app.py

import streamlit as st
import requests

# Page Configuration
st.set_page_config(
    page_title="SuperKart Sales Predictor",
    page_icon=":shopping_cart:",
    layout="centered"
)

# App Title and User Guidance
st.title("Ramakanth's SuperKart Sales Predictor")
st.markdown("""
Welcome to the **SuperKart Sales Forecasting Tool**\!

This application predicts the **total product-store sales revenue** for a given
product and store combination using a trained **XGBoost model** deployed on
Hugging Face Spaces.

**How to use:**
1. Fill in the **Product Details** and **Store Details** below.
2. All fields are required. Hover over each label to see valid values and ranges.
3. Click **Predict Sales** to get an instant forecast.
""")

st.divider()

# Input Form -- Product Details
st.subheader("Product Details")
col1, col2 = st.columns(2)

with col1:
    Product_Weight = st.number_input(
        "Product Weight (kg)",
        min_value=4.0, max_value=22.0, value=12.66, step=0.01,
        help="Weight of the product in kilograms. Valid range: 4.0 to 22.0 kg."
    )
    Product_Allocated_Area = st.number_input(
        "Product Allocated Area (ratio)",
        min_value=0.004, max_value=0.298, value=0.068, step=0.001,
        format="%.3f",
        help="Ratio of display area for this product to total store display. Range: 0.004 to 0.298."
    )
    Product_MRP = st.number_input(
        "Product MRP (INR)",
        min_value=31.0, max_value=266.0, value=147.0, step=0.5,
        help="Maximum Retail Price of the product in Indian Rupees. Range: 31 to 266."
    )

with col2:
    Product_Sugar_Content = st.selectbox(
        "Sugar Content",
        ["Low Sugar", "Regular", "No Sugar"],
        help="Sugar content classification: Low Sugar / Regular / No Sugar."
    )
    Product_Id_char = st.selectbox(
        "Product ID Prefix",
        ["FD", "DR", "NC"],
        help="Two-letter prefix of the Product ID: FD=Food, DR=Drinks, NC=Non-Consumable."
    )
    Product_Type_Category = st.selectbox(
        "Product Type Category",
        ["Perishables", "Non Perishables"],
        help="Perishables: dairy, meat, fruits. Non-Perishables: canned, household, health."
    )

# Input Form -- Store Details
st.subheader("Store Details")
col3, col4 = st.columns(2)

with col3:
    Store_Size = st.selectbox(
        "Store Size",
        ["High", "Medium", "Low"],
        index=1,
        help="Physical size: High=large supermarket, Medium=standard, Low=small food mart."
    )
    Store_Location_City_Type = st.selectbox(
        "City Type",
        ["Tier 1", "Tier 2", "Tier 3"],
        index=1,
        help="Tier 1=metro cities, Tier 2=mid-size cities, Tier 3=smaller towns."
    )

with col4:
    Store_Type = st.selectbox(
        "Store Type",
        ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"],
        index=2,
        help="Store format based on product range offered."
    )
    Store_Age_Years = st.number_input(
        "Store Age (Years)",
        min_value=0, max_value=100, value=26, step=1,
        help="Number of years since the store was established."
    )

st.divider()

# Prediction Button
if st.button("Predict Sales", type="primary", use_container_width=True):
    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
    }
    with st.spinner("Contacting prediction API -- please wait..."):
        try:
            response = requests.post(
                "https://ramzai9-superkartprediction.hf.space/v1/predict",
                json=payload,
                timeout=60
            )
            if response.status_code == 200:
                predicted_sales = response.json()["Sales"]
                st.success(f"Predicted Product Store Sales Total: INR {predicted_sales:,.2f}")
                st.balloons()
            elif response.status_code == 400:
                st.error(f"Validation Error: {response.json().get('error', 'Bad request')}")
            else:
                st.error(f"API Error (HTTP {response.status_code}): {response.text}")
        except requests.exceptions.Timeout:
            st.warning(
                "Request timed out. The backend space may be waking up -- "
                "please wait 30 seconds and try again."
            )
        except requests.exceptions.ConnectionError:
            st.error(
                "Could not reach the prediction backend. "
                "Please verify the backend Hugging Face Space is running."
            )
        except Exception as e:
            st.error(f"Unexpected error: {str(e)}")

st.caption("SuperKart Sales Predictor | Powered by XGBoost & Streamlit | Deployed on Hugging Face Spaces")