app.py

import streamlit as st
import pandas as pd
import requests

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

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

# --- Categorical inputs ---
Product_Sugar_Content = st.selectbox(
    "Product Sugar Content",
    ['Low Sugar', 'Regular', 'No Sugar']
)

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

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', 'Departmental Store', 'Supermarket Type1', 'Food Mart']
)

Product_Category = st.selectbox(
    "Product Category",
    ['Food Items', 'Non-Consumables', 'Drinks']
)

# --- Numerical inputs ---
Product_Weight = st.number_input("Product Weight (kg)", min_value=1.0, step=0.1, format="%.2f")
Product_Allocated_Area = st.number_input("Allocated Area (sq ft)", min_value=0.01, step=0.01, format="%.3f")
Product_MRP = st.number_input("Product MRP", min_value=1.0, step=0.1, format="%.2f")
Store_Age = st.number_input("Store Age (years)", min_value=1, step=1)

# --- Collect inputs into a DataFrame ---
input_data = pd.DataFrame([{
    "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_Size": [Store_Size],
    "Store_Location_City_Type": [Store_Location_City_Type],
    "Store_Type": [Store_Type],
    "Store_Age": [Store_Age],
    "Product_Category": [Product_Category]
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    # Send data to Flask API
    response = requests.post("https://subratm62-SuperKartSalesBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])
    if response.status_code == 200:
        prediction = response.json()['Predicted Sales (in INR)']
        st.success(f"Predicted Sales Total (in INR): {prediction}")
    else:
        st.error("Error making prediction.")