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import streamlit as st
import pandas as pd
import requests
# Set page configuration
st.set_page_config(page_title="SuperKart Sales Forecasting App", layout="centered")
# App title and description
st.title("SuperKart Sales Forecasting App")
st.write(
"This tool predicts the sales revenue of products across SuperKart stores "
"based on product and store attributes."
)
# Section for online prediction
st.subheader("Online Prediction (Single Product-Store Entry)")
# Collect user input for product & store features
product_weight = st.number_input("Product Weight (kg)", min_value=4.0, max_value=22.0, value=12.65, step=0.1)
product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "Non-edible"])
product_allocated_area = st.number_input("Allocated Area (ratio)", min_value=0.004, max_value=0.298, value=0.068, step=0.001)
product_type = st.selectbox("Product Type", [
"Fruits and Vegetables", "Snack Foods", "Dairy", "Canned", "Soft Drinks",
"Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
"Household", "Seafood", "Starchy Foods", "Meat", "Hard Drinks", "Others"
])
product_mrp = st.number_input("Product MRP", min_value=31.0, max_value=266.0, value=147.0, step=1.0)
store_id = st.selectbox("Store ID", ["OUT001","OUT002","OUT003","OUT004"])
store_establishment_year = st.number_input("Store Establishment Year", min_value=1987, max_value=2025, value=2002, step=1)
store_size = st.selectbox("Store Size", ["High", "Medium", "Low"])
store_location_city_type = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Food Mart"])
# Input DataFrame with EXACT column names as training
input_data = pd.DataFrame([{
"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_Id": "store_id",
"Store_Establishment_Year": store_establishment_year,
"Store_Size": store_size,
"Store_Location_City_Type": store_location_city_type,
"Store_Type": store_type
}])
# Make prediction when "Forecast Sales" is clicked
if st.button("Forecast Sales"):
try:
response = requests.post(
"https://nsa9-ProductStoreSalesBackend.hf.space/v1/forecast", # Flask backend endpoint
json=input_data.to_dict(orient="records")[0]
)
if response.status_code == 200:
# First, get the JSON data
response_json = response.json()
# Then, check if the key exists before trying to access it
if "Predicted Sales (in dollars)" in response_json:
prediction = response_json.get("Predicted Sales (in dollars)")
st.success(f"Predicted Sales Revenue: ${prediction:,.2f}")
else:
st.error("Invalid response format from backend. The expected key 'Predicted Sales (in dollars)' was not found.")
st.write("Full response from backend:", response_json)
else:
st.error(f"Error from backend: {response.status_code}")
st.write("Full response text:", response.text)
except requests.exceptions.RequestException as e:
st.error(f"Request failed: {e}")
# Section for batch prediction
st.subheader("Batch Prediction (Upload CSV)")
uploaded_file = st.file_uploader("Upload a CSV file with multiple product-store entries", type=["csv"])
if uploaded_file is not None and st.button("Predict Batch"):
try:
response = requests.post(
"https://nsa9-ProductStoreSalesBackend.hf.space/v1/forecastbatch", # Flask backend batch endpoint
files={"file": uploaded_file}
)
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
st.write(pd.DataFrame(predictions, index=["Predicted Sales"])) # Display nicely as a table
else:
st.error(f"Error from backend: {response.status_code}")
except Exception as e:
st.error(f"Batch request failed: {e}")
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