app.py

import streamlit as st
import requests

st.title("SuperKart Sales Predictor") #Complete the code to define the title of the app.
st.write("This application leverages SuperKart’s nationwide store-level historical data to forecast future sales in Nigerian Naira (NGN), supporting data-driven decision-making across its entire retail network.")
st.write("Input the correct data requested below to get a prediction.")

# Input fields for product and store data
Product_Weight = st.number_input("Product Weight in Kg", value=22.0)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Product Allocated Area in meters", value=0.129) #Complete the code to define the UI element for Product_Allocated_Area
Product_MRP = st.number_input("Product Retail Price in NGN", value=266) #Complete the code to define the UI element for Product_MRP
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"]) #Complete the code to define the UI element for Store_Size
Store_Location_City_Type = st.selectbox("Store Location Type", ["Tier 1", "Tier 2", "Tier 3"]) #Complete the code to define the UI element for Store_Location_City_Type
Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2","Food Mart", "Departmental Store"]) #Complete the code to define the UI element for Store_Type
Product_Id_char = st.selectbox("Product ID", ["FD", "NC", "DR"]) #Complete the code to define the UI element for Product_Id_char
Store_Age_Years = st.number_input("Store Age", min_value=1, max_value=27, value=26) #Complete the code to define the UI element for Store_Age_Years
Product_Type_Category = st.selectbox("Product Category", ["Perishables","Non Perishables"]) #Complete the code to define the UI element for Product_Type_Category

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", type='primary'):
    response = requests.post("https://huggingface.co/proxy/dotunbabayemi-SuperKartSalesPredictionBackend.hf.space/v1/predict", json=product_data)    # Complete the code to enter user name and space name to correctly define the endpoint
    if response.status_code == 200:
        result = response.json()
        predicted_sales = result["Sales"]
        st.write(f"Predicted Product Store Sales Total: NGN{predicted_sales:.2f}")
    else:
        st.error("Error in API request")