app.py

import streamlit as st
import pandas as pd
import requests

# Hugging Face backend API endpoint
API_URL = "https://vallabbharath-Backend.hf.space/predict"

st.set_page_config(page_title="Sales Prediction App", page_icon="🛍️", layout="wide")

st.title("🛒 Sales Prediction Dashboard")

st.markdown("""
Upload or enter product and store details below to predict expected sales.
""")

# --- Input Section ---
st.subheader("Enter Input Data")

# You can also adjust these based on your backend model features
col1, col2 = st.columns(2)

with col1:
    product_weight = st.number_input("Product Weight", min_value=0.0, step=0.1)
    product_sugar = st.selectbox("Product Sugar Content", ['Low Sugar', 'No Sugar', 'Regular', 'reg'])
    product_type = st.selectbox("Product Type", ['Baking Goods','Breads','Breakfast','Canned','Dairy',
 'Frozen Foods','Fruits and Vegetables','Hard Drinks','Health and Hygiene',
 'Household','Meat','Others','Seafood','Snack Foods','Soft Drinks','Starchy Foods'])
    product_mrp = st.number_input("Product MRP", min_value=0.0, step=0.1)
    product_allocated_area = st.number_input("Product Allocated Area", min_value=0.001, step=0.001)

with col2:
    store_id = st.text_input("Store ID")
    store_est_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2010)
    store_size = st.selectbox("Store Size", ['High', 'Medium', 'Small'])
    store_loc = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 2', 'Tier 3'] )
    store_type = st.selectbox("Store Type", ['Departmental Store', 'Food Mart', 'Supermarket Type1', 'Supermarket Type2'])

if st.button("Predict"):
    # Prepare data for backend
    data = {
        "Product_Weight": product_weight,
        "Product_Sugar_Content": product_sugar,
        "Product_Allocated_Area": product_allocated_area,  # if needed, or remove if not used
        "Product_Type": product_type,
        "Product_MRP": product_mrp,
        "Store_Id": store_id,
        "Store_Establishment_Year": int(store_est_year),
        "Store_Size": store_size,
        "Store_Location_City_Type": store_loc,
        "Store_Type": store_type
    }

    try:
        response = requests.post(API_URL, json=data)
        if response.status_code == 200:
            result = response.json()
            st.success(f"🟢 Predicted Sales: **{result['prediction']:.2f}**")
        else:
            st.error(f"Backend Error: {response.text}")
    except Exception as e:
        st.error(f"Error connecting to backend: {e}")