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| import streamlit as st | |
| import joblib | |
| import pickle | |
| import pandas as pd | |
| # Load the model from disk | |
| model = joblib.load('rf.sav') | |
| # Load the model from disk | |
| with open('scaler.pkl', 'rb') as file: | |
| scaler = pickle.load(file) | |
| st.title('Rossmann Sales Prediction App') | |
| st.write('This app takes in the several input parameters and predict the sales for a particular day of a 1115 rossmann stores.') | |
| store = int(st.number_input('Store Number (select between 1-1115)', step=1, min_value=1, max_value=1115)) | |
| # st.write('Store Number is', store) | |
| week_days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] | |
| week_days_mapping = {'Monday':1, 'Tuesday':2, 'Wednesday':3, 'Thursday':4, 'Friday':5, 'Saturday':6, 'Sunday':7} | |
| week_days_input = st.selectbox('Select the day of the week', week_days) | |
| # st.write(week_days_input) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| promo = ['yes', 'no'] | |
| promo_map = {'yes':1, 'no':0} | |
| promo_or_not = st.selectbox('Promotion was opted or not?', promo) | |
| with col2: | |
| school_holiday = ['yes', 'no'] | |
| school_map = {'yes':1, 'no':0} | |
| school_holiday_or_not = st.selectbox('Is there a School Holiday?', school_holiday) | |
| col3, col4 = st.columns(2) | |
| with col3: | |
| year = st.number_input('Enter the year:',step=1, min_value=1973, max_value=2025) | |
| with col4: | |
| months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] | |
| month_map = {'January': 1, 'February': 2, 'March': 3, 'April': 4, 'May': 5, 'June': 6, | |
| 'July': 7, 'August': 8, 'September': 9, 'October': 10, 'November': 11, 'December': 12} | |
| select_month = st.selectbox('Select the month', months) | |
| days = st.number_input('Enter the day number for which you want to predict:', step=1, min_value=1, max_value=31) | |
| stores_type = ['a', 'b', 'c', 'd'] | |
| stores_type_input = st.selectbox('Select the type of store', stores_type) | |
| assortment_type = ['basic', 'extra', 'extended'] | |
| assortment_type_map = {'basic':'a', 'extra':'b', 'extended':'c'} | |
| assortment_type_input = st.selectbox('Select the assortment type (variations in the product)', assortment_type) | |
| customers = st.number_input('Enter the expected number of customers', step=5, min_value=5, max_value=7500) | |
| comp_distance = st.number_input('Enter the distance in meters to the nearest competitor store', step=1) | |
| comp_open_since_month = float(st.number_input('The month is which the nearest competitor store was opened (1-12)', step=1, min_value=1, max_value=12)) | |
| comp_open_since_year = float(st.number_input('The year in which the nearest competitor store was opened', step=1, min_value=1973, max_value=2025)) | |
| competition_open = (12* (year-comp_open_since_year)) + (month_map[select_month] - (comp_open_since_month)) | |
| if stores_type_input == 'a': | |
| store_type_value_b = 0 | |
| store_type_value_c = 0 | |
| store_type_value_d = 0 | |
| if stores_type_input == 'b': | |
| store_type_value_b = 1 | |
| store_type_value_c = 0 | |
| store_type_value_d = 0 | |
| if stores_type_input == 'c': | |
| store_type_value_b = 0 | |
| store_type_value_c = 1 | |
| store_type_value_d = 0 | |
| if stores_type_input == 'd': | |
| store_type_value_b = 0 | |
| store_type_value_c = 0 | |
| store_type_value_d = 1 | |
| if assortment_type_input == 'basic': | |
| assortment_b = 0 | |
| assortment_c = 0 | |
| if assortment_type_input == 'extra': | |
| assortment_b = 1 | |
| assortment_c = 0 | |
| if assortment_type_input == 'extended': | |
| assortment_b = 0 | |
| assortment_c = 1 | |
| if st.button('Predict Sales'): | |
| try: | |
| final_dict = {'Store':store, 'day_of_week':week_days_mapping[week_days_input], | |
| 'promotion':promo_map[promo_or_not], 'school holiday':school_map[school_holiday_or_not], | |
| 'year':year, 'month':month_map[select_month], 'day':days, 'store b':store_type_value_b, | |
| 'store c':store_type_value_c, 'store d':store_type_value_d, 'assortment b':assortment_b, | |
| 'assortment c':assortment_c, 'customers':customers, 'Comp Dist':comp_distance, | |
| 'Comp_open': competition_open} | |
| final_df = pd.DataFrame([final_dict]) | |
| final_df_scaled = scaler.transform(final_df) | |
| # st.write(final_df_scaled) | |
| # st.write(final_df) | |
| sales = model.predict(final_df_scaled) | |
| st.write('The sales for this particular day of the store you selected is:', sales[0]) | |
| except Exception as e: | |
| st.error('There is something wrong, please enter the correct inputs', e) | |