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import streamlit as st
import pandas as pd
import joblib
import requests
# Title
st.title("πŸ›’ SuperKart Sales Forecasting")
st.markdown("Enter product and store details to predict sales.")
# Input fields
product_weight = st.number_input("Product Weight (kg)", min_value=1.0, step=0.1)
product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "Sugar Free"])
product_allocated_area = st.slider("Display Area (%)", 0.0, 0.3, 0.05)
product_type = st.selectbox("Product Type", [
"Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks", "Health and Hygiene",
"Baking Goods", "Bread", "Breakfast", "Frozen Foods", "Fruits and Vegetables",
"Household", "Seafood", "Starchy Foods", "Others"
])
product_mrp = st.number_input("Product MRP (β‚Ή)", min_value=10.0, step=1.0)
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location_city_type = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", [
"Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"
])
store_age = st.slider("Store Age (Years)", 1, 40, 15)
# Submit
if st.button("Predict Sales"):
input_data = {
"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_Size": store_size,
"Store_Location_City_Type": store_location_city_type,
"Store_Type": store_type,
"Store_Age": store_age
}
# Call the backend API
try:
response = requests.post("https://<your-backend-username>.huggingface.space/<backend-space-name>/predict", json=input_data)
result = response.json()
if "predicted_sales" in result:
st.success(f"πŸ“ˆ Predicted Sales: β‚Ή{result['predicted_sales']}")
else:
st.error(f"Error: {result.get('error', 'Unknown')}")
except Exception as e:
st.error(f"API call failed: {e}")