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("superkart_prediction_model_v1_0.joblib")

model = load_model()

# Streamlit UI for Price Prediction
st.title("SuperKart Sales Prediction App")
st.write("This tool predicts the sales of SuperKart based on the Store details.")

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

# Collect user input
Product_Sugar_Content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
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"])
Store_Id = st.selectbox("Store ID", ["OUT004", "OUT001", "OUT003", "OUT002"])
Store_Size = st.selectbox("Store Size", ["Medium", "High", "Small"])
Store_Location_City_Type = st.selectbox("City Location", ["Tier 2", "Tier 1", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type2", "Supermarket Type1", "Departmental Store", "Food Mart"])
Product_Weight = st.number_input("Weight of the Product", min_value=1, value=2)
Product_Allocated_Area = st.number_input("Are allocated for Products", min_value=1, step=1, value=2)
Product_MRP = st.number_input("MRP of Products", min_value=1, step=1, value=2)
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1, step=1, value=2)


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

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