app.py

import streamlit as st
import pandas as pd
import requests

# Streamlit UI for Product sales revenue Prediction
st.title("Product Sales Revenue Prediction App")
st.write("This tool predicts the revenue generated by sale of a particular product in particular store.")

st.subheader("Enter the product details:")

# Collect user input based on dataset columns
Product_Id = st.text_input("Product_Id", placeholder="Enter product id")
Product_Weight = st.number_input("Product Weight", min_value=1.0, value=12.2)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ['No Sugar', 'Low Sugar', 'Regular'])
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.001, max_value=1.0, value=0.056)
Product_Type = st.selectbox("Product Type", ['Household',
 'Frozen Foods',
 'Snack Foods',
 'Canned',
 'Baking Goods',
 'Breads',
 'Fruits and Vegetables',
 'Dairy',
 'Health and Hygiene',
 'Meat',
 'Hard Drinks',
 'Soft Drinks',
 'Starchy Foods',
 'Others',
 'Breakfast',
 'Seafood'])
Product_MRP = st.number_input("Product_MRP", min_value=1, value=147)
Store_Id = st.selectbox("Store_Id", ['OUT004', 'OUT001', 'OUT003', 'OUT002'])
Store_Establishment_Year = st.selectbox("Store_Establishment_Year", [2009, 1987, 1999, 1998])
Store_Size = st.selectbox("Store_Size", ['Medium', 'High', 'Small'])
Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ['Tier 2', 'Tier 1', 'Tier 3'])
Store_Type = st.selectbox("Store_Type", ['Supermarket Type2', 'Supermarket Type1', 'Departmental Store', 'Food Mart'])


# Convert user inputs to match model training 
input_data = {
    '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
}

# Make prediction when the "Predict" button is clicked
if st.button("Predict", type='primary'):
    response = requests.post("https://nbhoite9988-ProductSalesRevenuePredictionBackend.hf.space/v1/sales", json=input_data)    # enter user name and space name before running the cell
    if response.status_code == 200:
        result = response.json()
        sales_revenue_prediction = result["Prediction"]  # Extract only the value
        st.write(f"Based on the information provided, the product with ID {Product_Id} is likely to generate sales revenue: {sales_revenue_prediction}.")
    else:
        st.error("Error in API request")

# Batch Prediction
st.subheader("Batch Prediction")

file = st.file_uploader("Upload CSV file", type=["csv"])
if file is not None:
    if st.button("Predict for Batch", type='primary'):
        response = requests.post("https://nbhoite9988-ProductSalesRevenuePredictionBackend.hf.space/v1/salesbatch", files={"file": file})    # enter user name and space name before running the cell
        if response.status_code == 200:
            result = response.json()
            st.header("Batch Prediction Results")
            st.write(result)
        else:
            st.error("Error in API request")