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import streamlit as st
import pickle
import numpy as np
import pandas as pd
#from sklearn.impute import SimpleImputer
#from xgboost import XGBRegressor
#from sklearn.preprocessing import LabelEncoder
#from sklearn.preprocessing import StandardScaler
import joblib
# Load the final model
regressor = joblib.load("Best_model.joblib")
#@st.cache_resource()
def show_predict_page():
# Add a title and subtitle
st.write("<center><h1>Predicting Sales App</h1></center>", unsafe_allow_html=True)
# Add a subtitle or description
st.write("This app predict sales by the using machine learning, based on certain input parameters. Simply enter the required information and click 'Predict' to get a sales prediction!")
st.subheader("Enter the following details to predict sales")
input_data = {
'store_nbr': st.slider("store_nbr", step=1, min_value=0, max_value=54),
'onpromotion': st.number_input("onpromotion, 0 - 800", min_value=0, max_value=800),
'transactions': st.number_input("Number of Transactions, 0 - 10000", min_value=0, max_value=10000),
'oil_price': st.number_input("oil_price, 1 - 200", step=1, min_value=0, max_value=200),
'cluster': st.slider("cluster", step=1, min_value=0, max_value=17),
'day': st.slider("day", 1, 31, 1),
'year': st.selectbox("year", [1970]),
'month': st.slider("month", 1, 12, 1),
#'dayofmonth': st.slider("dayofmonth", 1, 31, 1),
#'dayofweek': st.slider("dayofweek, 0=Sun and 6=Sat", step=1, min_value=1, max_value=6),
'family': st.selectbox("products", ['AUTOMOTIVE', 'Personal Care', 'Beverages', 'STATIONERY', 'Food', 'CLEANING', 'HARDWARE', 'Home and Kitchen', 'Clothing', 'PET SUPPLIES', 'ELECTRONICS']),
'holiday_type': st.selectbox("holiday_type", ['Workday', 'holiday']),
'city': st.selectbox("City", ['Salinas', 'Quito', 'Cayambe', 'Latacunga', 'Riobamba', 'Ibarra', 'Santo Domingo', 'Guaranda', 'Ambato', 'Guayaquil', 'Daule', 'Babahoyo', 'Quevedo', 'Playas', 'Cuenca', 'Loja', 'Machala', 'Esmeraldas', 'El Carmen', 'Libertad', 'Manta', 'Puyo'])
}
# Create a button to make a prediction
if st.button("Predict", key="predict_button", help="Click to make a prediction."):
# Convert the input data to a pandas DataFrame
input_df = pd.DataFrame([input_data])
# Make a prediction
prediction = round(regressor.predict(input_df)[0], 2)
# Display the prediction
#st.write(f"The predicted sales are: {prediction}.")
# Display the prediction
st.subheader("Sales Prediction")
st.write("The predicted sales for the company is:", prediction) |