Refine merchant interface workflow

app.py

@@ -696,6 +696,29 @@ def merchant_product_scores(metric: str) -> List[List[Any]]:
     return rows or [["No rows", "Try Refresh", metric, "-", 0.0, 0.0, 0.0]]
 
 
+def merchant_aspect_overview() -> List[List[Any]]:
+    rows = []
+    for aspect in ASPECTS:
+        neg_count, top_name, top_conf, top_reason = 0, "-", 0.0, "-"
+        for product in PRODUCTS:
+            try:
+                result = _predict_product(product["name"])
+            except Exception:
+                continue
+            d = result.get("aspect_details", {}).get(aspect, {})
+            if d.get("label") != "Negative":
+                continue
+            neg_count += 1
+            conf = _safe_float(d.get("confidence"))
+            if conf >= top_conf:
+                hits = _keyword_hits(product.get("review", ""), aspect)
+                top_name = product["name"]
+                top_conf = conf
+                top_reason = ", ".join(hits) if hits else _short_text(product.get("review") or product.get("features"), 90)
+        rows.append([aspect, neg_count, top_name, round(top_conf, 4), top_reason])
+    return rows
+
+
 def merchant_score_filter(aspect: str, prediction: str, category: str) -> List[List[Any]]:
     rows = []
     for product in PRODUCTS:
@@ -823,6 +846,62 @@ def export_risk_rows(rows):
     )
 
 
+def _read_uploaded_records(file_obj) -> List[Dict[str, Any]]:
+    if not file_obj:
+        return []
+    path = Path(getattr(file_obj, "name", file_obj))
+    try:
+        if path.suffix.lower() == ".json":
+            data = json.loads(path.read_text(encoding="utf-8"))
+            if isinstance(data, dict):
+                data = data.get("items") or data.get("data") or [data]
+            return data if isinstance(data, list) else []
+        with path.open("r", encoding="utf-8-sig", newline="") as fh:
+            return list(csv.DictReader(fh))
+    except Exception:
+        return []
+
+
+def download_new_product_template():
+    rows = [[
+        "Cotton Polyester Blend, Slim Fit, Graphic Print, Machine Wash",
+        "Clothing > Women > Tops > T-Shirts",
+        29.99,
+        4.1,
+        35,
+        "All",
+    ]]
+    return _write_csv_download(
+        "new_product_metadata_template.csv",
+        ["features", "categories", "price", "average_rating", "rating_number", "focus_aspect"],
+        rows,
+    )
+
+
+def batch_screen_new_products(file_obj) -> Tuple[str, List[List[Any]]]:
+    records = _read_uploaded_records(file_obj)
+    if not records:
+        return '<div class="note-card">Upload a CSV or JSON file first.</div>', []
+    rows = []
+    for i, data in enumerate(records, 1):
+        features = data.get("features") or data.get("features_text") or ""
+        categories = data.get("categories") or data.get("categories_text") or ""
+        price = _safe_float(data.get("price"), 0.0)
+        rating = _safe_float(data.get("average_rating") or data.get("rating"), 4.0)
+        count = _safe_float(data.get("rating_number") or data.get("rating_count"), 0.0)
+        focus = data.get("focus_aspect") or data.get("focus") or "All"
+        _, detail_rows = screen_new_product(features, categories, price, rating, count, focus)
+        high = [r[0] for r in detail_rows if r[1] == "High"]
+        rows.append([
+            f"Product {i}",
+            "High" if high else "Low",
+            focus,
+            len(high),
+            f"{_short_text(categories, 55)} | high-risk aspects: {', '.join(high) or 'None'}",
+        ])
+    return f'<div class="note-card"><b>Batch screening completed:</b> {len(rows)} products analyzed.</div>', rows
+
+
 def external_review_predict(review: str, features: str, categories: str, price: Any, rating: Any, count: Any, selected_aspect: str) -> Tuple[str, str, List[List[Any]]]:
     try:
         result = _predict_custom(review, features, categories, price, rating, count)
@@ -862,6 +941,52 @@ def export_external_rows(rows):
     )
 
 
+def download_external_review_template():
+    rows = [[
+        "The fabric is soft and the color looks good, but it runs small.",
+        "Cotton Blend, Slim Fit, Zipper Closure",
+        "Clothing > Women > Jackets",
+        39.99,
+        4.2,
+        312,
+        "All",
+    ]]
+    return _write_csv_download(
+        "external_review_template.csv",
+        ["review", "features", "categories", "price", "average_rating", "rating_number", "highlight_aspect"],
+        rows,
+    )
+
+
+def batch_external_review_predict(file_obj) -> Tuple[str, str, List[List[Any]]]:
+    records = _read_uploaded_records(file_obj)
+    if not records:
+        return '<div class="note-card">Upload a CSV or JSON file first.</div>', "", []
+    rows = []
+    for i, data in enumerate(records, 1):
+        review = data.get("review") or data.get("review_text") or ""
+        features = data.get("features") or data.get("features_text") or ""
+        categories = data.get("categories") or data.get("categories_text") or ""
+        price = _safe_float(data.get("price"), 0.0)
+        rating = _safe_float(data.get("average_rating") or data.get("rating"), 4.0)
+        count = _safe_float(data.get("rating_number") or data.get("rating_count"), 0.0)
+        aspect = data.get("highlight_aspect") or data.get("aspect") or "All"
+        overall_html, _, detail_rows = external_review_predict(review, features, categories, price, rating, count, aspect)
+        negative = [r[0] for r in detail_rows if r[1] == "Negative"]
+        rows.append([
+            f"Review {i}",
+            f"Negative: {', '.join(negative)}" if negative else "No major negative",
+            len(negative),
+            f"{_short_text(review, 70)} | {_short_text(categories, 45)}",
+        ])
+    return f'<div class="note-card"><b>Batch review prediction completed:</b> {len(rows)} reviews analyzed.</div>', "", rows
+
+
+def toggle_analysis_mode(mode: str):
+    single = mode.startswith("Single")
+    return gr.update(visible=single), gr.update(visible=not single)
+
+
 def _status_html() -> str:
     missing = []
     if not CHECKPOINT_PATH.exists() or CHECKPOINT_PATH.stat().st_size < 1024 * 1024:
@@ -932,10 +1057,11 @@ mark { background:#fde68a; color:#111827; border-radius:4px; padding:1px 3px; }
 .info-tip { position:relative; display:inline-flex; align-items:center; justify-content:center; width:24px; height:24px; border-radius:999px; border:1px solid #bfdbfe; background:#eff6ff; color:#1e40af; font-weight:800; cursor:help; }
 .info-tip .tip-content { display:none; position:absolute; right:0; top:30px; z-index:20; width:420px; max-width:80vw; background:white; border:1px solid var(--line); border-radius:8px; padding:10px; box-shadow:0 12px 30px rgba(15,23,42,.16); }
 .info-tip:hover .tip-content { display:block; }
-.negative-grid { display:grid; grid-template-columns:repeat(3, minmax(0, 1fr)); gap:10px; margin:8px 0 14px; }
+.negative-grid { display:grid; grid-template-columns:repeat(6, minmax(0, 1fr)); gap:10px; margin:8px 0 14px; }
 .negative-card { display:grid; grid-template-columns:64px 1fr; gap:10px; border:1px solid #fecaca; border-left:4px solid #ef4444; border-radius:8px; padding:10px; background:#fffafa; }
 .negative-card p { margin:5px 0 0; color:#334155; font-size:13px; }
 .mini-product-img { width:64px; height:76px; object-fit:cover; border-radius:6px; border:1px solid var(--line); background:#f8fafc; }
+@media (max-width:1100px) { .negative-grid { grid-template-columns:repeat(3, minmax(0, 1fr)); } }
 @media (max-width:860px) { .aspect-grid, .metric-grid, .product-layout, .decision-layout, .negative-grid { grid-template-columns:1fr; } .product-img { width:100%; height:180px; } }
 """
 
@@ -981,6 +1107,7 @@ def build_app() -> gr.Blocks:
                 gr.HTML('<div class="module-head"><h3>Product Score Monitor</h3><span class="info-tip">i<span class="tip-content">' + model_info_html() + '</span></span></div>')
                 gr.HTML('<div class="small-label">Most negative products across the six aspects</div>')
                 negative_spotlight = gr.HTML('<div class="note-card">Loading most negative products...</div>')
+                aspect_overview = gr.Dataframe(headers=["Aspect", "Negative Count", "Most Negative Product", "Confidence", "Reason"], value=merchant_aspect_overview(), datatype=["str", "number", "str", "number", "str"], interactive=False, label="Six-aspect negative overview")
                 with gr.Accordion("Score filters and export", open=True):
                     with gr.Row():
                         merchant_metric = gr.Dropdown(["Overall"] + ASPECTS, value="Overall", label="Aspect")
@@ -992,33 +1119,52 @@ def build_app() -> gr.Blocks:
                         export_scores = gr.Button("Export Score List")
                     merchant_scores_file = gr.File(label="Downloaded score CSV", interactive=False)
                 with gr.Accordion("New Product Metadata Risk Screening", open=False):
+                    risk_mode = gr.Radio(["Single product analysis", "Batch import analysis"], value="Single product analysis", label="Analysis mode")
                     with gr.Row():
                         with gr.Column(scale=1):
-                            new_import = gr.File(label="Import product metadata JSON/CSV")
-                            new_features = gr.Textbox("Cotton Polyester Blend, Slim Fit, Graphic Print, Machine Wash", label="New product features", lines=4)
-                            new_categories = gr.Textbox("Clothing > Women > Tops > T-Shirts", label="New product categories")
-                            new_price = gr.Number(29.99, label="Price")
-                            new_rating = gr.Number(4.1, label="Expected or early average rating")
-                            new_count = gr.Number(35, label="Expected or early rating count")
-                            new_focus = gr.Dropdown(["All"] + ASPECTS, value="All", label="Focus aspect")
-                            screen_btn = gr.Button("Predict Metadata Risk", variant="primary")
+                            with gr.Group(visible=True) as risk_single_group:
+                                new_features = gr.Textbox("Cotton Polyester Blend, Slim Fit, Graphic Print, Machine Wash", label="New product features", lines=4)
+                                new_categories = gr.Textbox("Clothing > Women > Tops > T-Shirts", label="New product categories")
+                                with gr.Row():
+                                    new_price = gr.Number(29.99, label="Price")
+                                    new_rating = gr.Number(4.1, label="Expected or early average rating")
+                                with gr.Row():
+                                    new_count = gr.Number(35, label="Expected or early rating count")
+                                    new_focus = gr.Dropdown(["All"] + ASPECTS, value="All", label="Focus aspect")
+                                screen_btn = gr.Button("Predict Metadata Risk", variant="primary")
+                            with gr.Group(visible=False) as risk_batch_group:
+                                new_import = gr.File(label="Import product metadata JSON/CSV")
+                                with gr.Row():
+                                    new_template = gr.Button("Download Import Template")
+                                    batch_screen_btn = gr.Button("Batch Analyze Metadata", variant="primary")
+                                new_template_file = gr.File(label="Template CSV", interactive=False)
                         with gr.Column(scale=1):
                             risk_summary = gr.HTML()
                             risk_table = gr.Dataframe(headers=["Aspect", "Risk Level", "Model Signal", "Confidence", "Reason"], value=[["Click Predict Metadata Risk", "", "", 0.0, ""]], datatype=["str", "str", "str", "number", "str"], interactive=False)
                             export_risk = gr.Button("Export Risk Result")
                             risk_file = gr.File(label="Downloaded risk CSV", interactive=False)
                 with gr.Accordion("External Review Prediction", open=False):
+                    ext_mode = gr.Radio(["Single product analysis", "Batch import analysis"], value="Single product analysis", label="Analysis mode")
                     with gr.Row():
                         with gr.Column(scale=1):
-                            ext_import = gr.File(label="Import review metadata JSON/CSV")
-                            external_review = gr.Textbox("The fabric is soft and the color looks good, but it runs small and the zipper feels weak.", label="External customer review", lines=5)
-                            ext_features = gr.Textbox("Cotton Blend, Slim Fit, Zipper Closure", label="Product features", lines=3)
-                            ext_categories = gr.Textbox("Clothing > Women > Jackets", label="Product categories")
-                            ext_price = gr.Number(39.99, label="Price")
-                            ext_rating = gr.Number(4.2, label="Average rating")
-                            ext_count = gr.Number(312, label="Rating count")
-                            ext_aspect = gr.Dropdown(["All"] + ASPECTS, value="All", label="Highlight aspect")
-                            external_btn = gr.Button("Analyze External Review", variant="primary")
+                            with gr.Group(visible=True) as ext_single_group:
+                                external_review = gr.Textbox("The fabric is soft and the color looks good, but it runs small and the zipper feels weak.", label="External customer review", lines=5)
+                                with gr.Row():
+                                    ext_features = gr.Textbox("Cotton Blend, Slim Fit, Zipper Closure", label="Product features", lines=3)
+                                    ext_categories = gr.Textbox("Clothing > Women > Jackets", label="Product categories", lines=3)
+                                with gr.Row():
+                                    ext_price = gr.Number(39.99, label="Price")
+                                    ext_rating = gr.Number(4.2, label="Average rating")
+                                with gr.Row():
+                                    ext_count = gr.Number(312, label="Rating count")
+                                    ext_aspect = gr.Dropdown(["All"] + ASPECTS, value="All", label="Highlight aspect")
+                                external_btn = gr.Button("Analyze External Review", variant="primary")
+                            with gr.Group(visible=False) as ext_batch_group:
+                                ext_import = gr.File(label="Import review metadata JSON/CSV")
+                                with gr.Row():
+                                    ext_template = gr.Button("Download Import Template")
+                                    batch_external_btn = gr.Button("Batch Analyze Reviews", variant="primary")
+                                ext_template_file = gr.File(label="Template CSV", interactive=False)
                         with gr.Column(scale=1):
                             ext_overall = gr.HTML()
                             ext_aspects = gr.HTML()
@@ -1030,11 +1176,15 @@ def build_app() -> gr.Blocks:
                 merchant_metric.change(merchant_score_filter, [merchant_metric, merchant_prediction, merchant_category], merchant_scores)
                 merchant_prediction.change(merchant_score_filter, [merchant_metric, merchant_prediction, merchant_category], merchant_scores)
                 merchant_category.change(merchant_score_filter, [merchant_metric, merchant_prediction, merchant_category], merchant_scores)
-                new_import.change(import_new_product_payload, new_import, [new_features, new_categories, new_price, new_rating, new_count, new_focus])
+                risk_mode.change(toggle_analysis_mode, risk_mode, [risk_single_group, risk_batch_group])
                 screen_btn.click(screen_new_product, [new_features, new_categories, new_price, new_rating, new_count, new_focus], [risk_summary, risk_table])
+                new_template.click(download_new_product_template, None, new_template_file)
+                batch_screen_btn.click(batch_screen_new_products, new_import, [risk_summary, risk_table])
                 export_risk.click(export_risk_rows, risk_table, risk_file)
-                ext_import.change(import_external_review_payload, ext_import, [external_review, ext_features, ext_categories, ext_price, ext_rating, ext_count, ext_aspect])
+                ext_mode.change(toggle_analysis_mode, ext_mode, [ext_single_group, ext_batch_group])
                 external_btn.click(external_review_predict, [external_review, ext_features, ext_categories, ext_price, ext_rating, ext_count, ext_aspect], [ext_overall, ext_aspects, ext_table])
+                ext_template.click(download_external_review_template, None, ext_template_file)
+                batch_external_btn.click(batch_external_review_predict, ext_import, [ext_overall, ext_aspects, ext_table])
                 export_ext.click(export_external_rows, ext_table, ext_file)
 
             with gr.Tab("Research Metrics"):