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import streamlit as st
import pandas as pd
import joblib
import numpy as np
# Load the trained model
@st.cache_resource
def load_model():
return joblib.load("SuperKart_Model_V1_0.joblib")
model = load_model()
# Streamlit UI for Price Prediction
st.title("SuperKart Revenue Prediction App")
st.write("This tool predicts the sales revenue listing based on the given details.")
st.subheader("Enter the listing details:")
# Collect user input
Product_Type = st.selectbox("Product_Type", ["Fruits and Vegetables ", "Snack Foods", "Frozen Foods","Dairy","Household","Baking Goods","Canned","Health and Hygiene","Meat","Soft Drinks","Breads","Hard Drinks","Others","Starchy Foods ","Breakfast","Seafood"])
Product_Weight = st.number_input("Product_Weight", min_value=0.0, value=12.66)
Product_MRP = st.number_input("Product_MRP",min_value=0.0, value=100.0)
Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.0, value=100.0)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "No Sugar", "Regular", "reg"])
Store_Type = st.selectbox("Store_Type", ["Supermarket Type2 ", "Supermarket Type1","Departmental Store","Food Mart"])
Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ["Tier 2", "Tier 1","Tier 3"])
Store_Id = st.selectbox("Store_Id",["OUT004","OUT003","OUT002","OUT001"])
Store_Establishment_Year = st.number_input(
"Store_Establishment_Year",
min_value=1900,
max_value=2025,
step=1,
value=2000,
format='%d'
)
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'Product_Type': Product_Type,
'Product_Weight': Product_Weight,
'Product_MRP': Product_MRP,
'Product_Allocated_Area': Product_Allocated_Area,
'Product_Sugar_Content': Product_Sugar_Content,
'Store_Type': Store_Type,
'Store_Location_City_Type': Store_Location_City_Type,
'Store_Id': Store_Id,
'Store_Establishment_Year': Store_Establishment_Year,
}])
# Predict button
if st.button("Predict"):
prediction = model.predict(input_data)
#st.write(f"The predicted revnue is ${np.exp(prediction)[0]:.2f}.")
st.write(f"The predicted revenue is ${prediction[0]:.2f}.")
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