predict_page.py

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)