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import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("SuperKart Sales Revenue Forecasting")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for product-store features
#Product_Id = st.text_input("Product ID (e.g., FD123)")
Product_Weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.1, value=1.0)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Allocated Display Area Ratio (0.0 to 1.0)", min_value=0.0, max_value=1.0, step=0.01, value=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 (in ₹)", min_value=1.0, step=1.0, value=100.0)
#Store_Id = st.text_input("Store ID (e.g., S012)")
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1980, max_value=2025, step=1, value=2015)
Store_Size = st.selectbox("Store Size", ["High", "Medium", "Small"])
Store_Location_City_Type = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
#'Product_Id': Product_Id,
'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
}])
# Predict button logic
if st.button("Predict"):
try:
# Send POST request to backend API
response = requests.post(
"https://viveksardey-SuperKartSalesRevPredictionBackend.hf.space/v1/forecast",
json=input_data.to_dict(orient='records')[0]
)
# Process response
if response.status_code == 200:
prediction = response.json()['Predicted_Sales_Revenue']
st.success(f"Predicted Sales Revenue: $ {prediction}")
else:
st.error(f"Prediction failed! Status Code: {response.status_code}")
except Exception as e:
st.error(f"An error occurred: {e}")
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