app.py

import os

# ✅ Force Streamlit to use /tmp/.streamlit at runtime
os.environ["STREAMLIT_CONFIG_DIR"] = "/tmp/.streamlit"
os.makedirs("/tmp/.streamlit", exist_ok=True)

# Optional: create config if missing
config_path = "/tmp/.streamlit/config.toml"
if not os.path.exists(config_path):
    with open(config_path, "w") as f:
        f.write("[server]\nheadless = true\nport = 7860\nenableCORS = false\n"
                "enableXsrfProtection = false\naddress = \"0.0.0.0\"")

import streamlit as st
import requests

st.title("🛒 SuperKart Sales Prediction")

st.subheader("Enter Product Details")

# Product inputs
product_id = st.text_input("Product ID (e.g., FD1234)")
product_weight = st.number_input("Product Weight", min_value=0.0, step=0.1)
product_sugar = st.selectbox("Sugar Content", ["low sugar", "regular", "no sugar"])
product_area = st.number_input("Allocated Display Area Ratio", min_value=0.0, 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, step=0.1)

# Store inputs
store_id = st.text_input("Store ID (e.g., STR001)")
store_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, step=1)
store_size = st.selectbox("Store Size", ["high", "medium", "low"])
store_city = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
store_age = 2025 - store_year

# Backend endpoint
# backend_url = "https://huggingface.co/spaces/keerthas/superkart-api/predict"
backend_url = "https://keerthas-superkart-api.hf.space/predict"


if st.button("Predict Sales"):
    input_data = {
        "Product_Id": product_id,
        "Product_Weight": product_weight,
        "Product_Sugar_Content": product_sugar,
        "Product_Allocated_Area": product_area,
        "Product_Type": product_type,
        "Product_MRP": product_mrp,
        "Store_Id": store_id,
        "Store_Establishment_Year": store_year,
        "Store_Size": store_size,
        "Store_Location_City_Type": store_city,
        "Store_Type": store_type,
        "Store_Age": store_age
    }

    try:
        response = requests.post(backend_url, json=input_data)
        if response.status_code == 200:
            result = response.json()
            if "prediction" in result:
                st.success(f"💰 Predicted Sales: {result['prediction']:.2f}")
            else:
                st.error(f"Backend Error: {result}")
        else:
            st.error(f"❌ Request Failed: {response.text}")
    except Exception as e:
        st.error(f"Error: {e}")