app.py

from datetime import datetime
import streamlit as st
import pandas as pd
import requests

# -------------------------
# SuperKart Streamlit UI
# -------------------------
st.title("SuperKart Sales Revenue Predictor")

st.markdown("""
Use this app to predict the **expected sales revenue** for a given product
based on its characteristics and store details.
""")

# -------------------------
# Online Prediction Section
# -------------------------
st.subheader("Single Product Prediction")

# --- Collect user inputs ---

Product_Weight = st.number_input("Product Weight (in kg)", min_value=0.0, step=0.1)
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, step=0.1)
Product_MRP = st.number_input("Product MRP (Maximum Retail Price)", min_value=0.0, step=0.5)

Product_Sugar_Content = st.selectbox(
    "Product Sugar Content",
    ["Low Sugar", "Regular", "No Sugar", "reg"]
)

Product_Type = st.selectbox(
    "Product Type",
    [
        "Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household",
        "Baking Goods", "Canned", "Health and Hygiene", "Meat", "Soft Drinks",
        "Breads", "Hard Drinks", "Others", "Starchy Foods", "Breakfast", "Seafood"
    ]
)

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

Store_Location_City_Type = st.selectbox(
    "Store Location City Type",
    ["Tier 1", "Tier 2", "Tier 3"]
)

Store_Type = st.selectbox(
    "Store Type",
    ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"]
)

Store_Establishment_Year = st.number_input(
    "Store Establishment Year", min_value=1980, max_value=datetime.now().year, value=2010
    )

# --- Prepare input data ---
input_data = pd.DataFrame([{
    "Product_Weight": Product_Weight,
    "Product_Allocated_Area": Product_Allocated_Area,
    "Product_MRP": Product_MRP,
    "Product_Sugar_Content": Product_Sugar_Content,
    "Product_Type": Product_Type,
    "Store_Establishment_Year": Store_Establishment_Year,
    "Store_Size": Store_Size,
    "Store_Location_City_Type": Store_Location_City_Type,
    "Store_Type": Store_Type
}])

if st.button("Predict Sales Revenue"):
    with st.spinner("Predicting..."):
        response = requests.post("https://Dtapkir-SuperkartSalesPredictionBackend.hf.space/v1/predict", 
                                 json=input_data.to_dict(orient="records")[0])

        # --- Check for success ---
        if response.status_code == 200:
            result = response.json()
            
            # Debug: display entire response
            st.write("API Response:", result)

            # Extract prediction safely
            prediction = result.get("Predicted_Product_Store_Sales_Total", None)

            if prediction is not None:
                st.success(f"Predicted Sales Revenue (in dollars): **${prediction}**")
            else:
                st.warning("Prediction key not found in API response.")
        else:
            st.error(f"Error making prediction. Status code: {response.status_code}")
            st.write("Response text:", response.text)