app.py

import streamlit as st
import pandas as pd
import joblib

st.set_page_config(page_title="SuperKart Predictor", page_icon="🛒")

# Load model one time only
@st.cache_resource
def load_model():
    return joblib.load("model.joblib")

model = load_model()

st.title("🛒 SuperKart Sales Prediction")
st.write("Enter product details and click **Predict** to estimate sales.")

# Input form
product_weight = st.number_input("Product Weight", 0.0, value=12.0)
product_mrp = st.number_input("Product MRP ($)", 0.0, value=149.99)
sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Medium", "High"])
product_type = st.selectbox("Product Type", ["Home_Office", "Food", "Drinks", "Other"])
store_size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
city_type = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Grocery Store"])

# Predict button
if st.button("Predict Sales"):
    X = pd.DataFrame([{
        "Product_Weight": product_weight,
        "Product_MRP": product_mrp,
        "Product_Sugar_Content": sugar_content,
        "Product_Type": product_type,
        "Store_Size": store_size,
        "Store_Location_City_Type": city_type,
        "Store_Type": store_type,
    }])

    y_pred = model.predict(X)[0]
    st.success(f"Predicted Sales: **${y_pred:,.2f}**")