app.py

import streamlit as st
import pandas as pd
import joblib

# Load trained model
@st.cache_resource
def load_model():
    return joblib.load("t_superkart_sales_prediction_model_v1_0.joblib")

model = load_model()

st.set_page_config(page_title="SuperKart Sales Predictor", layout="wide")
st.title("🛒 SuperKart Sales Prediction App")

st.write("Fill in the product and store details below to predict sales.")

# Input fields
col1, col2 = st.columns(2)

with col1:
    product_weight = st.number_input("Product Weight", min_value=0.0, step=0.01)
    product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, step=0.001)
    product_mrp = st.number_input("Product MRP", min_value=0.0, step=0.01)
    product_store_sales_total = st.number_input("Product Store Sales Total", min_value=0.0, step=0.01)
    store_age = st.number_input("Store Age (Years)", min_value=0, step=1)

with col2:
    product_sugar_content = st.selectbox("Sugar Content", ["no sugar", "regular"])
    product_type = st.selectbox(
        "Product Type",
        ["breads", "breakfast", "canned", "dairy", "frozen foods",
         "fruits and vegetables", "hard drinks", "health and hygiene",
         "household", "meat", "others", "seafood", "snack foods",
         "soft drinks", "starchy foods"]
    )
    store_size = st.selectbox("Store Size", ["small", "medium"])
    store_location_city_type = st.selectbox("Store Location City Type", ["tier 2", "tier 3"])
    store_type = st.selectbox("Store Type", ["food mart", "supermarket type1", "supermarket type2"])
    product_group_code = st.selectbox("Product Group Code", ["fd", "nc"])

# Convert inputs to DataFrame
input_data = {
    "Product_Weight": product_weight,
    "Product_Allocated_Area": product_allocated_area,
    "Product_MRP": product_mrp,
    "Product_Store_Sales_Total": product_store_sales_total,
    "Store_Age": store_age,
    f"Product_Sugar_Content_{product_sugar_content}": True,
    f"Product_Type_{product_type}": True,
    f"Store_Size_{store_size}": True,
    f"Store_Location_City_Type_{store_location_city_type}": True,
    f"Store_Type_{store_type}": True,
    f"Product_Group_Code_{product_group_code}": True
}

input_df = pd.DataFrame([input_data]).astype(object)

# Align with model features
if hasattr(model, "feature_names_in_"):
    input_df = input_df.reindex(columns=model.feature_names_in_, fill_value=0)

# Prediction
if st.button("Predict"):
    try:
        prediction = model.predict(input_df)[0]
        st.success(f"Predicted Product Sales: {prediction:.2f}")
    except Exception as e:
        st.error(f"Prediction failed: {e}")