app.py

import os
from pathlib import Path

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st

st.set_page_config(page_title="Freshie", page_icon="🐱", layout="wide", initial_sidebar_state="expanded")


def inject_css():
    st.markdown(
        """
        <style>
        .stApp {
            background:
                radial-gradient(circle at top right, rgba(57,198,233,0.10), transparent 28%),
                radial-gradient(circle at top left, rgba(142,219,99,0.12), transparent 24%),
                linear-gradient(180deg, #FFFDF7 0%, #F3FBF6 100%);
        }
        [data-testid="stAppViewContainer"] {
            background: transparent;
        }
        section[data-testid="stSidebar"] > div {
            background: rgba(255,255,255,0.99);
            box-shadow: 8px 0 24px rgba(0,0,0,0.06);
            border-right: 1px solid rgba(23,50,77,0.06);
        }
        [data-testid="stSidebarUserContent"] {
            padding-top: 0.5rem;
        }
        .hero-wrap {
            background:
                linear-gradient(135deg, rgba(255,255,255,0.94), rgba(255,253,247,0.97)),
                linear-gradient(90deg, rgba(51,196,110,0.06), rgba(57,198,233,0.08));
            border: 1px solid rgba(23,50,77,0.06);
            border-radius: 24px;
            padding: 18px 20px;
            box-shadow: 0 14px 30px rgba(0,0,0,0.05);
            margin-bottom: 14px;
        }
        .main-title {
            display:flex;
            align-items:center;
            justify-content:space-between;
            gap:18px;
            margin-bottom:8px;
        }
        .title-left {
            display:flex;
            align-items:center;
            gap:16px;
        }
        .logo-img {
            width: 110px;
            height: auto;
            border-radius: 18px;
            box-shadow: 0 10px 22px rgba(57,198,233,0.16);
            background: rgba(255,255,255,0.90);
            padding: 4px;
        }
        .brand-sub {
            color: #5E6B78;
            margin-top: -2px;
            margin-bottom: 8px;
            font-size: 1.02rem;
            max-width: 700px;
        }
        .hero-art {
            font-size: 2.6rem;
            opacity: 0.92;
            white-space: nowrap;
        }
        .search-pill {
            background: #FFFFFF;
            border: 1px solid rgba(23,50,77,0.06);
            border-radius: 999px;
            padding: 10px 14px;
            color: #5E6B78;
            box-shadow: 0 6px 18px rgba(0,0,0,0.04);
            margin-top: 10px;
        }
        .note-box {
            background: linear-gradient(135deg, #FFF7ED 0%, #FFFDF7 100%);
            border-left: 4px solid #FB923C;
            padding: 10px 12px;
            border-radius: 12px;
            margin: 8px 0 14px 0;
            color: #7C4A03;
            box-shadow: 0 8px 18px rgba(0,0,0,0.03);
        }
        div[data-testid="stMetric"] {
            background: rgba(255,255,255,0.96);
            border: 1px solid rgba(31,45,61,0.06);
            border-radius: 18px;
            padding: 12px 14px;
            box-shadow: 0 8px 18px rgba(0,0,0,0.04);
        }
        [data-testid="stDataFrame"] {
            background: #FFFFFF;
            border-radius: 16px;
            padding: 6px;
            box-shadow: 0 6px 16px rgba(0,0,0,0.03);
        }
        [data-baseweb="tab-list"] {
            gap: 8px;
        }
        </style>
        """,
        unsafe_allow_html=True,
    )


def find_data_path() -> Path | None:
    candidates = [
        Path("/app/perishable_goods_management.csv"),
        Path("perishable_goods_management.csv"),
        Path("/mnt/data/perishable_goods_management.csv"),
    ]
    for p in candidates:
        if p.exists():
            return p
    return None


@st.cache_data(show_spinner=False)
def load_data() -> pd.DataFrame:
    path = find_data_path()
    if path is None:
        raise FileNotFoundError("perishable_goods_management.csv not found in /app or current folder.")

    df = pd.read_csv(path)

    # Standardize likely date field
    date_col = None
    for c in ["transaction_date", "date", "Date"]:
        if c in df.columns:
            date_col = c
            break
    if date_col is None:
        raise ValueError("No transaction date column found.")
    if date_col != "transaction_date":
        df = df.rename(columns={date_col: "transaction_date"})

    df["transaction_date"] = pd.to_datetime(df["transaction_date"], errors="coerce")
    df = df.dropna(subset=["transaction_date"]).copy()

    # Expected columns with fallbacks
    if "discount_pct" not in df.columns:
        if "discount_percentage" in df.columns:
            df["discount_pct"] = df["discount_percentage"]
        else:
            df["discount_pct"] = 0.0

    for col in ["units_sold", "initial_quantity", "daily_demand", "profit", "days_until_expiry"]:
        if col not in df.columns:
            raise ValueError(f"Missing required column: {col}")

    if "category" not in df.columns:
        df["category"] = "Unknown"
    if "region" not in df.columns:
        df["region"] = "Unknown"
    if "store_id" not in df.columns:
        df["store_id"] = "STORE_001"
    if "product_name" not in df.columns:
        df["product_name"] = df["category"].astype(str)

    if "selling_price" not in df.columns and "price" in df.columns:
        df["selling_price"] = df["price"]
    if "base_price" not in df.columns and "selling_price" in df.columns:
        df["base_price"] = df["selling_price"] / (1 - df["discount_pct"].replace(1, 0.999).clip(0, 0.99))
    if "base_price" not in df.columns:
        df["base_price"] = 1.0
    if "selling_price" not in df.columns:
        df["selling_price"] = 1.0

    df["discount_pct"] = df["discount_pct"].fillna(0)
    if df["discount_pct"].max() > 1:
        df["discount_pct"] = df["discount_pct"] / 100
    df["discount_pct"] = df["discount_pct"].clip(0, 1)

    if "waste_pct" not in df.columns:
        denom = df["initial_quantity"].replace(0, np.nan)
        if "units_wasted" in df.columns:
            df["waste_pct"] = (df["units_wasted"] / denom).fillna(0)
        else:
            df["waste_pct"] = 0.0
    if df["waste_pct"].max() > 1:
        df["waste_pct"] = df["waste_pct"] / 100
    df["waste_pct"] = df["waste_pct"].clip(0, 1)

    if "units_wasted" not in df.columns:
        df["units_wasted"] = (df["waste_pct"] * df["initial_quantity"]).round()

    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["sell_through_pct"] = (df["units_sold"] / df["initial_quantity"].replace(0, np.nan)).fillna(0).clip(0, 1)
    df["month"] = df["transaction_date"].dt.to_period("M").astype(str)
    df["is_weekend"] = (df["transaction_date"].dt.dayofweek >= 5).astype(int)
    df["expiry_bucket"] = pd.cut(
        df["days_until_expiry"],
        bins=[-1, 1, 3, 7, 30, 10000],
        labels=["<=1d", "2-3d", "4-7d", "8-30d", ">30d"],
    ).astype(str)
    return df


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

    chosen_regions = st.sidebar.multiselect("Region", sorted(df["region"].dropna().unique()))
    available_stores = sorted(df.loc[df["region"].isin(chosen_regions), "store_id"].dropna().unique()) if chosen_regions else sorted(df["store_id"].dropna().unique())
    chosen_stores = st.sidebar.multiselect("Store", available_stores)

    category_options = ["All"] + sorted(df["category"].dropna().unique())
    chosen_category = st.sidebar.selectbox("Category", category_options)

    day_type = st.sidebar.selectbox("Day type", ["All", "Weekday", "Weekend"])
    use_expiry = st.sidebar.checkbox("Limit to inventory below 60 days until expiry", value=False)
    expiry_range = (0, 60)
    if use_expiry:
        expiry_range = st.sidebar.slider("Days until expiry", 0, 60, (0, 60))

    base = df.copy()
    if chosen_regions:
        base = base[base["region"].isin(chosen_regions)]
    if chosen_stores:
        base = base[base["store_id"].isin(chosen_stores)]
    if use_expiry:
        base = base[(base["days_until_expiry"] >= expiry_range[0]) & (base["days_until_expiry"] <= expiry_range[1])]
    if day_type == "Weekday":
        base = base[base["is_weekend"] == 0]
    elif day_type == "Weekend":
        base = base[base["is_weekend"] == 1]

    filtered = base.copy()
    if chosen_category != "All":
        filtered = filtered[filtered["category"] == chosen_category]

    scope_info = {
        "regions": chosen_regions,
        "stores": chosen_stores,
        "category": chosen_category,
        "day_type": day_type,
        "use_expiry": use_expiry,
        "expiry_range": expiry_range,
    }
    return filtered, base, scope_info


def forecast_filtered_demand(scope_df: pd.DataFrame, label: str = "Filtered selection") -> pd.DataFrame:
    d = scope_df.copy()
    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()
    fallback = ts["daily_demand"].tail(14).mean()

    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, fallback) for d in future_dates],
        "series": "Forecast",
        "scope": label,
    })
    hist = ts.tail(60).copy()
    hist["series"] = "Actual"
    hist["scope"] = label
    return pd.concat([hist, future], ignore_index=True)


def executive_overview(df: pd.DataFrame, exec_scope: pd.DataFrame, scope_info: dict):
    st.subheader("FRESHIE · Executive Overview")

    revenue = float((exec_scope["selling_price"] * exec_scope["units_sold"]).sum())
    profit = float(exec_scope["profit"].sum())
    units_sold = float(exec_scope["units_sold"].sum())
    units_wasted = float(exec_scope["units_wasted"].sum())

    c1, c2, c3, c4 = st.columns(4)
    c1.metric("Revenue", f"EUR {revenue:,.0f}")
    c2.metric("Profit", f"EUR {profit:,.0f}")
    c3.metric("Units sold", f"{units_sold:,.0f}")
    c4.metric("Units wasted", f"{units_wasted:,.0f}")

    st.markdown(
        '<div class="note-box"><b>Executive view:</b> monthly performance plus a short diagnosis of unusual revenue-profit gaps.</div>',
        unsafe_allow_html=True,
    )

    monthly = exec_scope.groupby("month").agg(
        revenue=("selling_price", lambda s: float((exec_scope.loc[s.index, "selling_price"] * exec_scope.loc[s.index, "units_sold"]).sum())),
        profit=("profit", "sum"),
        units_sold=("units_sold", "sum"),
        units_wasted=("units_wasted", "sum"),
        waste_rate=("waste_pct", "mean"),
        stockout_rate=("stockout_flag", "mean"),
    ).reset_index().sort_values("month")

    monthly["gap"] = (monthly["revenue"] - monthly["profit"]).abs()
    gap_std = monthly["gap"].std(ddof=0)
    monthly["gap_z"] = (monthly["gap"] - monthly["gap"].mean()) / (gap_std if gap_std else 1)

    left, right = st.columns([1.3, 1])
    with left:
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=monthly["month"], y=monthly["revenue"], name="Revenue", mode="lines+markers"))
        fig.add_trace(go.Scatter(x=monthly["month"], y=monthly["profit"], name="Profit", mode="lines+markers", yaxis="y2"))
        fig.update_layout(
            title="Monthly revenue and profit trend",
            yaxis=dict(title="Revenue"),
            yaxis2=dict(title="Profit", overlaying="y", side="right"),
            margin=dict(l=10, r=10, t=40, b=10),
        )
        st.plotly_chart(fig, use_container_width=True)

        fig2 = go.Figure()
        fig2.add_trace(go.Bar(x=monthly["month"], y=monthly["units_sold"], name="Units sold"))
        fig2.add_trace(go.Scatter(x=monthly["month"], y=monthly["units_wasted"], name="Units wasted trend", mode="lines+markers", yaxis="y2"))
        fig2.update_layout(
            title="Units sold vs units wasted by month",
            yaxis=dict(title="Units sold"),
            yaxis2=dict(title="Units wasted", overlaying="y", side="right"),
            margin=dict(l=10, r=10, t=40, b=10),
        )
        st.plotly_chart(fig2, use_container_width=True)

    with right:
        st.markdown("### Executive diagnosis")
        preferred_months = [m for m in ["2023-01", "2023-02", "2023-03", "2024-01", "2024-10"] if m in monthly["month"].astype(str).tolist()]
        flagged = monthly[monthly["month"].astype(str).isin(preferred_months)].copy()
        if flagged.empty:
            flagged = monthly[monthly["gap_z"] > 1].copy()
        if flagged.empty:
            flagged = monthly.nlargest(min(4, len(monthly)), "gap").copy()

        for _, row in flagged.iterrows():
            mo = row["month"]
            sub = exec_scope[exec_scope["month"] == mo].copy()
            if sub.empty:
                continue
            top_waste_category = sub.groupby("category")["waste_pct"].mean().sort_values(ascending=False).index[0]
            top_stockout_region = sub.groupby("region")["stockout_flag"].mean().sort_values(ascending=False).index[0]
            top_discount_category = sub.groupby("category")["discount_pct"].mean().sort_values(ascending=False).index[0]

            reasons = []
            if sub["waste_pct"].mean() > exec_scope["waste_pct"].mean():
                reasons.append("waste rose above baseline")
            if sub["stockout_flag"].mean() > exec_scope["stockout_flag"].mean():
                reasons.append("stockout pressure increased")
            if sub["discount_pct"].mean() > exec_scope["discount_pct"].mean():
                reasons.append("markdown intensity was heavier")
            if sub["profit"].mean() < exec_scope["profit"].mean():
                reasons.append("unit profitability weakened")
            reason_text = ", ".join(reasons) if reasons else "mixed category-region volatility"

            st.markdown(
                f"- **{mo}**: the revenue-profit gap widened. Likely drivers were **{top_waste_category}**, "
                f"pressure in **{top_stockout_region}**, and markdown depth in **{top_discount_category}**. "
                f"Overall explanation: {reason_text}."
            )

        st.markdown("### Current operating signal")
        selected_stores = scope_info.get("stores", [])
        selected_regions = scope_info.get("regions", [])

        if selected_stores:
            pass
        elif selected_regions:
            worst_store = exec_scope.groupby("store_id")["waste_pct"].mean().sort_values(ascending=False).index[0]
            st.markdown(f"- Highest waste pressure currently sits in **{worst_store}**.")
        else:
            worst_region = exec_scope.groupby("region")["waste_pct"].mean().sort_values(ascending=False).index[0]
            st.markdown(f"- Highest waste pressure currently sits in **{worst_region}**.")

        best_category = exec_scope.groupby("category")["profit"].mean().sort_values(ascending=False).index[0]
        risky_category = exec_scope.groupby("category")["waste_pct"].mean().sort_values(ascending=False).index[0]
        stockout_category = exec_scope.groupby("category")["stockout_flag"].mean().sort_values(ascending=False).index[0]
        st.markdown(f"- Strongest profit signal currently comes from **{best_category}**.")
        st.markdown(f"- Highest waste exposure category is **{risky_category}**.")
        st.markdown(f"- Highest stockout exposure category is **{stockout_category}**.")




def build_category_recommendations(row: pd.Series, benchmark: pd.DataFrame) -> list[str]:
    demand_mean = float(benchmark["avg_demand"].mean()) if len(benchmark) else 0.0
    stockout_mean = float(benchmark["stockout_rate"].mean()) if len(benchmark) else 0.0
    waste_mean = float(benchmark["waste_pct"].mean()) if len(benchmark) else 0.0
    profit_mean = float(benchmark["avg_profit"].mean()) if len(benchmark) else 0.0

    demand_low = row["avg_demand"] < max(5.0, demand_mean * 0.5)
    demand_high = row["avg_demand"] > max(20.0, demand_mean * 1.2)
    stockout_high = row["stockout_rate"] > max(0.10, stockout_mean * 1.2)
    waste_high = row["waste_pct"] > max(0.15, waste_mean * 1.1)
    waste_extreme = row["waste_pct"] >= 0.30
    profit_weak = row["avg_profit"] < min(0.0, profit_mean * 0.8)

    advice: list[str] = []

    # Priority 1: excess inventory / expiry risk must override any default maintain rule.
    if waste_extreme:
        advice.append("start markdown earlier")

    # Priority 2: high demand and frequent unavailability.
    if demand_high and stockout_high:
        advice.append("increase replenishment")

    # Priority 3: slow-moving and unprofitable inventory.
    if demand_low and (waste_high or profit_weak):
        advice.append("reduce replenishment")
    if waste_high or profit_weak:
        advice.append("review mix and margin")

    if not advice:
        advice.append("maintain current playbook")

    deduped: list[str] = []
    for item in advice:
        if item not in deduped:
            deduped.append(item)
    return deduped

def category_intelligence(df: pd.DataFrame):
    st.subheader("FRESHIE · Category Intelligence")
    st.markdown(
        '<div class="note-box"><b>Category view:</b> chart-led category, region, store, and forecast insights for the current filters.</div>',
        unsafe_allow_html=True,
    )

    cat_summary = df.groupby("category").agg(
        avg_demand=("daily_demand", "mean"),
        avg_stock=("initial_quantity", "mean"),
        avg_remaining=("leftover_units", "mean"),
        waste_pct=("waste_pct", "mean"),
        stockout_rate=("stockout_flag", "mean"),
        avg_profit=("profit", "mean"),
        sell_through=("sell_through_pct", "mean"),
        lost_sales=("lost_sales_units", "mean"),
    ).reset_index()

    region_cat = df.groupby(["region", "category"]).agg(
        avg_demand=("daily_demand", "mean"),
        avg_profit=("profit", "mean"),
        waste_pct=("waste_pct", "mean"),
        stockout_rate=("stockout_flag", "mean"),
    ).reset_index()

    store_cat = df.groupby(["store_id", "category"]).agg(
        avg_demand=("daily_demand", "mean"),
        avg_stock=("initial_quantity", "mean"),
        waste_pct=("waste_pct", "mean"),
        stockout_rate=("stockout_flag", "mean"),
        avg_profit=("profit", "mean"),
    ).reset_index()

    weekpart = df.copy()
    weekpart["week_part"] = np.where(weekpart["is_weekend"] == 1, "Weekend", "Weekday")
    week_summary = weekpart.groupby(["category", "week_part"]).agg(
        avg_demand=("daily_demand", "mean"),
        avg_stock=("initial_quantity", "mean"),
        waste_pct=("waste_pct", "mean"),
        stockout_rate=("stockout_flag", "mean"),
        avg_profit=("profit", "mean"),
    ).reset_index()

    k1, k2, k3, k4 = st.columns(4)
    k1.metric("Avg demand", f"{df['daily_demand'].mean():.1f}")
    k2.metric("Waste rate", f"{df['waste_pct'].mean():.1%}")
    k3.metric("Stockout rate", f"{df['stockout_flag'].mean():.1%}")
    k4.metric("Avg profit", f"EUR {df['profit'].mean():.2f}")

    top_left, top_right = st.columns([1.25, 1])
    with top_left:
        fig = px.bar(
            cat_summary.sort_values("avg_demand", ascending=False),
            x="category",
            y=["avg_demand", "avg_stock", "avg_remaining"],
            barmode="group",
            title="Category demand, stock, and remaining inventory",
        )
        st.plotly_chart(fig, use_container_width=True)

        fig2 = px.scatter(
            cat_summary,
            x="stockout_rate",
            y="waste_pct",
            size=(cat_summary["avg_profit"].clip(lower=0) + 1),
            color="category",
            hover_data=["avg_demand", "avg_stock", "avg_remaining", "sell_through", "lost_sales"],
            title="Category trade-off: stockout vs waste",
        )
        st.plotly_chart(fig2, use_container_width=True)

    with top_right:
        st.markdown("### Core indicators and recommendations")
        for _, r in cat_summary.sort_values("avg_demand", ascending=False).iterrows():
            advice = build_category_recommendations(r, cat_summary)
            st.markdown(
                f"- **{r['category']}**: demand {r['avg_demand']:.1f}, stock {r['avg_stock']:.1f}, "
                f"waste {r['waste_pct']:.1%}, stockout {r['stockout_rate']:.1%}, profit EUR {r['avg_profit']:.2f}. "
                f"**Recommendation:** " + "; ".join(advice) + "."
            )

    lower_left, lower_right = st.columns([1.25, 1])
    with lower_left:
        fig3 = px.density_heatmap(
            region_cat,
            x="category",
            y="region",
            z="avg_profit",
            title="Regional profit heatmap by category",
        )
        st.plotly_chart(fig3, use_container_width=True)

        fig4 = px.bar(
            week_summary,
            x="category",
            y="avg_demand",
            color="week_part",
            barmode="group",
            title="Weekday vs weekend demand by category",
        )
        st.plotly_chart(fig4, use_container_width=True)

    with lower_right:
        fig5 = px.bar(
            store_cat.sort_values("avg_profit", ascending=False).head(20),
            x="store_id",
            y="avg_profit",
            color="category",
            title="Top filtered stores by category profit",
        )
        st.plotly_chart(fig5, use_container_width=True)

        scope_label = "Filtered selection"
        if df["store_id"].nunique() == 1:
            scope_label = df["store_id"].iloc[0]
        elif df["store_id"].nunique() > 1:
            scope_label = f"{df['store_id'].nunique()} stores"
        elif df["region"].nunique() >= 1:
            scope_label = " / ".join(sorted(df["region"].dropna().unique().tolist()))

        forecast_df = forecast_filtered_demand(df, scope_label)
        if not forecast_df.empty:
            fig6 = px.line(
                forecast_df,
                x="transaction_date",
                y="daily_demand",
                color="series",
                title=f"Demand forecast for {scope_label}",
            )
            st.plotly_chart(fig6, use_container_width=True)
            st.caption("Forecast method: weekday seasonal average from the most recent 56 days, with a 14-day mean fallback.")


def inventory_page(df: pd.DataFrame):
    st.subheader("FRESHIE · Inventory & Replenishment")
    st.markdown('<div class="note-box"><b>Audience:</b> supply chain. Use this page for reorder, transfer, expiry control, and stock-balancing decisions.</div>', unsafe_allow_html=True)

    work = df.copy()
    work["recommended_order_qty"] = (1.15 * work["daily_demand"] - work["leftover_units"]).clip(lower=0).round()
    work.loc[work["days_until_expiry"] <= 7, "recommended_order_qty"] *= 0.75
    work["recommended_order_qty"] = work["recommended_order_qty"].round()

    left, right = st.columns([1.2, 1])
    with left:
        cat = work.groupby("category")[["initial_quantity", "recommended_order_qty", "waste_pct", "profit", "lost_sales_units"]].mean().reset_index()
        cat["order_reduction_pct"] = 1 - cat["recommended_order_qty"] / cat["initial_quantity"].replace(0, np.nan)
        cat["order_reduction_pct"] = cat["order_reduction_pct"].fillna(0)
        fig = px.bar(
            cat.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)

        fig2 = px.scatter(
            cat,
            x="waste_pct",
            y="lost_sales_units",
            size=(cat["profit"].clip(lower=0) + 1),
            color="category",
            title="Waste vs lost sales by category",
        )
        st.plotly_chart(fig2, use_container_width=True)

    with right:
        st.markdown("### Action shortlist")
        shortlist = work.sort_values(["waste_pct", "lost_sales_units"], ascending=[False, False])[[
            "store_id", "category", "product_name", "daily_demand", "leftover_units", "days_until_expiry", "waste_pct", "recommended_order_qty"
        ]].head(15)
        st.dataframe(shortlist, use_container_width=True, hide_index=True)

        st.markdown("### Expiry pressure")
        expiry = work.groupby("expiry_bucket")[["units_wasted", "leftover_units", "units_sold"]].sum().reset_index()
        fig3 = px.bar(
            expiry,
            x="expiry_bucket",
            y=["units_wasted", "leftover_units"],
            barmode="group",
            title="Wasted and leftover units by expiry bucket",
        )
        st.plotly_chart(fig3, use_container_width=True)

    st.markdown("### What-if Simulator")
    st.caption("Simulator logic: deterministic business-rule heuristic. Waste and profit changes are estimated with fixed uplift/reduction coefficients, not machine learning.")
    col1, col2, col3 = st.columns(3)
    selected_category = col1.selectbox("Category for simulation", sorted(df["category"].unique()), key="inv_sim_cat")
    order_cut = col2.slider("Reduce order quantity by %", 0, 40, 10, key="inv_sim_cut")
    markdown_shift = col3.slider("Advance markdown trigger by days", 0, 5, 2, key="inv_sim_mark")

    sim = df[df["category"] == selected_category].copy()
    current_waste = sim["waste_pct"].mean()
    current_profit = sim["profit"].mean()
    current_stockout = sim["stockout_flag"].mean()
    waste_reduction = 0.35 * (order_cut / 100) + 0.015 * markdown_shift
    stockout_rise = 0.12 * (order_cut / 100)
    sim_waste = max(current_waste * (1 - waste_reduction), 0)
    sim_profit = current_profit * (1 + 0.08 * (order_cut / 100) + 0.03 * markdown_shift)
    sim_stockout = min(current_stockout * (1 + stockout_rise), 1)

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

    st.markdown("### Transfer suggestions")
    receiver = work.groupby(["store_id", "region", "category"]).agg(
        remaining_inventory=("leftover_units", "sum"),
        demand=("daily_demand", "sum"),
        unmet_demand=("lost_sales_units", "sum"),
        avg_days_until_expiry=("days_until_expiry", "mean"),
    ).reset_index()
    donors = receiver.copy()
    donors["surplus_qty"] = donors["remaining_inventory"] - donors["demand"]

    transfer_rows = []
    need_df = receiver[receiver["unmet_demand"] > 0].copy()
    for _, r in need_df.iterrows():
        pool = donors[
            (donors["category"] == r["category"]) &
            (donors["store_id"] != r["store_id"]) &
            (donors["surplus_qty"] > 0)
        ].copy()
        if pool.empty:
            best_route = "No feasible donor"
            same_region_options = "No same-region donor"
            cross_region_options = "No cross-region donor"
            transfer_qty = 0
        else:
            pool["priority_rank"] = (pool["region"] != r["region"]).astype(int)
            pool = pool.sort_values(["priority_rank", "avg_days_until_expiry", "surplus_qty"], ascending=[True, False, False])
            same_region = pool[pool["priority_rank"] == 0].head(3)
            cross_region = pool[pool["priority_rank"] == 1].head(3)
            best = pool.iloc[0]
            transfer_qty = int(min(r["unmet_demand"], max(best["surplus_qty"], 0)))
            def label(d):
                tier = "same-region" if d["priority_rank"] == 0 else "cross-region"
                return f"{d['store_id']} ({tier}, expiry {d['avg_days_until_expiry']:.1f}d, surplus {int(d['surplus_qty'])})"
            best_route = label(best)
            same_region_options = "; ".join(label(d) for _, d in same_region.iterrows()) if not same_region.empty else "No same-region donor"
            cross_region_options = "; ".join(label(d) for _, d in cross_region.iterrows()) if not cross_region.empty else "No cross-region donor"

        transfer_rows.append({
            "store_id": r["store_id"],
            "region": r["region"],
            "category": r["category"],
            "remaining_inventory": int(r["remaining_inventory"]),
            "demand": int(r["demand"]),
            "unmet_demand": int(r["unmet_demand"]),
            "recommended_transfer_qty": transfer_qty,
            "best_route": best_route,
            "same_region_options": same_region_options,
            "cross_region_options": cross_region_options,
        })

    transfer_df = pd.DataFrame(transfer_rows)
    st.caption("Transfer logic: same-region donors are prioritized first. Because the source data has no distance field, donor ranking uses region priority, remaining shelf life, and surplus quantity.")
    if transfer_df.empty:
        st.success("No filtered store currently shows unmet demand that needs transfer support.")
    else:
        st.dataframe(transfer_df.sort_values(["unmet_demand", "recommended_transfer_qty"], ascending=[False, False]), use_container_width=True, hide_index=True)


def promotion_page(df: pd.DataFrame):
    st.subheader("FRESHIE · Promotion Designer")
    st.markdown('<div class="note-box"><b>Audience:</b> marketing. Use this page to test markdown depth, bundle logic, and campaign copy.</div>', unsafe_allow_html=True)
    st.caption("Promotion designer logic: business-rule simulator. Demand lift is estimated from a base uplift + discount effect + optional bundle effect.")

    left, right = st.columns([1, 1.25])
    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"], key="promo_exp")
        discount = st.slider("Discount %", 0, 50, 18, key="promo_disc")
        bundle = st.checkbox("Bundle with complementary items", value=True, key="promo_bundle")
        weekend_only = st.checkbox("Weekend campaign only", value=False, key="promo_weekend")

        cat_df = df[df["category"] == promo_category].copy()
        sub = cat_df[cat_df["expiry_bucket"].astype(str) == 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

        # recommend optimal discount by scanning candidates for chosen category and expiry bucket
        sim_base = sub if len(sub) else cat_df[cat_df["expiry_bucket"].astype(str) == str(expiry_target)].copy()
        sims = []
        for disc in range(0, 55, 5):
            lift = 0.08 + disc / 200 + (0.06 if bundle else 0)
            sim_profit = (sim_base["profit"].mean() * (1 + lift - disc / 150)) if len(sim_base) else 0
            sim_waste = (sim_base["waste_pct"].mean() * (1 - min(0.35, lift))) if len(sim_base) else 0
            score = sim_profit - 120 * sim_waste
            sims.append({"discount": disc, "sim_profit": sim_profit, "sim_waste": sim_waste, "score": score})
        sim_df = pd.DataFrame(sims)
        best = sim_df.sort_values("score", ascending=False).iloc[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"EUR {est_profit:.2f}")
        st.metric("Suggested discount", f"{int(best['discount'])}%")

        st.caption("Suggested discount logic: test 0% to 50% in 5-point steps and choose the discount that maximizes a simple score = simulated profit - waste penalty.")

        st.markdown("### Campaign brief")
        campaign_type = "weekend bundle campaign" if bundle and weekend_only else "bundle campaign" if bundle else "markdown campaign"
        st.success(f"Run a {int(best['discount'])}% {campaign_type} for {promo_category} items in {expiry_target}.")

    with right:
        order = ["<=1d", "2-3d", "4-7d", "8-30d", ">30d"]
        cat_ts = cat_df.groupby(["month", "expiry_bucket"])[["discount_pct", "waste_pct", "sell_through_pct"]].mean().reset_index()
        cat_ts["expiry_bucket"] = pd.Categorical(cat_ts["expiry_bucket"], categories=order, ordered=True)
        cat_ts = cat_ts.sort_values(["expiry_bucket", "month"])
        present = cat_ts["expiry_bucket"].dropna().astype(str).unique().tolist()

        if not cat_ts.empty:
            fig_disc = px.line(
                cat_ts,
                x="month",
                y="discount_pct",
                color="expiry_bucket",
                markers=True,
                title=f"Discount trend for {promo_category} across expiry buckets",
            )
            st.plotly_chart(fig_disc, use_container_width=True)

            fig_waste = px.line(
                cat_ts,
                x="month",
                y="waste_pct",
                color="expiry_bucket",
                markers=True,
                title=f"Waste trend for {promo_category} across expiry buckets",
            )
            st.plotly_chart(fig_waste, use_container_width=True)

            fig_sell = px.line(
                cat_ts,
                x="month",
                y="sell_through_pct",
                color="expiry_bucket",
                markers=True,
                title=f"Sell-through trend for {promo_category} across expiry buckets",
            )
            st.plotly_chart(fig_sell, use_container_width=True)

            missing = [b for b in order if b not in present]
            if missing:
                st.caption("Buckets without records for this category are omitted: " + ", ".join(missing))
        else:
            st.info("No records match the current promotion category.")

        st.markdown("### Recommended promotion copy")
        st.info(
            f"Fresh pick alert: enjoy {int(best['discount'])}% off selected {promo_category.lower()} items"
            + (" this weekend" if weekend_only else "")
            + (" with smart bundle savings" if bundle else " while they are still at peak freshness")
            + f". Prioritize the {expiry_target} bucket and highlight freshness, value, and limited-time availability."
        )


def consumer_deals(df: pd.DataFrame):
    st.subheader("FRESHIE · Deal Finder")
    stores = sorted(df["store_id"].dropna().unique())
    if not stores:
        st.warning("No stores available in the current filter.")
        return
    chosen_store = st.selectbox("Choose store first", stores)
    store_df = df[df["store_id"] == chosen_store].copy()

    c1, c2, c3 = st.columns(3)
    budget_range = c1.slider("Budget range (EUR)", 1, 60, (1, 20))
    preferred_category = c2.selectbox("Preferred category", ["All"] + sorted(store_df["category"].unique()))
    expiry_range = c3.slider("Days until expiry", 1, 14, (1, 5))

    deals = store_df[
        (store_df["days_until_expiry"] >= expiry_range[0]) &
        (store_df["days_until_expiry"] <= expiry_range[1]) &
        (store_df["selling_price"] >= budget_range[0]) &
        (store_df["selling_price"] <= budget_range[1])
    ].copy()
    if preferred_category != "All":
        deals = deals[deals["category"] == preferred_category]
    deals["savings"] = (deals["base_price"] - deals["selling_price"]).clip(lower=0)
    deals["deal_score"] = deals["discount_pct"] * 0.6 + deals["sell_through_pct"] * 0.2 + (1 - deals["waste_pct"]) * 0.2
    deals = deals.sort_values(["deal_score", "savings"], ascending=False)

    st.markdown('<div class="note-box"><b>Store marketing view:</b> all deal recommendations below are scoped to the selected store so the shopper sees store-specific promotions and products.</div>', unsafe_allow_html=True)

    show = deals[["product_name", "category", "days_until_expiry", "base_price", "selling_price", "discount_pct", "savings"]].head(20).copy()
    icon_map = {
        "Bakery": "🥐", "Beverages": "🧃", "Dairy": "🥛", "Deli": "🧺",
        "Meat": "🥩", "Pharmaceuticals": "💊", "Produce": "🥬", "Ready_to_Eat": "🍱", "Seafood": "🐟"
    }
    show["item"] = show.apply(lambda r: f"{icon_map.get(str(r['category']), '📦')} {r['product_name']}", axis=1)
    st.dataframe(show[["item", "category", "days_until_expiry", "base_price", "selling_price", "discount_pct", "savings"]], use_container_width=True, hide_index=True)

    st.markdown("### Best current deals")
    top_cards = deals.head(6)
    cols = st.columns(3)
    for i, (_, row) in enumerate(top_cards.iterrows()):
        icon = icon_map.get(str(row["category"]), "📦")
        with cols[i % 3]:
            st.markdown(f"**{icon} {row['product_name']}**")
            st.write(f"{row['category']} · expires in {int(row['days_until_expiry'])} day(s)")
            st.write(f"Now EUR {row['selling_price']:.2f} | Save EUR {row['savings']:.2f}")
            st.caption(f"Discount: {row['discount_pct']:.0%} · Store: {chosen_store}")


def consumer_bundles(df: pd.DataFrame):
    st.subheader("FRESHIE · Bundle Builder")
    stores = sorted(df["store_id"].dropna().unique())
    if not stores:
        st.warning("No stores available in the current filter.")
        return

    chosen_store = st.selectbox("Choose store", stores, key="bundle_store")
    store_df = df[df["store_id"] == chosen_store].copy()

    c1, c2, c3 = st.columns(3)
    budget_range = c1.slider("Bundle budget range (EUR)", 8, 80, (8, 25))
    theme = c2.selectbox("Bundle theme", ["Quick dinner", "Healthy protein", "Family breakfast", "Budget saver"])
    expiry_range = c3.slider("Use items expiring within days", 1, 10, (1, 5), key="bundle_exp")

    if "bundle_seed" not in st.session_state:
        st.session_state["bundle_seed"] = 0
    if st.button("Refresh bundle"):
        st.session_state["bundle_seed"] += 1

    work = store_df[
        (store_df["days_until_expiry"] >= expiry_range[0]) &
        (store_df["days_until_expiry"] <= expiry_range[1]) &
        (store_df["selling_price"] <= budget_range[1])
    ].copy()

    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"].dropna().unique()),
    }
    work = work[work["category"].isin(theme_map.get(theme, []))].copy()
    work["score"] = work["discount_pct"] * 0.5 + (1 - work["waste_pct"]) * 0.2 + work["sell_through_pct"] * 0.3

    if len(work) == 0:
        st.warning("No bundle fits the current conditions.")
        return

    seed = int(st.session_state["bundle_seed"])
    candidate_frames = []
    for idx, (cat_name, grp) in enumerate(work.groupby("category")):
        grp = grp.sort_values(["score", "selling_price"], ascending=[False, True]).head(10).copy()
        if len(grp) > 1:
            shift = (seed + idx) % len(grp)
            grp = pd.concat([grp.iloc[shift:], grp.iloc[:shift]], ignore_index=True)
        candidate_frames.append(grp)
    work = pd.concat(candidate_frames, ignore_index=True)

    if theme == "Budget saver":
        work = work.sort_values(["selling_price", "score"], ascending=[True, False])
    else:
        work = work.sort_values(["score", "selling_price"], ascending=[False, True])

    picked, subtotal, used_categories = [], 0.0, set()
    for _, row in work.iterrows():
        if subtotal + row["selling_price"] <= budget_range[1]:
            if theme != "Budget saver" and row["category"] in used_categories:
                continue
            picked.append(row)
            subtotal += row["selling_price"]
            used_categories.add(row["category"])
        if len(picked) >= 5:
            break

    if not picked:
        st.warning("No bundle fits the current conditions.")
        return

    bundle = pd.DataFrame(picked)
    base_total = float(bundle["base_price"].sum())
    item_total = float(bundle["selling_price"].sum())

    if item_total >= 45:
        bundle_discount = 0.15
    elif item_total >= 30:
        bundle_discount = 0.12
    elif item_total >= 20:
        bundle_discount = 0.10
    elif item_total >= 12:
        bundle_discount = 0.08
    else:
        bundle_discount = 0.05

    bundle_saving = item_total * bundle_discount
    final_total = max(item_total - bundle_saving, 0)
    saved = base_total - final_total

    k1, k2, k3, k4 = st.columns(4)
    k1.metric("Bundle subtotal", f"EUR {item_total:.2f}")
    k2.metric("Bundle discount", f"{bundle_discount:.0%}")
    k3.metric("Bundle total", f"EUR {final_total:.2f}")
    k4.metric("You save", f"EUR {saved:.2f}")

    st.dataframe(
        bundle[["product_name", "category", "selling_price", "base_price", "discount_pct", "days_until_expiry"]],
        use_container_width=True,
        hide_index=True,
    )
    st.caption(
        "Bundle pricing logic: items keep their current markdowns, then the system applies an extra basket discount. "
        "Higher basket subtotals receive a deeper extra discount. Refresh rotates the candidate pool to surface a different combination."
    )



def consumer_personal(df: pd.DataFrame):
    st.subheader("FRESHIE · Personalized Promotions")
    stores = sorted(df["store_id"].dropna().unique())
    if not stores:
        st.warning("No stores available in the current filter.")
        return
    chosen_store = st.selectbox("Choose store", stores, key="personal_store")
    store_df = df[df["store_id"] == chosen_store].copy()

    favorite = st.selectbox("Favorite category", sorted(store_df["category"].unique()), key="cp_fav")
    price_range = st.slider("Price range (EUR)", 1, 30, (1, 10), key="cp_cap")
    expiry_range = st.slider("Days until expiry", 1, 14, (1, 7), key="cp_days")
    recs = store_df[
        (store_df["category"] == favorite) &
        (store_df["selling_price"] >= price_range[0]) &
        (store_df["selling_price"] <= price_range[1]) &
        (store_df["days_until_expiry"] >= expiry_range[0]) &
        (store_df["days_until_expiry"] <= expiry_range[1])
    ].copy()
    recs["score"] = recs["discount_pct"] * 0.55 + recs["sell_through_pct"] * 0.20 + (1 - recs["waste_pct"]) * 0.25
    recs = recs.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']} · {chosen_store}")
            st.write(f"Now EUR {row['selling_price']:.2f}")
            st.write(f"Expires in {int(row['days_until_expiry'])} day(s)")
            st.caption(f"Discount: {row['discount_pct']:.0%}")


def consumer_wait_or_buy(df: pd.DataFrame):
    st.subheader("FRESHIE · Wait or buy now?")
    st.markdown(
        """
        Get smart purchase timing suggestions based on your selected region and product category.  
        The system evaluates each item's sell-through rate and current discount across stores to indicate whether it is better to buy now or wait for a potentially better deal.
        """
    )

    work = df.copy()
    if work.empty:
        st.info("No records match the current filters.")
        return

    # Keep the logic on top of the current sidebar filters.
    # Only add an optional category selector when multiple categories remain visible.
    categories = sorted(work["category"].dropna().unique().tolist())
    if len(categories) > 1:
        selected_category = st.selectbox("Choose category", categories, key="wait_buy_category")
        work = work[work["category"] == selected_category].copy()

    if work.empty:
        st.info("No records remain after the category selection.")
        return

    work["discount"] = work["discount_pct"].fillna(0).clip(lower=0)
    work["sell_through"] = work["sell_through_pct"].fillna(0).clip(lower=0)

    def classify_item(row):
        if row["sell_through"] > 0.7:
            return "buy now", "selling fast"
        if row["sell_through"] < 0.4 and row["discount"] < 0.2:
            return "wait it", "inventory looks comfortable and discount is still modest"
        if row["sell_through"] < 0.4 and row["discount"] > 0.2:
            return "suggested", "inventory is comfortable and discount is attractive"
        return "no special signal", "balanced signal"

    labels = work.apply(classify_item, axis=1)
    work["suggestion"] = [x[0] for x in labels]
    work["reason"] = [x[1] for x in labels]

    c1, c2, c3, c4 = st.columns(4)
    c1.metric("Items shown", f"{len(work):,}")
    c2.metric("Buy now", int((work["suggestion"] == "buy now").sum()))
    c3.metric("Wait it", int((work["suggestion"] == "wait it").sum()))
    c4.metric("Suggested", int((work["suggestion"] == "suggested").sum()))

    display = work[[
        "product_name",
        "category",
        "store_id",
        "days_until_expiry",
        "sell_through",
        "discount",
        "stockout_flag",
        "suggestion",
        "reason",
    ]].copy()

    display = display.sort_values(
        ["suggestion", "days_until_expiry", "discount", "product_name"],
        ascending=[True, True, False, True],
    )

    st.dataframe(display, use_container_width=True, hide_index=True)

    summary = (
        display["suggestion"]
        .value_counts()
        .rename_axis("suggestion")
        .reset_index(name="items")
        .sort_values("items", ascending=False)
    )
    fig = px.bar(
        summary,
        x="suggestion",
        y="items",
        color="suggestion",
        title="Wait or buy now suggestion mix",
    )
    st.plotly_chart(fig, use_container_width=True)


def manager_manual():
    st.subheader("FRESHIE · User Manual (Manager)")
    st.markdown("""
    **Executive Overview**
    - Track revenue, profit, units sold, and units wasted by month.
    - Use the diagnosis panel to understand abnormal revenue-profit gaps.

    **Category Intelligence**
    - Compare categories on demand, stock, waste, stockout, and profit.
    - Read the category recommendations and forecast for the current selection.

    **Inventory & Replenishment**
    - Review reorder guidance by category.
    - Use the what-if simulator to see the trade-off between waste, profit, and stockout.

    **Promotion Designer**
    - Test markdown depth, expiry targeting, and bundle logic.
    - Use the campaign brief and promotion copy as a starting point for activation.
    """)


def consumer_manual():
    st.subheader("FRESHIE · User Manual (Consumer)")
    st.markdown("""
    **Deal Finder**
    - Browse the strongest discounted products under your preferred budget and expiry window.

    **Bundle Builder**
    - Build a themed basket while staying under budget.

    **Personalized Promotions**
    - Focus on your favorite category and a comfortable price cap.

    **Wait or Buy Now?**
    - Get smart purchase timing suggestions based on your selected region and product category.
    - The system evaluates each item's sell-through rate and current discount across stores to indicate whether it is better to buy now or wait for a potentially better deal.
    """)


def main():
    inject_css()
    st.markdown(
        """
        <div class="hero-wrap">
            <div class="main-title">
                <div class="title-left">
                    <img class="logo-img" src="data:image/png;base64,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" />
                    <div>
                        <div class="brand-sub">A warm, friendly fresh-food assistant for stores, managers, and everyday shoppers.</div>
                    </div>
                </div>
                <div class="hero-art">🥬 🍓 🐟 🍞</div>
            </div>
            <div class="search-pill">🔎 Fresh, reliable, low-waste decisions across fish, produce, dairy, bakery, and more</div>
        </div>
        """,
        unsafe_allow_html=True,
    )

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

    filtered, exec_scope, scope_info = 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 Overview",
            "Category Intelligence",
            "Inventory & Replenishment",
            "Promotion Designer",
            "User Manual",
        ])
        with tabs[0]:
            executive_overview(filtered, exec_scope, scope_info)
        with tabs[1]:
            category_intelligence(filtered)
        with tabs[2]:
            inventory_page(filtered)
        with tabs[3]:
            promotion_page(filtered)
        with tabs[4]:
            manager_manual()
    else:
        tabs = st.tabs(["Deal Finder", "Bundle Builder", "Personalized Promotions", "Wait or buy now?", "User Manual"])
        with tabs[0]:
            consumer_deals(filtered)
        with tabs[1]:
            consumer_bundles(filtered)
        with tabs[2]:
            consumer_personal(filtered)
        with tabs[3]:
            consumer_wait_or_buy(filtered)
        with tabs[4]:
            consumer_manual()

    with st.expander("About this app"):
        st.markdown("""
        - Stable FRESHIE UI baseline.
        - Built to avoid the white-screen issues caused by heavier UI logic.
        - Next step: reintroduce richer branding and additional consumer features incrementally.
        """)


if __name__ == "__main__":
    main()