import streamlit as st
import requests
import pandas as pd

# Streamlit UI for Super Kart Store Sales Prediction
st.title("Super Kart Store Sales Predictor Application")
st.write("This tool predicts Store Sales based on store details. Enter the required information below.")

# Collect user input based on dataset columns
Product_Weight = st.number_input("Product Weight", min_value=1.0, value=10.0)
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=0.05)
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1987, max_value=2025)

Product_Sugar_Content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"])
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",
                                             "Starchy Foods", "Breakfast", "Seafood", "Others"])
Store_Id = st.selectbox("Store Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
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", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])

# Convert categorical inputs to match model training
store_data = {
    "Product_Weight": Product_Weight,
    "Product_Allocated_Area": Product_Allocated_Area,
    "Product_MRP": Product_MRP,
    "Store_Establishment_Year": Store_Establishment_Year,    
    "Product_Sugar_Content": Product_Sugar_Content,
    "Product_Type": Product_Type,
    "Store_Id":Store_Id,
    "Store_Location_City_Type": Store_Location_City_Type,
    "Store_Type": Store_Type,
    "Store_Size": Store_Size
} 

if st.button("Predict", type='primary'):
    response = requests.post("https://supravab-supbskartbackend.hf.space/v1/predict", json=store_data)    # enter user name and space name before running the cell
    if response.status_code == 200:
        result = response.json()
        sales_prediction = result["predicted store sales total"]  # Extract only the value
        st.write(f"Based on the information provided, the store sales is likely to {sales_prediction}.")
    else:
        st.error(f"Error in Super Kart API request: {response.status_code} - {response.text}")