app.py

# Streamlit frontend that collects product/store details and calls the Flask backend API

import streamlit as st
import requests

# Backend URL: within the shared Docker network, containers reach each other
# by container name. Defaults to the container name used when running the
# backend container (see deployment instructions).
BACKEND_URL = os.environ.get("BACKEND_URL", "http://superkart-backend:7860")

st.set_page_config(page_title="SuperKart Sales Forecasting", layout="centered")
st.title("SuperKart Sales Forecasting")
st.write("Enter the product and store details below to forecast sales revenue.")

# ----- Input widgets -----
product_weight = st.number_input("Product Weight", min_value=0.0, value=12.66, step=0.1)
product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
product_allocated_area = st.number_input(
    "Product Allocated Area", min_value=0.0, max_value=1.0, value=0.027, step=0.001, format="%.3f"
)
product_mrp = st.number_input("Product MRP", min_value=0.0, value=117.08, step=0.1)
store_size = st.selectbox("Store Size", ["High", "Medium", "Small"])
store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox(
    "Store Type",
    ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Food Mart"],
)
product_id_char = st.selectbox("Product Id Prefix", ["FD", "DR", "NC"])
store_age_years = st.number_input("Store Age (Years)", min_value=0, value=16, step=1)
product_type_category = st.selectbox("Product Type Category", ["Perishables", "Non Perishables"])

if st.button("Predict Sales"):
    payload = {
        "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,
    }
    try:
        response = requests.post(f"{BACKEND_URL}/v1/predict", json=payload)
        response.raise_for_status()
        prediction = response.json()["predicted_Product_Store_Sales_Total"]
        st.success(f"Predicted Sales: {prediction}")
    except Exception as e:
        st.error(f"Error while calling the backend API: {e}")