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import streamlit as st
import pandas as pd
import requests
st.title("🛒 SuperKart Sales Forecast App")
st.markdown("""
Use this interactive interface to forecast product-level sales for SuperKart stores based on store and product attributes.
""")
API_URL = "https://jkng77433-Backend.hf.space/v1/forecast/single"
st.header("Single Prediction")
col1, col2 = st.columns(2)
with col1:
product_id = st.text_input("Product ID", "FD6114")
product_type = st.selectbox("Product Type", ["Frozen Foods", "Dairy", "Canned", "Snack Foods"])
sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
product_weight = st.number_input("Product Weight", min_value=0.1, value=12.66)
product_mrp = st.number_input("Product MRP", min_value=0.0, value=117.08)
with col2:
store_id = st.text_input("Store ID", "OUT004")
store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
est_year = st.number_input("Store Establishment Year", min_value=1980, max_value=2025, value=2009)
allocated_area = st.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.027, format="%.3f")
if st.button("Predict Sales"):
payload = {
"Product_Id": product_id,
"Product_Type": product_type,
"Product_Sugar_Content": sugar_content,
"Product_Weight": product_weight,
"Product_MRP": product_mrp,
"Store_Id": store_id,
"Store_Type": store_type,
"Store_Size": store_size,
"Store_Location_City_Type": store_location,
"Store_Establishment_Year": est_year,
"Product_Allocated_Area": allocated_area
}
response = requests.post(API_URL, json=payload)
if response.status_code == 200:
result = response.json()
st.success(f"Predicted Sales: **${result['Predicted_Product_Store_Sales_Total']:.2f}**")
else:
st.error("Prediction failed — check input values or backend availability.")
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