app.py

import streamlit as st
import pandas as pd
import requests
import altair as alt

from predictor.batch_handler import get_predictions, get_single_prediction
from predictor.utils import reorder_columns, get_csv_download
from predictor.chart_plotter import plot_actual_vs_predicted
from predictor.error_summary_table import  show_error_summary
st.set_page_config(page_title="SuperKart Sales Predictor", layout="wide")
st.title("🛒 SuperKart Sales Predictor")
st.markdown("Use the tabs below to predict sales for a single product or upload a CSV file for batch prediction.")

# Create tabs
tab1, tab2 = st.tabs(["🔍 Single Prediction", "📄 Batch Prediction"])

# ----------------- Tab 1: Single Prediction -----------------
with tab1:
    col1, col2 = st.columns(2)

    with col1:
        with st.expander("📦 Product Details", expanded=True):
            product_weight = st.slider("Product Weight (kg)", 4.0, 22.0, 12.65, 0.1)
            product_allocated_area = st.slider("Allocated Shelf Area", 0.0, 0.3, 0.07, 0.01)
            product_mrp = st.slider("Product MRP", 31.0, 266.0, 147.0)
            product_sugar_content = st.radio("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"], horizontal=True)
            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"
            ])

    with col2:
        with st.expander("🏬 Store Details", expanded=True):
            store_id = st.radio("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"], horizontal=True)
            store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
            store_location = st.radio("City Tier", ["Tier 1", "Tier 2", "Tier 3"], horizontal=True)
            store_type = st.selectbox("Store Type", [
                "Supermarket Type1", "Supermarket Type2", "Departmental Store", "Grocery Store"
            ])
            est_year = st.slider("Establishment Year", 1987, 2009, 2002)

    # Submit button
    col1, col2 = st.columns([1, 3])  # Adjust ratio as needed

    with col1:
        predict_clicked = st.button("🎯 Predict Sales 🎯", key="predict_button")

    if predict_clicked:
        payload = {
            "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_Id": store_id,
            "Store_Size": store_size,
            "Store_Location_City_Type": store_location,
            "Store_Type": store_type,
            "Store_Establishment_Year": est_year
        }

        try:
            prediction = get_single_prediction(payload)
            pred_value = prediction[0] if isinstance(prediction, list) else prediction
            with col2:
                st.success(f"✅ Predicted Sales: ₹{pred_value:,.2f}")
                st.json({**payload, "Predicted_Sales": pred_value})
        except Exception as e:
            with col2:
                st.error(f"⚠️ Error during prediction: {e}")


# ----------------- Tab 2: Batch Prediction -----------------
with tab2:
    st.subheader("📄 Upload CSV for Batch Prediction")
    uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])

    if uploaded_file:
        try:
            df = pd.read_csv(uploaded_file)

            if df.empty:
                st.warning("Uploaded file is empty.")
            else:
                st.write("📋 Uploaded Data Preview:")
                st.dataframe(df.head())

                df = get_predictions(df)
                if "Product_Store_Sales_Total" in df.columns:
                    df = reorder_columns(df, ["Product_Store_Sales_Total", "Predicted_Sales"])
                else:
                    df = reorder_columns(df, ["Predicted_Sales"])

                col1, col2 = st.columns([6, 1])
                with col1:
                    st.subheader("📈 Prediction Results:")
                with col2:
                    st.download_button(
                        label="📥 Download CSV",
                        data=get_csv_download(df),
                        file_name="batch_predictions.csv",
                        mime="text/csv",
                        use_container_width=True
                    )

                st.dataframe(df)
                show_error_summary(df)
                plot_actual_vs_predicted(df)
                
        except Exception as e:
            st.error(f"⚠️ Error while processing the file: {e}")