import requests
import streamlit as st

st.title("SuperKart Sales Predictor")

# Input fields for product and store data (same as LC)
Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=0.05)
Product_MRP = st.number_input("Product MRP", min_value=0.0, value=200.0)
Store_Size = st.selectbox("Store Size", ["High", "Medium", "Low"])
Store_Location_City_Type = 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"])
Product_Id_char = st.text_input("Product ID (first 2 letters)", "FD")
Store_Age_Years = st.number_input("Store Age (years)", min_value=0, value=10)
Product_Type_Category = st.selectbox(
    "Product Type Category",
    [
        "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks",
        "Health and Hygiene", "Baking Goods", "Bread", "Breakfast",
        "Frozen Foods", "Fruits and Vegetables", "Household", "Seafood",
        "Starchy Foods", "Others"
    ]
)

# --- Minimal additions to satisfy backend ---
# Store_Id expected by pipeline
Store_Id = st.number_input("Store Id", min_value=1, value=1, step=1)
# Product_Type_Clean expected by pipeline. If your training cleaned/normalized names,
# the simplest safe fallback is to pass the selected category through unchanged:
Product_Type_Clean = Product_Type_Category  # keep identical unless your backend requires specific cleaning

# Payload (same keys as LC + 2 required by backend)
product_data = {
    "Product_Weight": Product_Weight,
    "Product_Sugar_Content": Product_Sugar_Content,
    "Product_Allocated_Area": Product_Allocated_Area,
    "Product_MRP": Product_MRP,
    "Store_Size": Store_Size,
    "Store_Location_City_Type": Store_Location_City_Type,
    "Store_Type": Store_Type,
    "Product_Id_char": Product_Id_char,
    "Store_Age_Years": Store_Age_Years,
    "Product_Type_Category": Product_Type_Category,
    "Store_Id": int(Store_Id),
    "Product_Type_Clean": Product_Type_Clean,
}

# Call backend (same flow as LC; minimal safety added)
if st.button("Predict", type="primary"):
    try:
        response = requests.post(
            "https://johnny-five-c-SuperKartBackend.hf.space/v1/predict",
            json=product_data,
            timeout=15
        )
        if response.status_code == 200:
            result = response.json()
            predicted_sales = result.get("result", result.get("prediction"))
            if isinstance(predicted_sales, list):
                predicted_sales = predicted_sales[0]
            if predicted_sales is None:
                st.error("Unexpected response format.")
            else:
                st.success(f"Predicted Product_Store Sales Total: {float(predicted_sales):.2f}")
        else:
            st.error(f"Error in API request: {response.status_code} - {response.text}")
    except Exception as e:
        st.error(f"Request failed: {e}")