app.py

import streamlit as st
import pandas as pd
import joblib

st.title("SuperKart Sales Revenue Predictor")

# Load model
@st.cache_resource
def load_model():
    return joblib.load('final_rf_model.pkl')

model = load_model()

# Define all input fields
st.header("Enter Product & Store Details:")
product_weight = st.number_input("Product Weight", min_value=0.0, max_value=50.0, value=12.5)
product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "High"])
product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.05)
product_type = st.selectbox("Product Type", [
    "Fruits and Vegetables", "Snack Foods", "Household", "Dairy", "Baking Goods",
    "Frozen Foods", "Canned", "Soft Drinks", "Breads", "Breakfast", "Health and Hygiene",
    "Meat", "Others", "Seafood", "Starchy Foods", "Hard Drinks"
])
product_mrp = st.number_input("Product MRP", min_value=0.0, max_value=300.0, value=150.0)
store_establishment_year = st.selectbox("Store Establishment Year", list(range(1987, 2010)))
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket Type2", "Supermarket Type1", "Departmental Store", "Food Mart"])

# Predict button
if st.button("Predict Sales"):
    input_df = pd.DataFrame([{
        "Product_Weight": product_weight,
        "Product_Sugar_Content": product_sugar_content,
        "Product_Allocated_Area": product_allocated_area,
        "Product_Type": product_type,
        "Product_MRP": product_mrp,
        "Store_Establishment_Year": store_establishment_year,
        "Store_Size": store_size,
        "Store_Location_City_Type": store_location_city_type,
        "Store_Type": store_type
    }])
    prediction = model.predict(input_df)[0]
    st.success(f"Predicted Quarterly Sales Revenue: ₹ {prediction:,.2f}")