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")

# Collect user input for property features
Product_Weight = st.number_input("Product Weight (in kg)", min_value=0.0, step=0.1, value=1.0)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Product Allocated Area (in sq. feet)", min_value=0.0, step=0.1, value=1.0)
Product_Type = st.selectbox("Product Type", ["Meat", "Snack Foods", "Hard Drinks", "Dairy"
, "Canned", "Soft Drinks", "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods", 
                                             "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods"
                                             , "Others"])
Product_MRP = st.number_input("Product MRP (in dollars)", min_value=0.0, step=0.1, value=1.0)
Store_Id = st.selectbox("Store Id", ["OUT001","OUT002","OUT003","OCT004"])
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1987, step=1, value=1987)
Store_Size = st.selectbox("Store Size", ["High", "Medium", "Low"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])

# Convert user input into a DataFrame
input_data = {
    '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_Establishment_Year': Store_Establishment_Year,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type
    }

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