app.py

import streamlit as st
import pandas as pd
import requests

# Streamlit UI for Price Prediction
st.title("SuperKart Revenue Prediction App")
st.write("This tool predicts the Revenue for SuperKart store based on the Product & Store details.")

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

# Collect user input
Product_Weight = st.number_input("Product Weight (in kg)", min_value=0.0, step=0.1, value=12.66) # Default from example
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular", "reg"])
Product_Allocated_Area = st.number_input("Product Allocated Area Ratio", min_value=0.001, step=0.01, value=0.027) # Default from example
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"])
Product_MRP = st.number_input("Product MRP (in dollars)", min_value=0.0, step=0.1, value=117.08) # Default from example
Store_Id = st.selectbox("Store Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
Store_Age = st.number_input("Store_Age", min_value=16, max_value=38, value=20)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])

# Convert user input 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_Id': Store_Id,
    'Store_Age': Store_Age,
    '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"):
    response = requests.post("https://ramanub-Superkartbe.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()
        st.success(f"Predicted Revenue in dollars is : {prediction}")
    else:
        st.error("Error making prediction.")