Upload app.py

app.py

@@ -282,7 +282,7 @@ def executive_overview(df: pd.DataFrame):
     c4.metric("Units wasted", f"{units_wasted:,.0f}")
 
     st.markdown(
-        '<div class="note-box"><b>Executive logic:</b> this page gives a clean monthly view of revenue, profit, sold units, and wasted units for the current filter selection. It also flags the months where the revenue-profit gap looks unusually wide.</div>',
+        '<div class="note-box"><b>Executive view:</b> monthly performance plus a short diagnosis of unusual revenue-profit gaps.</div>',
         unsafe_allow_html=True,
     )
 
@@ -373,7 +373,7 @@ def executive_overview(df: pd.DataFrame):
 def category_intelligence(df: pd.DataFrame):
     st.subheader("FRESHIE · Category Intelligence")
     st.markdown(
-        '<div class="note-box"><b>Coverage:</b> this page covers all visible categories from the current sidebar filters. It keeps the story chart-led and focuses on category, region, store, and demand forecast signals.</div>',
+        '<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,
     )
 
@@ -513,7 +513,7 @@ def category_intelligence(df: pd.DataFrame):
 
 def inventory_page(df: pd.DataFrame):
     st.subheader("FRESHIE · Inventory & Replenishment")
-    st.markdown('<div class="note-box"><b>Audience:</b> supply chain lead. Use this page for reorder, transfer, expiry control, and stock-balancing decisions.</div>', unsafe_allow_html=True)
+    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()
@@ -644,7 +644,7 @@ def inventory_page(df: pd.DataFrame):
 
 def promotion_page(df: pd.DataFrame):
     st.subheader("FRESHIE · Promotion Designer")
-    st.markdown('<div class="note-box"><b>Audience:</b> marketing lead. Use this page to test markdown depth, bundle logic, and campaign copy.</div>', unsafe_allow_html=True)
+    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, 1.2])
@@ -676,24 +676,27 @@ def promotion_page(df: pd.DataFrame):
         st.success(f"Run a {discount}% {campaign_type} for {promo_category} items in {expiry_target}.")
 
     with right:
-        promo_base = df.groupby(["expiry_bucket"])[["discount_pct", "waste_pct", "sell_through_pct"]].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)
-
-        fig2 = px.line(
-            promo_base,
-            x="expiry_bucket",
-            y="sell_through_pct",
-            markers=True,
-            title="Sell-through by expiry bucket",
-        )
-        st.plotly_chart(fig2, use_container_width=True)
+        promo_base = sub.groupby("expiry_bucket")[["discount_pct", "waste_pct", "sell_through_pct"]].mean().reset_index() if len(sub) else pd.DataFrame(columns=["expiry_bucket","discount_pct","waste_pct","sell_through_pct"])
+        if not promo_base.empty:
+            fig = px.bar(
+                promo_base,
+                x="expiry_bucket",
+                y=["discount_pct", "waste_pct"],
+                barmode="group",
+                title=f"Discount vs waste for {promo_category}",
+            )
+            st.plotly_chart(fig, use_container_width=True)
+
+            fig2 = px.line(
+                promo_base,
+                x="expiry_bucket",
+                y="sell_through_pct",
+                markers=True,
+                title=f"Sell-through for {promo_category}",
+            )
+            st.plotly_chart(fig2, use_container_width=True)
+        else:
+            st.info("No records match the current promotion settings.")
 
         st.markdown("### Recommended promotion copy")
         st.info(
@@ -714,11 +717,16 @@ def consumer_deals(df: pd.DataFrame):
     store_df = df[df["store_id"] == chosen_store].copy()
 
     c1, c2, c3 = st.columns(3)
-    max_budget = c1.slider("Budget (EUR)", 5, 60, 20)
+    budget_range = c1.slider("Budget range (EUR)", 1, 60, (1, 20))
     preferred_category = c2.selectbox("Preferred category", ["All"] + sorted(store_df["category"].unique()))
-    max_expiry = c3.slider("Maximum days until expiry", 1, 14, 5)
-
-    deals = store_df[store_df["days_until_expiry"] <= max_expiry].copy()
+    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)
@@ -757,11 +765,15 @@ def consumer_bundles(df: pd.DataFrame):
     store_df = df[df["store_id"] == chosen_store].copy()
 
     c1, c2, c3 = st.columns(3)
-    budget = c1.slider("Bundle budget (EUR)", 8, 80, 25)
+    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"])
-    max_expiry = c3.slider("Use items expiring within days", 1, 10, 5, key="bundle_exp")
+    expiry_range = c3.slider("Use items expiring within days", 1, 10, (1, 5), key="bundle_exp")
 
-    work = store_df[store_df["days_until_expiry"] <= max_expiry].copy()
+    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"],
@@ -774,7 +786,7 @@ def consumer_bundles(df: pd.DataFrame):
 
     picked, total, used_categories = [], 0.0, set()
     for _, row in work.iterrows():
-        if total + row["selling_price"] <= budget:
+        if total + row["selling_price"] <= budget_range[1]:
             if theme != "Budget saver" and row["category"] in used_categories:
                 continue
             picked.append(row)
@@ -808,8 +820,15 @@ def consumer_personal(df: pd.DataFrame):
     store_df = df[df["store_id"] == chosen_store].copy()
 
     favorite = st.selectbox("Favorite category", sorted(store_df["category"].unique()), key="cp_fav")
-    price_cap = st.slider("Max item price (EUR)", 1, 30, 10, key="cp_cap")
-    recs = store_df[(store_df["category"] == favorite) & (store_df["selling_price"] <= price_cap)].copy()
+    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)