app.py

import requests
import streamlit as st
import pandas as pd

st.title("SuperKart Product Revenue Prediction")

# Section for online prediction
st.subheader("Online Prediction")

# Input fields for Product data
Product_Weight = st.number_input("Product's Weight (weight of the product)",  value=0.0, format="%.2f", min_value=0.0, max_value=50.0)
Product_Allocated_Area = st.number_input("Product's Allocated Area (Ratio of the allocated display area of the product to the total display area of the store in which the product is being sold)", value=0.0, format="%.3f", min_value=0.0, max_value=1.0)
Product_MRP = st.number_input("Product's MRP (in rupees)",  value=0.0, format="%.2f", min_value=0.0, max_value=300.0)

Product_Id = st.text_input("Product ID (This must have two letters at the beginning, followed by a number)")
# Building ProductIdCode using First two character code of ProductId
Product_Id_Code = str(Product_Id)[:2]
Product_Sugar_Content = st.selectbox("Product's Sugar Content (Sugar content of the product)", ["Low Sugar", "Regular", "No Sugar"])
Store_Id = st.selectbox("Store ID (Unique identifier of the store)", ["OUT001", "OUT002", "OUT003", "OUT004"])
Store_Establishment_Year = st.selectbox("Year in which the store was established", ["1987", "1998", "1999", "2009"])
Store_Size = st.selectbox("Size of the store", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("Type of the city in which the store is located", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Type of store the product is being sold", ["Food Mart", "Departmental Store", "Supermarket Type1", "Supermarket Type2"])

#Build a logic to make sure Product_Id_Code must be either of three strings - "FD","NC","DR"
allowed_codes = {"FD", "NC", "DR"}

if Product_Id_Code in allowed_codes:
    print("Valid Product ID Code")
else:
    print("Invalid Product ID Code : The first two characters of Product ID must be either of three strings - 'FD','NC','DR'")

product_data = {
    'Product_Weight': Product_Weight,
    'Product_Allocated_Area': Product_Allocated_Area,
    'Product_MRP': Product_MRP,
    'Product_Sugar_Content': Product_Sugar_Content,
    '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,
    'Product_Id_Code': Product_Id_Code
}

if st.button("Predict", type='primary'):
    response = requests.post("https://mvnmanojhirwani-StoreRevenuePredictionBackend.hf.space/v1/product", json=product_data)
    if response.status_code == 200:
        result = response.json()
        revenue_prediction = result["Predicted revenue (in rupees)"]  # Extract only the value
        st.write(f"Based on the information provided, the product with ID {Product_Id} is likely to generate a revenue of {revenue_prediction} rupees in {Store_Id} store.")
    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://mvnmanojhirwani-StoreRevenuePredictionBackend.hf.space/v1/productBatch", 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")