app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("SuperKart Revenue Prediction")

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

# Input fields for product and store data
Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)

Product_Sugar_Content = st.selectbox(
    "Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"]
)

Product_Allocated_Area = st.number_input(
    "Product Allocated Area", min_value=0.00, max_value=0.30, value=0.15, step=0.05
)

Product_MRP = st.slider(
    "Product MRP", min_value=0, max_value=250, value=100, step=50
)

Store_Size = st.selectbox(
    "Store Size", ["High", "Medium", "Small"]
)

Store_Location_City_Type = st.selectbox(
    "Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"]
)

Store_Type = st.selectbox(
    "Store Type", ["Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"]
)

Product_Id_char = st.text_input(
    "Product ID", value="FD306"
)

Store_Age_Years = st.slider(
    "Store Age (in years)", min_value=0, max_value=30, value=10, step=1
)

Product_Type_Category = st.selectbox(
    "Product Type Category",
    [
        "Baking Goods", "Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods",
        "Fruits and Vegetables", "Hard Drinks", "Health and Hygiene", "Household",
        "Meat", "Others", "Seafood", "Snack Foods", "Soft Drinks", "Starchy Foods"
    ]
)

product_data = {
    "Product_Weight": Product_Weight,
    "Product_Sugar_Content": Product_Sugar_Content,
    "Product_Allocated_Area": Product_Allocated_Area,
    "Product_MRP": Product_MRP,
    "Store_Size": Store_Size,
    "Store_Location_City_Type": Store_Location_City_Type,
    "Store_Type": Store_Type,
    "Product_Id_char": Product_Id_char,
    "Store_Age_Years": Store_Age_Years,
    "Product_Type_Category": Product_Type_Category
}

if st.button("Predict", type='primary'):
    response = requests.post("https://VeerendraManikonda-SuperKartPredictionBackend.hf.space/v1/revenue", json=product_data)
    if response.status_code == 200:
        result = response.json()
        predicted_sales = result["Product_Store_Sales_Total"]
        st.write(f"Predicted Product Store Sales Total: ₹{predicted_sales:.2f}")
    else:
        st.error("Error in API request")