Crude Oil Price.") # Create the input fields input_data = {} col1,col2,col3 = st.columns(3) with col1: input_data['store_nbr'] = st.slider("Store Number",0,54) input_data['family'] = st.selectbox("Product Family", ['AUTOMOTIVE', 'BEAUTY', 'CELEBRATION', 'CLEANING', 'CLOTHING', 'FOODS', 'GROCERY', 'HARDWARE', 'HOME', 'LADIESWEAR', 'LAWN AND GARDEN', 'LIQUOR,WINE,BEER', 'PET SUPPLIES', 'STATIONERY']) input_data['onpromotion'] =st.number_input("Number of Items on Promotion",step=1) input_data['state'] = st.selectbox("State Where The Store Is Located", ['Pichincha', 'Cotopaxi', 'Chimborazo', 'Imbabura', 'Santo Domingo de los Tsachilas', 'Bolivar', 'Pastaza', 'Tungurahua', 'Guayas', 'Santa Elena', 'Los Rios', 'Azuay', 'Loja', 'El Oro', 'Esmeraldas', 'Manabi']) input_data['transactions'] = st.number_input("Number of Transactions", step=1) with col2: input_data['store_type'] = st.selectbox("Store Type",['A', 'B', 'C', 'D', 'E']) input_data['cluster'] = st.selectbox("Cluster", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]) input_data['dcoilwtico'] = st.number_input("Crude Oil Price",step=1) input_data['year'] = st.number_input("Year",step=1) with col3: input_data['month'] = st.slider("Month",1,12) input_data['day'] = st.slider("Day",1,31) input_data['dayofweek'] = st.number_input("Day of Week (0=Sunday and 6=Satruday)",step=1) # Create a button to make a prediction if st.button("Predict"): # Convert the input data to a pandas DataFrame input_df = pd.DataFrame([input_data]) # Product Categorization Based on Families food_families = ['BEVERAGES', 'BREAD/BAKERY', 'FROZEN FOODS', 'MEATS', 'PREPARED FOODS', 'DELI', 'PRODUCE', 'DAIRY', 'POULTRY', 'EGGS', 'SEAFOOD'] home_families = ['HOME AND KITCHEN I', 'HOME AND KITCHEN II', 'HOME APPLIANCES'] clothing_families = ['LINGERIE', 'LADYSWARE'] grocery_families = ['GROCERY I', 'GROCERY II'] stationery_families = ['BOOKS', 'MAGAZINES', 'SCHOOL AND OFFICE SUPPLIES'] cleaning_families = ['HOME CARE', 'BABY CARE', 'PERSONAL CARE'] hardware_families = ['PLAYERS AND ELECTRONICS', 'HARDWARE'] # Apply the same preprocessing steps as done during training input_df['family'] = np.where(input_df['family'].isin(food_families), 'FOODS', input_df['family']) input_df['family'] = np.where(input_df['family'].isin(home_families), 'HOME', input_df['family']) input_df['family'] = np.where(input_df['family'].isin(clothing_families), 'CLOTHING', input_df['family']) input_df['family'] = np.where(input_df['family'].isin(grocery_families), 'GROCERY', input_df['family']) input_df['family'] = np.where(input_df['family'].isin(stationery_families), 'STATIONERY', input_df['family']) input_df['family'] = np.where(input_df['family'].isin(cleaning_families), 'CLEANING', input_df['family']) input_df['family'] = np.where(input_df['family'].isin(hardware_families), 'HARDWARE', input_df['family']) categorical_columns = ['family', 'store_type', 'state'] numerical_columns = ['transactions', 'dcoilwtico'] # Impute missing values input_df_cat = input_df[categorical_columns].copy() input_df_num = input_df[numerical_columns].copy() input_df_cat_imputed = cat_imputer.transform(input_df_cat) input_df_num_imputed = num_imputer.transform(input_df_num) # Encode categorical features input_df_cat_encoded = pd.DataFrame(encoder.transform(input_df_cat_imputed).toarray(), columns=encoder.get_feature_names_out(categorical_columns)) # Scale numerical features input_df_num_scaled = scaler.transform(input_df_num_imputed) input_df_num_sc = pd.DataFrame(input_df_num_scaled, columns=numerical_columns) # Combine encoded categorical features and scaled numerical features input_df_processed = pd.concat([input_df_num_sc, input_df_cat_encoded], axis=1) # Make predictions using the trained model predictions = dt_model.predict(input_df_processed) # Display the predicted sales value to the user: st.write("The predicted sales are:", predictions[0])