import streamlit as st
import pandas as pd
import requests

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

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

# Collect user input for property features
store_id = st.selectbox("Store Id", ["OUT001", "OUT002", "OUT003","OUT004"])
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_sugar_content = st.selectbox("Sugar Contents", ["Low Sugar","Regular","No Sugar","reg"])
product_mrp = st.number_input("MPR", min_value=1.0, max_value=500.0, step=1.0, value=90.0)
product_weight = st.number_input("Product Weight", min_value=1.0, max_value=30.0, step=1.0, value=5.0)
product_allocated_area = st.number_input("Allocated Display Area Ratio", min_value=0.001, max_value=0.3, step=0.001, value=0.10)


# Convert user input into a DataFrame
input_data = {
    'store_id': store_id,
    'product_type': product_type,
    'product_sugar_content': product_sugar_content,
    'product_mrp': product_mrp,
    'product_weight': product_weight,
    'product_allocated_area': product_allocated_area
}

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