app.py

import streamlit as st
import numpy as np
import pickle
import pandas as pd

# Load the trained model
def load_model():
    with open("walmart_sales_model.pkl", "rb") as f:
        model = pickle.load(f)
    return model

# Load the model
model = load_model()

# Streamlit UI
def main():
    st.title("🛒 Walmart Sales Prediction")
    st.write("Enter the input features below to predict the weekly sales.")
    
    # User inputs
    store = st.number_input("Enter Store ID", min_value=1, max_value=50, value=1)
    temp = st.number_input("Enter Temperature (Celsius)", value=20.0)
    fuel_price = st.number_input("Enter Fuel Price", value=3.5)
    cpi = st.number_input("Enter CPI", value=200.0)
    unemployment = st.number_input("Enter Unemployment Rate", value=5.0)
    holiday_flag = st.selectbox("Is it a Holiday?", [0, 1])
    
    if st.button("Predict Sales"):
        features = np.array([[store, temp, fuel_price, cpi, unemployment, holiday_flag]])
        prediction = model.predict(features)
        st.success(f"Predicted Weekly Sales: ${prediction[0]:,.2f}")

if __name__ == "__main__":
    main()