app.py

import os
from functools import lru_cache

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

st.set_page_config(
    page_title="FreshWise - Perishable Retail Optimization",
    page_icon="🥗",
    layout="wide",
    initial_sidebar_state="expanded",
)

DATA_CANDIDATES = [
    os.environ.get("DATA_PATH", ""),
    "perishable_goods_management.csv",
    "/app/perishable_goods_management.csv",
    "/data/perishable_goods_management.csv",
    "/mnt/data/perishable_goods_management.csv",
]

CATEGORY_COLORS = {
    "Produce": "#2E8B57",
    "Dairy": "#1E90FF",
    "Meat": "#B22222",
    "Seafood": "#20B2AA",
    "Bakery": "#D2691E",
    "Ready_to_Eat": "#8A2BE2",
}

FOCUS_CATEGORY = "Bakery"


def find_data_path() -> str:
    for path in DATA_CANDIDATES:
        if path and os.path.exists(path):
            return path
    raise FileNotFoundError(
        "perishable_goods_management.csv not found. Put it next to app.py or set DATA_PATH."
    )


@st.cache_data(show_spinner=False)
def load_data() -> pd.DataFrame:
    path = find_data_path()
    df = pd.read_csv(path)

    df["transaction_date"] = pd.to_datetime(df["transaction_date"], errors="coerce")
    df["expiration_date"] = pd.to_datetime(df["expiration_date"], errors="coerce")

    df["sell_through_pct"] = np.where(
        df["initial_quantity"] > 0, df["units_sold"] / df["initial_quantity"], 0
    )
    df["stock_demand_ratio"] = np.where(
        df["daily_demand"] > 0, df["initial_quantity"] / df["daily_demand"], np.nan
    )
    df["gross_margin"] = df["selling_price"] - df["cost_price"]
    df["leftover_units"] = (df["initial_quantity"] - df["units_sold"]).clip(lower=0)
    df["stockout_flag"] = (df["daily_demand"] > df["initial_quantity"]).astype(int)
    df["lost_sales_units"] = (df["daily_demand"] - df["units_sold"]).clip(lower=0)
    df["value_score"] = (
        (1 - df["waste_pct"].clip(0, 1)) * 0.35
        + df["profit_margin_pct"].clip(lower=0) / 100 * 0.25
        + (1 - df["days_until_expiry"].clip(upper=14) / 14) * 0.15
        + df["discount_pct"].clip(0, 0.5) * 0.25
    )
    df["expiry_bucket"] = pd.cut(
        df["days_until_expiry"],
        bins=[-1, 1, 3, 7, 30, 10_000],
        labels=["<=1d", "2-3d", "4-7d", "8-30d", ">30d"],
    )
    df["high_waste_flag"] = (df["waste_pct"] >= df["waste_pct"].quantile(0.75)).astype(int)
    return df


@st.cache_data(show_spinner=False)
def fit_segments(df: pd.DataFrame) -> pd.DataFrame:
    work = df[[
        "daily_demand",
        "initial_quantity",
        "waste_pct",
        "shelf_life_days",
        "stock_demand_ratio",
        "sell_through_pct",
    ]].replace([np.inf, -np.inf], np.nan).dropna().copy()

    sample_size = min(len(work), 20000)
    work = work.sample(sample_size, random_state=42)
    scaler = StandardScaler()
    X = scaler.fit_transform(work)
    km = KMeans(n_clusters=4, random_state=42, n_init=10)
    work["cluster"] = km.fit_predict(X)
    return work


@st.cache_resource(show_spinner=False)
def fit_risk_model(df: pd.DataFrame):
    features = [
        "daily_demand",
        "initial_quantity",
        "shelf_life_days",
        "days_until_expiry",
        "temp_deviation",
        "temp_abuse_events",
        "handling_score",
        "packaging_score",
        "spoilage_risk",
        "discount_pct",
        "markdown_applied",
        "is_weekend",
        "supplier_score",
    ]
    X = df[features]
    y = df["high_waste_flag"]
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42, stratify=y
    )
    model = RandomForestClassifier(
        n_estimators=120, random_state=42, n_jobs=-1, max_depth=10
    )
    model.fit(X_train, y_train)
    importances = pd.Series(model.feature_importances_, index=features).sort_values(ascending=False)
    return model, importances


@lru_cache(maxsize=1)
def cluster_name_map():
    return {
        0: "Stable performers",
        1: "Overstocked slow movers",
        2: "Short-life high risk",
        3: "High demand fast movers",
    }


def apply_filters(df: pd.DataFrame):
    st.sidebar.header("Filters")

    if "filter_regions" not in st.session_state:
        st.session_state["filter_regions"] = []
    if "filter_stores" not in st.session_state:
        st.session_state["filter_stores"] = []

    all_regions = sorted(df["region"].dropna().unique())
    all_stores = sorted(df["store_id"].dropna().unique())

    # If the user selected stores directly, infer the matching region(s).
    if st.session_state["filter_stores"] and not st.session_state["filter_regions"]:
        inferred_regions = sorted(
            df.loc[df["store_id"].isin(st.session_state["filter_stores"]), "region"]
            .dropna()
            .unique()
        )
        st.session_state["filter_regions"] = inferred_regions

    # Region selection drives store options.
    regions = st.sidebar.multiselect(
        "Region",
        all_regions,
        key="filter_regions",
    )

    available_stores = sorted(
        df.loc[df["region"].isin(regions), "store_id"].dropna().unique()
    ) if regions else all_stores

    # Keep only stores that still belong to the selected region(s).
    st.session_state["filter_stores"] = [
        s for s in st.session_state["filter_stores"] if s in available_stores
    ]

    stores = st.sidebar.multiselect(
        "Store",
        available_stores,
        key="filter_stores",
    )

    # If stores are selected, make region selection follow them exactly.
    if stores:
        inferred_regions = sorted(
            df.loc[df["store_id"].isin(stores), "region"].dropna().unique()
        )
        if inferred_regions != regions:
            st.session_state["filter_regions"] = inferred_regions
            regions = inferred_regions

    categories = st.sidebar.multiselect("Category", sorted(df["category"].dropna().unique()), default=[])
    expiry_range = st.sidebar.slider("Days until expiry", 0, int(df["days_until_expiry"].max()), (0, 30))
    weekend_choice = st.sidebar.selectbox("Day type", ["All", "Weekday", "Weekend"])

    filtered = df.copy()
    if regions:
        filtered = filtered[filtered["region"].isin(regions)]
    if stores:
        filtered = filtered[filtered["store_id"].isin(stores)]
    if categories:
        filtered = filtered[filtered["category"].isin(categories)]
    filtered = filtered[
        (filtered["days_until_expiry"] >= expiry_range[0])
        & (filtered["days_until_expiry"] <= expiry_range[1])
    ]
    if weekend_choice == "Weekday":
        filtered = filtered[filtered["is_weekend"] == 0]
    elif weekend_choice == "Weekend":
        filtered = filtered[filtered["is_weekend"] == 1]
    return filtered


def metric_row(df: pd.DataFrame):
    c1, c2, c3, c4, c5 = st.columns(5)
    c1.metric("Waste %", f"{df['waste_pct'].mean():.1%}")
    c2.metric("Profit", f"€{df['profit'].mean():.2f}")
    c3.metric("Sell-through", f"{df['sell_through_pct'].mean():.1%}")
    c4.metric("Units wasted", f"{df['units_wasted'].mean():.1f}")
    c5.metric("Markdown rate", f"{df['markdown_applied'].mean():.1%}")


def manager_dashboard(df: pd.DataFrame):
    st.subheader("Manager Mode")
    metric_row(df)

    a, b = st.columns([1.2, 1])
    with a:
        trend = df.groupby(df["transaction_date"].dt.to_period("M").astype(str))[["waste_pct", "profit"]].mean().reset_index()
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=trend["transaction_date"], y=trend["waste_pct"], name="Waste %", mode="lines+markers"))
        fig.add_trace(go.Scatter(x=trend["transaction_date"], y=trend["profit"], name="Profit", mode="lines+markers", yaxis="y2"))
        fig.update_layout(
            title="Monthly Waste and Profit Trend",
            yaxis=dict(title="Waste %"),
            yaxis2=dict(title="Profit", overlaying="y", side="right"),
            legend=dict(orientation="h"),
            margin=dict(l=10, r=10, t=40, b=10),
        )
        st.plotly_chart(fig, use_container_width=True)
    with b:
        top_risk = (
            df.groupby("category")[["waste_pct", "profit", "stock_demand_ratio"]]
            .mean()
            .sort_values("waste_pct", ascending=False)
            .head(8)
            .reset_index()
        )
        fig = px.bar(top_risk, x="waste_pct", y="category", orientation="h", title="High Waste Categories")
        st.plotly_chart(fig, use_container_width=True)

    c1, c2 = st.columns(2)
    with c1:
        store_risk = (
            df.groupby("store_id")[["waste_pct", "profit", "temp_deviation"]]
            .mean()
            .sort_values(["waste_pct", "temp_deviation"], ascending=[False, False])
            .head(15)
            .reset_index()
        )
        st.dataframe(store_risk, use_container_width=True, hide_index=True)
    with c2:
        expiry = df.groupby("expiry_bucket")[["waste_pct", "profit", "discount_pct"]].mean().reset_index()
        fig = px.line(expiry, x="expiry_bucket", y=["waste_pct", "profit", "discount_pct"], markers=True, title="Expiry Stage Performance")
        st.plotly_chart(fig, use_container_width=True)



def forecast_region_demand(cat_df: pd.DataFrame, region: str) -> pd.DataFrame:
    d = cat_df[cat_df["region"] == region].copy()
    if d.empty:
        return pd.DataFrame()
    ts = d.groupby("transaction_date")["daily_demand"].mean().reset_index().sort_values("transaction_date")
    if len(ts) < 14:
        return pd.DataFrame()
    recent = ts.tail(56).copy()
    weekday_avg = recent.groupby(recent["transaction_date"].dt.dayofweek)["daily_demand"].mean().to_dict()
    last_date = ts["transaction_date"].max()
    future_dates = pd.date_range(last_date + pd.Timedelta(days=1), periods=14, freq="D")
    future = pd.DataFrame({
        "transaction_date": future_dates,
        "daily_demand": [weekday_avg.get(d.dayofweek, ts["daily_demand"].tail(14).mean()) for d in future_dates],
        "series": "Forecast"
    })
    hist = ts.tail(60).copy()
    hist["series"] = "Actual"
    return pd.concat([hist, future], ignore_index=True)


def manager_category_intelligence(df: pd.DataFrame):
    st.subheader("Category Intelligence")
    categories = sorted(df["category"].dropna().unique())
    default_idx = categories.index(FOCUS_CATEGORY) if FOCUS_CATEGORY in categories else 0
    focus = st.selectbox("Focus category", categories, index=default_idx)
    cat_df = df[df["category"] == focus].copy()

    if cat_df.empty:
        st.warning("No data for the selected category.")
        return

    st.markdown(
        f"Selected category: **{focus}**. This page compares regional operations, inventory, profitability, demand, stockout and waste trade-offs for a distinctive perishable category."
    )

    c1, c2, c3, c4 = st.columns(4)
    c1.metric("Avg demand", f"{cat_df['daily_demand'].mean():.1f}")
    c2.metric("Avg stock", f"{cat_df['initial_quantity'].mean():.1f}")
    c3.metric("Stockout rate", f"{cat_df['stockout_flag'].mean():.1%}")
    c4.metric("Waste rate", f"{cat_df['waste_pct'].mean():.1%}")

    region_summary = (
        cat_df.groupby("region")
        .agg(
            avg_demand=("daily_demand", "mean"),
            avg_stock=("initial_quantity", "mean"),
            avg_profit=("profit", "mean"),
            avg_margin=("profit_margin_pct", "mean"),
            waste_pct=("waste_pct", "mean"),
            units_wasted=("units_wasted", "mean"),
            markdown_rate=("markdown_applied", "mean"),
            promo_rate=("is_promoted", "mean"),
            temp_dev=("temp_deviation", "mean"),
            shelf_life=("shelf_life_days", "mean"),
            days_until_expiry=("days_until_expiry", "mean"),
            stockout_rate=("stockout_flag", "mean"),
            lost_sales=("lost_sales_units", "mean"),
        )
        .reset_index()
    )

    a, b = st.columns([1.2, 1])
    with a:
        melt = region_summary.melt(
            id_vars="region",
            value_vars=["avg_demand", "avg_stock", "avg_profit"],
            var_name="metric",
            value_name="value",
        )
        fig = px.bar(
            melt, x="region", y="value", color="metric", barmode="group",
            title=f"{focus}: regional operations, inventory and profit comparison",
        )
        st.plotly_chart(fig, use_container_width=True)
    with b:
        fig = px.scatter(
            region_summary, x="stockout_rate", y="waste_pct", size="avg_profit", color="region",
            hover_data=["avg_demand", "avg_stock", "markdown_rate", "promo_rate", "lost_sales"],
            title=f"{focus}: stockout vs waste trade-off by region",
        )
        st.plotly_chart(fig, use_container_width=True)

    c1, c2 = st.columns([1, 1.2])
    with c1:
        st.dataframe(region_summary.sort_values("avg_profit", ascending=False), use_container_width=True, hide_index=True)
    with c2:
        region_choice = st.selectbox("Forecast region", sorted(cat_df["region"].dropna().unique()))
        forecast_df = forecast_region_demand(cat_df, region_choice)
        if not forecast_df.empty:
            fig = px.line(
                forecast_df, x="transaction_date", y="daily_demand", color="series",
                title=f"{focus}: 60-day actual + 14-day demand forecast for {region_choice}",
            )
            st.plotly_chart(fig, use_container_width=True)

    st.markdown("### Regional recommendations")
    mean_stockout = region_summary["stockout_rate"].mean()
    mean_waste = region_summary["waste_pct"].mean()
    mean_margin = region_summary["avg_margin"].mean()
    mean_temp = region_summary["temp_dev"].mean()
    for _, r in region_summary.iterrows():
        advice = []
        if r["stockout_rate"] > mean_stockout:
            advice.append("raise replenishment and morning safety stock")
        if r["waste_pct"] > mean_waste:
            advice.append("start markdown earlier")
        if r["avg_margin"] < mean_margin:
            advice.append("use bundles instead of deeper discounts")
        if r["temp_dev"] > mean_temp:
            advice.append("tighten storage handling")
        if not advice:
            advice.append("maintain and scale current playbook")
        st.markdown(f"- **{r['region']}**: " + "; ".join(advice) + ".")

    st.markdown("### Marketing design simulator")
    m1, m2, m3, m4 = st.columns(4)
    promo_region = m1.selectbox("Target region", sorted(cat_df["region"].dropna().unique()), key="cat_region")
    promo_type = m2.selectbox("Promo type", ["Early markdown", "Breakfast bundle", "Happy-hour discount", "Loyalty coupon"])
    discount = m3.slider("Discount %", 0, 40, 15, key="cat_discount")
    duration = m4.slider("Duration (days)", 1, 10, 4, key="cat_duration")

    base = cat_df[cat_df["region"] == promo_region].copy()
    base_sales = base["units_sold"].mean()
    base_waste = base["waste_pct"].mean()
    base_profit = base["profit"].mean()
    promo_factor = {"Early markdown": 0.12, "Breakfast bundle": 0.16, "Happy-hour discount": 0.10, "Loyalty coupon": 0.08}[promo_type]
    sales_lift = promo_factor + discount / 180 + min(duration / 60, 0.10)
    waste_drop = min(0.42, promo_factor + discount / 200)
    margin_drag = discount / 160
    if promo_type == "Breakfast bundle":
        margin_drag *= 0.75

    est_sales = base_sales * (1 + sales_lift)
    est_waste = max(base_waste * (1 - waste_drop), 0)
    est_profit = base_profit * (1 + sales_lift - margin_drag)

    x1, x2, x3 = st.columns(3)
    x1.metric("Estimated avg units sold", f"{est_sales:.2f}", delta=f"+{(est_sales-base_sales):.2f}")
    x2.metric("Estimated waste", f"{est_waste:.1%}", delta=f"-{(base_waste-est_waste):.1%}")
    x3.metric("Estimated avg profit", f"€{est_profit:.2f}", delta=f"€{(est_profit-base_profit):.2f}")

def manager_inventory(df: pd.DataFrame):
    st.subheader("Inventory & Replenishment")

    overstock = df.copy()
    overstock["recommended_order_qty"] = (
        1.2 * overstock["daily_demand"] * (1 + overstock["demand_variability"])
        - overstock["leftover_units"]
    )
    overstock.loc[overstock["shelf_life_days"] <= 7, "recommended_order_qty"] *= 0.7
    overstock.loc[overstock["spoilage_risk"] >= overstock["spoilage_risk"].quantile(0.75), "recommended_order_qty"] *= 0.8
    overstock["recommended_order_qty"] = overstock["recommended_order_qty"].clip(lower=0).round()

    c1, c2 = st.columns([1.3, 1])
    with c1:
        category_summary = overstock.groupby("category")[["initial_quantity", "recommended_order_qty", "waste_pct", "profit"]].mean().reset_index()
        category_summary["order_reduction_pct"] = 1 - category_summary["recommended_order_qty"] / category_summary["initial_quantity"]
        fig = px.bar(
            category_summary.sort_values("order_reduction_pct", ascending=False),
            x="order_reduction_pct",
            y="category",
            orientation="h",
            title="Recommended Order Reduction by Category",
        )
        st.plotly_chart(fig, use_container_width=True)
    with c2:
        st.markdown("**Action shortlist**")
        shortlist = overstock.sort_values(["waste_pct", "stock_demand_ratio"], ascending=[False, False])[[
            "store_id", "product_name", "category", "initial_quantity", "daily_demand",
            "days_until_expiry", "waste_pct", "recommended_order_qty"
        ]].head(20)
        st.dataframe(shortlist, use_container_width=True, hide_index=True)

    st.markdown("### What-if Simulator")
    col1, col2, col3 = st.columns(3)
    selected_category = col1.selectbox("Category for simulation", sorted(df["category"].unique()))
    order_cut = col2.slider("Reduce order quantity by %", 0, 40, 10)
    markdown_shift = col3.slider("Advance markdown trigger by days", 0, 5, 2)

    sim = df[df["category"] == selected_category].copy()
    current_waste = sim["waste_pct"].mean()
    current_profit = sim["profit"].mean()

    waste_reduction = 0.35 * (order_cut / 100) + 0.015 * markdown_shift
    sim_waste = max(current_waste * (1 - waste_reduction), 0)
    sim_profit = current_profit * (1 + 0.08 * (order_cut / 100) + 0.03 * markdown_shift)

    s1, s2, s3 = st.columns(3)
    s1.metric("Current waste", f"{current_waste:.1%}")
    s2.metric("Simulated waste", f"{sim_waste:.1%}", delta=f"-{(current_waste-sim_waste):.1%}")
    s3.metric("Simulated avg profit", f"€{sim_profit:.2f}", delta=f"€{(sim_profit-current_profit):.2f}")


def manager_promotions(df: pd.DataFrame):
    st.subheader("Promotion Designer")
    left, right = st.columns([1, 1.2])
    with left:
        promo_category = st.selectbox("Promotion category", sorted(df["category"].unique()), key="promo_cat")
        expiry_target = st.selectbox("Target expiry bucket", ["<=1d", "2-3d", "4-7d", "8-30d", ">30d"])
        discount = st.slider("Discount %", 0, 50, 18)
        bundle = st.checkbox("Bundle with complementary items", value=True)
        weekend_only = st.checkbox("Weekend campaign only", value=False)

        sub = df[(df["category"] == promo_category) & (df["expiry_bucket"].astype(str) == expiry_target)].copy()
        if weekend_only:
            sub = sub[sub["is_weekend"] == 1]

        demand_lift = 0.08 + discount / 200
        if bundle:
            demand_lift += 0.06

        est_sales_uplift = sub["units_sold"].mean() * demand_lift if len(sub) else 0
        est_waste_drop = sub["waste_pct"].mean() * min(0.35, demand_lift) if len(sub) else 0
        est_profit = sub["profit"].mean() * (1 + demand_lift - discount / 150) if len(sub) else 0

        st.metric("Estimated sales uplift", f"{est_sales_uplift:.2f} units")
        st.metric("Estimated waste reduction", f"{est_waste_drop:.1%}")
        st.metric("Estimated avg profit", f"€{est_profit:.2f}")

    with right:
        promo_base = df.groupby(["expiry_bucket"])[["discount_pct", "waste_pct", "profit"]].mean().reset_index()
        fig = px.bar(promo_base, x="expiry_bucket", y=["discount_pct", "waste_pct"], barmode="group", title="Current Discount vs Waste by Expiry")
        st.plotly_chart(fig, use_container_width=True)

        st.markdown("**Recommended promotion copy**")
        st.info(
            f"Run a {discount}% {promo_category} campaign for {expiry_target} items"
            + (" on weekends" if weekend_only else "")
            + (" with bundle offers" if bundle else " as single-item markdown")
            + ". Position the offer at high-traffic display zones and highlight value + freshness."
        )


def manager_risk(df: pd.DataFrame):
    st.subheader("Risk & Store Operations")
    _, importances = fit_risk_model(df)
    c1, c2 = st.columns([1.1, 1])
    with c1:
        fig = px.bar(importances.head(10).sort_values(), orientation="h", title="Top Drivers of High Waste Risk")
        st.plotly_chart(fig, use_container_width=True)
    with c2:
        heat = df.groupby(["region", "category"])["temp_deviation"].mean().reset_index()
        fig = px.density_heatmap(heat, x="category", y="region", z="temp_deviation", title="Temperature Deviation Heatmap")
        st.plotly_chart(fig, use_container_width=True)

    alerts = (
        df.groupby("store_id")[["temp_deviation", "temp_abuse_events", "waste_pct", "profit"]]
        .mean()
        .assign(alert_score=lambda x: 0.35 * x["temp_deviation"] + 0.25 * x["temp_abuse_events"] + 0.4 * x["waste_pct"] * 10)
        .sort_values("alert_score", ascending=False)
        .head(15)
        .reset_index()
    )
    st.markdown("### Automated store alerts")
    st.dataframe(alerts, use_container_width=True, hide_index=True)


def consumer_deals(df: pd.DataFrame):
    st.subheader("Consumer Mode")
    c1, c2, c3 = st.columns(3)
    max_budget = c1.slider("Budget (€)", 5, 60, 20)
    preferred_category = c2.selectbox("Preferred category", ["All"] + sorted(df["category"].unique()))
    max_expiry = c3.slider("Maximum days until expiry", 1, 14, 5)

    deals = df[df["days_until_expiry"] <= max_expiry].copy()
    if preferred_category != "All":
        deals = deals[deals["category"] == preferred_category]
    deals = deals.assign(
        savings=lambda x: x["base_price"] - x["selling_price"],
        deal_score=lambda x: x["discount_pct"] * 0.5 + x["value_score"] * 0.35 + (x["profit_margin_pct"].clip(lower=0) / 100) * 0.15,
    ).sort_values(["deal_score", "savings"], ascending=False)

    display = deals[[
        "product_name", "category", "store_id", "days_until_expiry",
        "base_price", "selling_price", "discount_pct", "savings"
    ]].head(25)
    st.dataframe(display, use_container_width=True, hide_index=True)

    fig = px.scatter(
        deals.head(500), x="selling_price", y="discount_pct", color="category",
        hover_data=["product_name", "store_id", "days_until_expiry"],
        title="Discounted Items Map"
    )
    st.plotly_chart(fig, use_container_width=True)

    affordable = deals[deals["selling_price"] <= max_budget].head(10)
    if not affordable.empty:
        st.markdown("### Best picks for your budget")
        for _, row in affordable.iterrows():
            st.success(
                f"Now €{row['selling_price']:.2f} (save €{row['base_price'] - row['selling_price']:.2f}) · expires in {int(row['days_until_expiry'])} day(s)"
            )
            st.markdown(
                f"""
                🛒 **{row['product_name']}**  
                📦 Category: {row['category']}  
                🏪 Store: {row['store_id']}  
                💸 Discount: {row['discount_pct']*100:.0f}%  
                ⏳ Expiry: {row['days_until_expiry']} days
                """
            )


def build_bundle(df: pd.DataFrame, budget: float, people: int, theme: str):
    work = df.copy()
    work = work[work["days_until_expiry"] <= 7].copy()
    work["score"] = work["value_score"] + work["discount_pct"]

    theme_map = {
        "Quick dinner": ["Ready_to_Eat", "Produce", "Bakery", "Dairy"],
        "Healthy protein": ["Meat", "Seafood", "Dairy", "Produce"],
        "Family breakfast": ["Bakery", "Dairy", "Beverages", "Produce"],
        "Budget saver": list(work["category"].unique()),
    }
    cats = theme_map.get(theme, list(work["category"].unique()))
    work = work[work["category"].isin(cats)].sort_values(["score", "selling_price"], ascending=[False, True])

    chosen = []
    remaining = budget
    target_items = min(max(people + 1, 3), 6)
    used_categories = set()

    for _, row in work.iterrows():
        if row["selling_price"] <= remaining:
            if theme != "Budget saver" and row["category"] in used_categories:
                continue
            chosen.append(row)
            remaining -= row["selling_price"]
            used_categories.add(row["category"])
            if len(chosen) >= target_items:
                break

    if not chosen:
        return pd.DataFrame(), 0.0, 0.0
    bundle = pd.DataFrame(chosen)
    total = bundle["selling_price"].sum()
    saved = (bundle["base_price"] - bundle["selling_price"]).sum()
    return bundle, total, saved


def consumer_bundles(df: pd.DataFrame):
    st.subheader("Bundle Builder")
    c1, c2, c3 = st.columns(3)
    budget = c1.slider("Bundle budget (€)", 8, 80, 25)
    people = c2.slider("People", 1, 6, 2)
    theme = c3.selectbox("Bundle theme", ["Quick dinner", "Healthy protein", "Family breakfast", "Budget saver"])

    bundle, total, saved = build_bundle(df, budget, people, theme)
    if bundle.empty:
        st.warning("No bundle found for the current filters.")
        return

    k1, k2, k3 = st.columns(3)
    k1.metric("Bundle total", f"€{total:.2f}")
    k2.metric("You save", f"€{saved:.2f}")
    k3.metric("Items", f"{len(bundle)}")

    st.dataframe(bundle[[
        "product_name", "category", "store_id", "selling_price", "base_price", "discount_pct", "days_until_expiry"
    ]], use_container_width=True, hide_index=True)

    st.info(
        "Suggested marketing use: turn these bundles into one-click promotions for end customers or pre-designed campaign packs for store managers."
    )


def consumer_personal(df: pd.DataFrame):
    st.subheader("Personalized Promotions")
    favorite = st.selectbox("Favorite category", sorted(df["category"].unique()))
    price_cap = st.slider("Max item price (€)", 1, 30, 10)
    not_too_close = st.checkbox("Hide items expiring within 1 day", value=False)

    recs = df[df["category"] == favorite].copy()
    recs = recs[recs["selling_price"] <= price_cap]
    if not_too_close:
        recs = recs[recs["days_until_expiry"] > 1]
    recs = recs.assign(score=lambda x: x["discount_pct"] * 0.55 + x["value_score"] * 0.45).sort_values("score", ascending=False).head(12)

    cols = st.columns(3)
    for i, (_, row) in enumerate(recs.iterrows()):
        with cols[i % 3]:
            st.markdown(f"### {row['product_name']}")
            st.write(f"{row['category']} · {row['store_id']}")
            st.write(f"Now **€{row['selling_price']:.2f}** | Save **€{(row['base_price'] - row['selling_price']):.2f}**")
            st.write(f"Expires in {int(row['days_until_expiry'])} day(s)")
            st.button("Add to shortlist", key=f"short_{i}")


def main():
    st.title("🥗 FreshWise")
    st.caption("Perishable retail optimization for managers and consumers")

    try:
        df = load_data()
    except Exception as e:
        st.error(str(e))
        st.stop()

    filtered = apply_filters(df)
    if filtered.empty:
        st.warning("No data left after filtering.")
        st.stop()

    role = st.radio("Choose your mode", ["Manager", "Consumer"], horizontal=True)

    if role == "Manager":
        tabs = st.tabs([
            "Executive Dashboard",
            "Category Intelligence",
            "Inventory & Replenishment",
            "Promotion Designer",
            "Risk Monitor",
        ])
        with tabs[0]:
            manager_dashboard(filtered)
        with tabs[1]:
            manager_category_intelligence(filtered)
        with tabs[2]:
            manager_inventory(filtered)
        with tabs[3]:
            manager_promotions(filtered)
        with tabs[4]:
            manager_risk(filtered)
    else:
        tabs = st.tabs([
            "Deal Finder",
            "Bundle Builder",
            "Personalized Promotions",
        ])
        with tabs[0]:
            consumer_deals(filtered)
        with tabs[1]:
            consumer_bundles(filtered)
        with tabs[2]:
            consumer_personal(filtered)

    with st.expander("About this app"):
        st.markdown(
            """
            - **Manager mode** turns data into inventory, markdown, and operational decisions.
            - **Consumer mode** surfaces discounted products, smart bundles, and personalized promotions.
            - Built for deployment on Hugging Face Docker Spaces with Streamlit.
            """
        )


if __name__ == "__main__":
    main()