import streamlit as st
import pandas as pd
import joblib
import numpy as np

# Load the trained model
@st.cache_resource
def load_model():
    return joblib.load("super_kart_prediction_model_v1_0.joblib")

model = load_model()

# Streamlit UI for Price Prediction
st.title("Super Kart Product total sales Prediction App")
st.write("This tool predicts the total sales of the provided product.")

st.subheader("Enter the listing details:")

# Collect user input
Product_Id = st.text_input("Product Id")
Product_Weight = st.number_input("weight of each product", min_value=0.001, max_value=1000.00)
Product_Sugar_Content = st.selectbox("Product Sugar Content Level", ["Low Sugar", "Regular", "No Sugar", "reg"])
Product_Allocated_Area = st.number_input("Product allocated display area(ratio to total product display area)", min_value=0.001, max_value=1.0)
Product_Type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household", "Baking Goods", "Canned", "Health and Hygiene",
                                             "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Others", "Starchy Foods", "Breakfast", "Seafood"])
Product_MRP = st.number_input("Maximum retail price of the product", min_value=1.00, max_value=10000.00)
Store_Establishment_Year = st.number_input("Year in which the store was established", min_value=1800, max_value=2025)
Store_Size = st.selectbox("Relative size of the store", ["Medium", "High", "Small"])
Store_Location_City_Type = st.selectbox("Type of city in which the store is located", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Id': Product_Id,
    'Product_Weight': Product_Weight,
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Allocated_Area': Product_Allocated_Area,
    'Product_Type': Product_Type,
    'Product_MRP': Product_MRP,
    'Store_Establishment_Year': Store_Establishment_Year,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type
}])

# Predict button
if st.button("Predict"):
    prediction = model.predict(input_data)
    st.write(f"The predicted total sales for the provided product is ${prediction[0]:.2f}.")