import streamlit as st
import pandas as pd
import requests

# Streamlit UI for Customer Churn Prediction
st.title("SuperKart Sales Prediction App")
st.write("This tool predicts SuperKart Store Product Sales based on their details. Enter the required information below.")

# Collect user input based on dataset columns
Product_Id = st.text_input("Enter a Product Identifier. This should start with one of the 3 Product Code values ('FD', 'NC', 'DR') followed by a 2/3 digit number.")
Product_Weight = st.number_input("Product Weight", min_value=1, max_value=99)
Product_Sugar_Content = st.selectbox("Select the Product Sugar Content Level", ['No Sugar', 'Low Sugar', 'Regular'])
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.004, max_value=0.3)
Product_Type = st.selectbox("Select the Product Sugar Content Level", ['Frozen Foods','Dairy','Canned','Baking Goods','Health and Hygiene','Snack Foods','Meat','Household','Hard Drinks','Fruits and Vegetables','Breads','Soft Drinks','Breakfast','Others','Starchy Foods','Seafood'])
Product_MRP = st.number_input("Product MRP", min_value=1, max_value=500)
Store_Id = st.selectbox("Select Store ID", ['OUT001', 'OUT002', 'OUT003', 'OUT004'])
Store_Establishment_Year = st.selectbox("Store Establishment Year", [1987, 1998, 1999, 2009])
Store_Size = st.selectbox("Select the correct store size?", ['High', 'Medium', 'Small'])
Store_Location_City_Type = st.selectbox("Select the Store Location City type", ['Tier 1', 'Tier 2', 'Tier 3'])
Store_Type = st.selectbox("Select the Store Type", ['Departmental Store', 'Food Mart', 'Supermarket Type2', 'Supermarket Type1'])

# Convert categorical inputs to match model training
customer_data = {
    '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_Id': Store_Id,
    'Store_Establishment_Year': Store_Establishment_Year,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type
}

if st.button("Predict", type='primary'):
    response = requests.post("https://SandeepMM-Backend.hf.space/v1/product", json=customer_data)    # enter user name and space name before running the cell
    if response.status_code == 200:
        result = response.json()
        sales_prediction = result["Prediction"]  # Extract only the value
        st.write(f"Based on the information provided, the total sales value {sales_prediction}.")
    else:
        st.error("Error in API request")