app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("Product Store Sales Prediction")
st.write("Enter product and store details to predict total sales.")

# Section for online prediction
st.subheader("Product Details")

# Collect user input for property features
Product_Weight = st.number_input(
    "Product Weight",
    min_value=0.0,
    step=0.1,
    value=10.0
)

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,
    max_value=1.0,
    step=0.01,
    value=0.10
)

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,
    step=1.0,
    value=100.0
)

# -----------------------------
# Store Inputs
# -----------------------------
st.subheader("🏬 Store Details")

Store_Establishment_Year = st.number_input(
    "Store Establishment Year",
    min_value=1950,
    max_value=2025,
    step=1,
    value=2005
)

Store_Size = st.selectbox(
    "Store Size",
    ["Low", "Medium", "High"]
)

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"
    ]
)

# -----------------------------
# Prediction
# -----------------------------
if st.button("🔮 Predict Sales"):
    payload = {
        "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(
            "https://chaitram-salespredictionbackend.hf.space/v1/sales",
            json=payload,
            timeout=10
        )

        if response.status_code == 200:
            prediction = response.json()["predicted_sales"]
            st.success(f"💰 Predicted Product Store Sales: **{prediction:,.2f}**")
        else:
            st.error("❌ Prediction failed. Please check API logs.")

    except Exception as e:
        st.error(f"⚠️ API connection error: {e}")