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import streamlit as st
import pandas as pd
import numpy as np
import requests
st.write("Hello, Hugging Face!")
# # Streamlit UI for Super Kart Sales Prediction
# st.title("Super Kart Product Sales Prediction App")
# st.write("This tool predicts the total sales for a product based on store and product details.")
# st.subheader("Enter the product and store details:")
# # Collect user input (matching Super Kart features)
# 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", ["No Sugar", "Low Sugar", "Regular"])
# product_allocated_area = st.number_input("Product Allocated Area (sq ft)", min_value=0.0, value=500.0, step=1.0)
# product_type = st.selectbox("Product Type", ["Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables", "Snack Foods", "Household", "Frozen Foods", "Baking Goods", "Canned", "Health and Hygiene", "Hard Drinks", "Breads", "Starchy Foods", "Breakfast", "Seafood", "Others"])
# product_mrp = st.number_input("Product MRP (price)", 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", ["Small", "Medium", "High"])
# store_location_city_type = st.selectbox("Store Location City Type", ["Tier 3", "Tier 2", "Tier 1"])
# store_type = st.selectbox("Store Type", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"])
# # Predict button
# if st.button("Predict"):
# sample = {
# '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
# }
# features_df = pd.DataFrame([sample])
# features_df = pd.get_dummies(features_df, columns=['Product_Type', 'Store_Type'], drop_first=True)
# sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
# size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
# city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
# features_df['Product_Sugar_Content'] = features_df['Product_Sugar_Content'].map(sugar_mapping)
# features_df['Store_Size'] = features_df['Store_Size'].map(size_mapping)
# features_df['Store_Location_City_Type'] = features_df['Store_Location_City_Type'].map(city_mapping)
# backend_url = "https://Hugo014-TotalSalesPredictionBackend.hf.space/v1/sales"
# try:
# response = requests.post(backend_url, json=sample)
# if response.status_code == 200:
# result = response.json()
# predicted_sales = result['Predicted Sales Total (in dollars)']
# st.write(f"The predicted sales total for the product is ${predicted_sales:.2f}.")
# else:
# st.error(f"Backend error: {response.status_code} - {response.text}")
# except Exception as e:
# st.error(f"Error calling backend: {str(e)}")
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