import streamlit as st
import requests

API_URL = "https://Hunagypsy-superkart-backend.hf.space/v1/predict"

st.title("Product Store Sales Prediction App")

# User Inputs
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.selectbox("Product Allocated Area", ["Small", "Medium", "Large"])
Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.0)
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", ["Type 1", "Type 2", "Type 3", "Type 4"])
Product_Id_char = st.text_input("Product ID (char)", value="FDX")
Store_Age_Years = st.number_input("Store Age (Years)", min_value=0, value=5)
Product_Type_Category = st.selectbox("Product Type Category", ["Food", "Non-Food", "Drinks"])

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
}

if st.button("Predict"):
    try:
        response = requests.post(API_URL, json=product_data)
        if response.status_code == 200:
            result = response.json()
            st.success(f"Predicted Product Store Sales Total: ₹{result['Sales']:.2f}")
        else:
            st.error(f"API request failed: {response.status_code}")
    except Exception as e:
        st.error(f"Error: {{e}}")