import streamlit as st
import requests

st.set_page_config(page_title="SuperKart Sales Forecast", layout="centered")
st.title("SuperKart Sales Forecast")

st.write("Predict **Product_Store_Sales_Total** using product and store attributes.")

API_URL = "https://bmax16634-superkart-backend.hf.space/predict"

product_id = st.text_input("Product_Id", "FD123")
product_weight = st.number_input("Product_Weight", min_value=0.0, value=1.0)
product_sugar = st.selectbox("Product_Sugar_Content", ["low sugar", "regular", "no sugar"])
allocated_area = st.number_input("Product_Allocated_Area", min_value=0.0, value=0.05)
product_type = st.text_input("Product_Type", "dairy")
mrp = st.number_input("Product_MRP", min_value=0.0, value=100.0)

store_id = st.text_input("Store_Id", "S001")
store_year = st.number_input("Store_Establishment_Year", min_value=1950, max_value=2030, value=2010)
store_size = st.selectbox("Store_Size", ["low", "medium", "high"])
city_tier = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox(
    "Store_Type",
    ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"]
)

payload = {
    "Product_Id": product_id,
    "Product_Weight": product_weight,
    "Product_Sugar_Content": product_sugar,
    "Product_Allocated_Area": allocated_area,
    "Product_Type": product_type,
    "Product_MRP": mrp,
    "Store_Id": store_id,
    "Store_Establishment_Year": store_year,
    "Store_Size": store_size,
    "Store_Location_City_Type": city_tier,
    "Store_Type": store_type,
}

if st.button("Predict Sales"):
    try:
        response = requests.post(API_URL, json=payload, timeout=30)
        response.raise_for_status()
        prediction = response.json()["predictions"][0]
        st.success(f"Predicted Sales Revenue: **{prediction:,.2f}**")
    except Exception as e:
        st.error(f"Prediction failed: {e}")