app.py

import streamlit as st
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download

st.set_page_config(page_title="SuperKart Sales Predictor", layout="centered")
st.title("SuperKart Sales Prediction")
st.write("Predict **Product_Store_Sales_Total** using the trained SuperKart model hosted on Hugging Face.")

MODEL_REPO = "atsh2846/superkart_model"
MODEL_FILE = "superkart_model.joblib"

@st.cache_resource
def load_model():
    model_path = hf_hub_download(
        repo_id=MODEL_REPO,
        filename=MODEL_FILE,
        repo_type="model"
    )
    return joblib.load(model_path)

model = load_model()

st.subheader("Enter product & store details")

# Inputs (match your dataset columns)
Product_Id = st.text_input("Product_Id", value="NC6218")
Product_Weight = st.number_input("Product_Weight", value=10.0)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Product_Allocated_Area", value=1200.0)
Product_Type = st.text_input("Product_Type", value="Snack Foods")
Product_MRP = st.number_input("Product_MRP", value=150.0)

Store_Id = st.text_input("Store_Id", value="OUT049")
Store_Establishment_Year = st.number_input("Store_Establishment_Year", value=2005)
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", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"])

if st.button("Predict Sales"):
    row = {
        "Product_Id": Product_Id,
        "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_Id": Store_Id,
        "Store_Establishment_Year": Store_Establishment_Year,
        "Store_Size": Store_Size,
        "Store_Location_City_Type": Store_Location_City_Type,
        "Store_Type": Store_Type,
    }
    X = pd.DataFrame([row])
    pred = model.predict(X)[0]
    st.success(f"Predicted Product_Store_Sales_Total: {pred:.2f}")