import streamlit as st
import requests
import pandas as pd

# Backend API URL
BACKEND_URL = "https://sastrysagi-SuperKartBackEnd.hf.space"  # Replace with actual backend URL

st.title("SuperKart Sales Forecasting System")

# Single prediction form
st.header("Single Prediction")
with st.form("single_prediction_form"):
    st.subheader("Enter Product and Store Details")
    product_weight = st.number_input("Product Weight", min_value=0.0, value=10.0, step=0.1)
    product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
    product_allocated_area = st.number_input("Product Allocated Area (Ratio)", min_value=0.0, value=0.1, step=0.01)
    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", min_value=0.0, value=100.0, step=1.0)
    store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2000, step=1)
    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 Type1", "Supermarket Type2", "Food Mart"])

    submitted = st.form_submit_button("Predict Sales")
    if submitted:
        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_Establishment_Year": store_establishment_year,
            "Store_Size": store_size,
            "Store_Location_City_Type": store_location_city_type,
            "Store_Type": store_type
        }
        try:
            response = requests.post(f"{BACKEND_URL}/v1/sales", json=input_data)
            if response.status_code == 200:
                st.success(f"Predicted Sales: ${response.json()['Predicted_Sales']:.2f}")
            else:
                st.error(f"Prediction Error: {response.json().get('error', 'Unknown error')}")
        except Exception as e:
            st.error(f"Connection Error: {str(e)}")

# Batch prediction
st.header("Batch Prediction")
st.write("Upload a CSV file with columns matching the input features.")
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
if uploaded_file is not None:
    try:
        response = requests.post(f"{BACKEND_URL}/v1/salesbatch", files={"file": uploaded_file})
        if response.status_code == 200:
            st.subheader("Batch Prediction Results")
            st.json(response.json())
        else:
            st.error(f"Batch Prediction Error: {response.json().get('error', 'Unknown error')}")
    except Exception as e:
        st.error(f"Connection Error: {str(e)}")