import streamlit as st
import requests

# ✅ Hugging Face backend API endpoint (Flask)
API_URL = "https://Akshat747-SuperKart.hf.space/predict"  # change this after deploying Flask backend

st.set_page_config(page_title="SuperKart Sales Predictor", layout="wide")

st.title("🛒 SuperKart Sales Predictor")
st.write("Enter product & store details below to predict sales:")

# Input fields
product_weight = st.number_input("Product Weight", min_value=0.0, format="%.2f")
allocated_area = st.number_input("Product Allocated Area", min_value=0)
mrp = st.number_input("Product MRP", min_value=0.0, format="%.2f")
sugar_content = st.selectbox("Product Sugar Content", ["Low", "Regular", "No Sugar"])
product_type = st.text_input("Product Type", "Snack Foods")
store_size = st.selectbox("Store Size", ["small", "medium", "high"])
est_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2012)
location_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket", "Grocery", "Convenience"])

if st.button("Predict Sales"):
    payload = {
        "Product_Weight": product_weight,
        "Product_Allocated_Area": allocated_area,
        "Product_MRP": mrp,
        "Product_Sugar_Content": sugar_content,
        "Product_Type": product_type,
        "Store_Size": store_size,
        "Store_Establishment_Year": est_year,
        "Store_Location_City_Type": location_type,
        "Store_Type": store_type
    }

    try:
        response = requests.post(API_URL, json=payload)
        if response.status_code == 200:
            result = response.json()
            if "prediction" in result:
                st.success(f"✅ Predicted Sales: **{result['prediction']:.2f} units**")
            else:
                st.error(f"⚠️ Error: {result}")
        else:
            st.error(f"⚠️ Backend Error {response.status_code}")
    except Exception as e:
        st.error(f"⚠️ Connection failed: {e}")