import streamlit as st
import pandas as pd
import requests
import os

st.set_page_config(page_title="SuperKart Forecast", layout="centered")
st.title("🛒 SuperKart Quarterly Sales Forecast")
st.write("Enter details below, then click 🔮 Predict.")

BACKEND_URL = os.getenv("BACKEND_URL", "$BACKEND_URL")

with st.form("forecast_form"):
    c1, c2 = st.columns(2)
    with c1:
        pw   = st.number_input("Product Weight (kg)", 0.0,100.0,12.5,0.1)
        pa   = st.number_input("Allocated Area Ratio",0.0,1.0,0.08,0.005)
        mrp  = st.number_input("Product MRP (₹)",     0.0,1000.0,50.0,1.0)
        year = st.number_input("Store Established Year",1900,2025,2015,1)
        size = st.selectbox("Store Size", ["low","medium","high"])
    with c2:
        city   = st.selectbox("City Tier", ["Tier 1","Tier 2","Tier 3"])
        stype  = st.selectbox("Store Type", [
            "Departmental Store","Supermarket Type 1",
            "Supermarket Type 2","Food Mart"
        ])
        prefix= st.text_input("Product Prefix","FD")
        pnum  = st.number_input("Product Numeric ID",0,100000,6114,1)
        age   = st.number_input(
                  "Store Age (yrs)",0,50,
                  int(pd.Timestamp.now().year - year),1
                )
    submit = st.form_submit_button("🔮 Predict")

if submit:
    payload={"data":[{
        "Product_Weight":pw,
        "Product_Allocated_Area":pa,
        "Product_MRP":mrp,
        "Store_Establishment_Year":year,
        "Store_Size":size,
        "Store_Location_City_Type":city,
        "Store_Type":stype,
        "Product_Prefix":prefix,
        "Product_Num":pnum,
        "Store_Age":age
    }]}
    try:
        r = requests.post(f"{BACKEND_URL}/predict", json=payload, timeout=10)
        r.raise_for_status()
        pred = r.json()["predictions"][0]
        st.success(f"🚀 Forecasted Sales: ₹{pred:,.2f}")
    except Exception as e:
        st.error(f"❌ Prediction error: {e}")