import streamlit as st
import requests
import json
import pandas as pd
import os # Import os to access environment variables if needed later

# Define the URL of your deployed backend API on Hugging Face Spaces
# The URL format is typically https://<your-username>-<your-space-name>.hf.space
# Replace with your actual Space URL
# Assuming the backend space name is 'SuperKartBackendSC'
# You need to manually replace <your-username> with your actual Hugging Face username
HF_USERNAME = os.getenv("HF_USERNAME", "sumachakkingal") # Get username from environment variable or use placeholder
BACKEND_SPACE_NAME = "SuperKartBackendSC"
BACKEND_API_URL = f"https://{HF_USERNAME}-{BACKEND_SPACE_NAME}.hf.space/predict"


st.title("SuperKart Sales Prediction Frontend")

st.write("Enter the product and store details to get a sales prediction.")

# Create input fields for the features
# Ensure the keys match the column names expected by your Flask app
product_weight = st.number_input("Product Weight", min_value=0.0)
product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
product_allocated_area = st.number_input("Product Allocated Area", min_value=0.00)
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", min_value=0.0)
store_id = st.selectbox("Store ID", ['OUT004', 'OUT001', 'OUT003', 'OUT002'])
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'])
store_establishment_year = st.number_input("Store Establishment Year", min_value=1985, max_value=2024)

# Calculate Store_Age (assuming current year is 2024)
current_year = 2024
store_age = current_year - store_establishment_year


# Create a dictionary with the input data
# The keys must match the column names used for training and expected by the Flask app
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_Size": store_size,
    "Store_Location_City_Type": store_location_city_type,
    "Store_Type": store_type,
    "Store_Age": store_age # Include Store_Age as it's used by the model
}

# Create a button to trigger the prediction
if st.button("Predict Sales"):
    try:
        # Send the input data to the backend API
        response = requests.post(BACKEND_API_URL, json=input_data)

        # Check if the request was successful
        if response.status_code == 200:
            prediction_result = response.json()
            predicted_sales = prediction_result.get("prediction")
            if predicted_sales is not None:
                st.success(f"Predicted Product Store Sales Total: {predicted_sales:.2f}")
            else:
                st.error("Could not retrieve a valid prediction from the backend.")
            #st.success(f"Predicted Product Store Sales Total: {predicted_sales:.2f}")
        else:
            st.error(f"Error: Could not get prediction from the backend. Status code: {response.status_code}")
            st.error(f"Response: {response.text}")

    except requests.exceptions.RequestException as e:
        st.error(f"Error connecting to the backend API: {e}")
    except Exception as e:
        st.error(f"An unexpected error occurred: {e}")