Update app with compact reports dashboard

.gitignore

@@ -0,0 +1,3 @@
+__pycache__/
+*.pyc
+.ipynb_checkpoints/

app.py

@@ -1,6 +1,7 @@
-"""Dual-interface Hugging Face Space for clothing aspect-level sentiment analysis."""
+"""Dual-interface Hugging Face Space for clothing aspect-level sentiment analysis."""
 from __future__ import annotations
 
+import csv
 import html
 import json
 import re
@@ -36,6 +37,7 @@ from src.inference import AspectPredictor
 
 ROOT = Path(__file__).resolve().parent
 REPORT_DIR = ROOT / "reports"
+DATA_DIR = ROOT / "data"
 CHECKPOINT_DIR = ROOT / "checkpoints" / "meta_acsa"
 CHECKPOINT_PATH = CHECKPOINT_DIR / "best.pt"
 META_ENCODER_PATH = ROOT / "data" / "meta_encoder.pkl"
@@ -60,16 +62,127 @@ ASPECT_KEYWORDS = {
 LABEL_BG = {"Positive": "#dcfce7", "Negative": "#fee2e2", "Not_Mentioned": "#f1f5f9", "Neutral": "#e0f2fe"}
 LABEL_FG = {"Positive": "#15803d", "Negative": "#b91c1c", "Not_Mentioned": "#475569", "Neutral": "#0369a1"}
 
-PRODUCTS = [
-    {"name": "Cotton Graphic Tee", "category": "Tops", "features": "100% Cotton, Slim Fit, Machine Wash Cold, Graphic Print", "categories": "Clothing > Men > T-Shirts > Graphic Tees", "price": 19.99, "average_rating": 4.2, "rating_number": 312, "tags": ["cotton", "casual", "print"], "review": "The size runs really small, I ordered an XL but it fits like a Medium. The fabric feels soft and the print looks great."},
-    {"name": "Stretch Yoga Leggings", "category": "Bottoms", "features": "Nylon Spandex Blend, High Waist, Four-Way Stretch, Moisture Wicking", "categories": "Clothing > Women > Activewear > Leggings", "price": 29.99, "average_rating": 4.5, "rating_number": 1280, "tags": ["stretch", "activewear", "high waist"], "review": "These leggings fit perfectly and the stretch is comfortable. The material is not see-through, but the seams started to loosen after washing."},
-    {"name": "Oversized Denim Jacket", "category": "Outerwear", "features": "Denim Cotton Blend, Oversized Fit, Button Front, Distressed Wash", "categories": "Clothing > Women > Jackets > Denim Jackets", "price": 58.00, "average_rating": 4.0, "rating_number": 447, "tags": ["denim", "oversized", "jacket"], "review": "The oversized style is cute and the color looks like the photo. It is heavier than expected and the buttons feel a little cheap."},
-    {"name": "Floral Summer Dress", "category": "Dresses", "features": "Rayon Blend, Floral Print, A-Line, Lightweight, V-Neck", "categories": "Clothing > Women > Dresses > Summer Dresses", "price": 36.50, "average_rating": 4.3, "rating_number": 864, "tags": ["floral", "summer", "dress"], "review": "The dress looks beautiful and the floral print is exactly as shown. The waist is a bit tight and the fabric wrinkles easily."},
-    {"name": "Fleece Pullover Hoodie", "category": "Tops", "features": "Cotton Polyester Fleece, Regular Fit, Kangaroo Pocket, Ribbed Cuffs", "categories": "Clothing > Unisex > Hoodies > Pullover Hoodies", "price": 42.99, "average_rating": 4.6, "rating_number": 2214, "tags": ["fleece", "hoodie", "warm"], "review": "Very warm and soft hoodie. The quality feels good for the price, though the sleeves are a little long for me."},
-    {"name": "Linen Button-Up Shirt", "category": "Tops", "features": "Linen Cotton Blend, Relaxed Fit, Button Front, Breathable Fabric", "categories": "Clothing > Men > Shirts > Button-Up Shirts", "price": 34.99, "average_rating": 3.9, "rating_number": 186, "tags": ["linen", "breathable", "shirt"], "review": "The shirt is breathable and stylish, but it wrinkles badly and the stitching near one button came loose."},
+FALLBACK_PRODUCTS = [
+    {"name": "Demo - Cotton Graphic Tee", "category": "Tops", "features": "100% Cotton, Slim Fit, Machine Wash Cold, Graphic Print", "categories": "Clothing > Men > T-Shirts > Graphic Tees", "price": 19.99, "average_rating": 4.2, "rating_number": 312, "tags": ["cotton", "casual", "print"], "source": "curated demo fallback", "review": "The size runs really small, I ordered an XL but it fits like a Medium. The fabric feels soft and the print looks great."},
+    {"name": "Demo - Stretch Yoga Leggings", "category": "Bottoms", "features": "Nylon Spandex Blend, High Waist, Four-Way Stretch, Moisture Wicking", "categories": "Clothing > Women > Activewear > Leggings", "price": 29.99, "average_rating": 4.5, "rating_number": 1280, "tags": ["stretch", "activewear", "high waist"], "source": "curated demo fallback", "review": "These leggings fit perfectly and the stretch is comfortable. The material is not see-through, but the seams started to loosen after washing."},
+    {"name": "Demo - Oversized Denim Jacket", "category": "Outerwear", "features": "Denim Cotton Blend, Oversized Fit, Button Front, Distressed Wash", "categories": "Clothing > Women > Jackets > Denim Jackets", "price": 58.00, "average_rating": 4.0, "rating_number": 447, "tags": ["denim", "oversized", "jacket"], "source": "curated demo fallback", "review": "The oversized style is cute and the color looks like the photo. It is heavier than expected and the buttons feel a little cheap."},
+    {"name": "Demo - Floral Summer Dress", "category": "Dresses", "features": "Rayon Blend, Floral Print, A-Line, Lightweight, V-Neck", "categories": "Clothing > Women > Dresses > Summer Dresses", "price": 36.50, "average_rating": 4.3, "rating_number": 864, "tags": ["floral", "summer", "dress"], "source": "curated demo fallback", "review": "The dress looks beautiful and the floral print is exactly as shown. The waist is a bit tight and the fabric wrinkles easily."},
+    {"name": "Demo - Fleece Pullover Hoodie", "category": "Tops", "features": "Cotton Polyester Fleece, Regular Fit, Kangaroo Pocket, Ribbed Cuffs", "categories": "Clothing > Unisex > Hoodies > Pullover Hoodies", "price": 42.99, "average_rating": 4.6, "rating_number": 2214, "tags": ["fleece", "hoodie", "warm"], "source": "curated demo fallback", "review": "Very warm and soft hoodie. The quality feels good for the price, though the sleeves are a little long for me."},
+    {"name": "Demo - Linen Button-Up Shirt", "category": "Tops", "features": "Linen Cotton Blend, Relaxed Fit, Button Front, Breathable Fabric", "categories": "Clothing > Men > Shirts > Button-Up Shirts", "price": 34.99, "average_rating": 3.9, "rating_number": 186, "tags": ["linen", "breathable", "shirt"], "source": "curated demo fallback", "review": "The shirt is breathable and stylish, but it wrinkles badly and the stitching near one button came loose."},
 ]
 
 
+TAG_CANDIDATES = [
+    "cotton", "polyester", "linen", "denim", "fleece", "leather", "stretch", "soft",
+    "breathable", "warm", "shirt", "dress", "jacket", "shorts", "sneakers",
+    "wallet", "jewelry", "casual", "formal", "activewear", "print", "floral",
+    "slim fit", "relaxed fit", "oversized", "high waist", "plus size",
+]
+
+
+def _safe_float(value, default=0.0):
+    try:
+        if value in (None, ""):
+            return default
+        return float(value)
+    except Exception:
+        return default
+
+
+def _parse_numeric_blob(blob):
+    text = str(blob or "")
+    out = {}
+    for key in ("price", "average_rating", "rating_number"):
+        match = re.search(rf"{key}\s*=\s*([-+]?\d+(?:\.\d+)?)", text)
+        if match:
+            out[key] = _safe_float(match.group(1))
+    return out
+
+
+def _tags_from_text(text):
+    low = str(text or "").lower()
+    tags = [tag for tag in TAG_CANDIDATES if tag in low]
+    return list(dict.fromkeys(tags))[:8]
+
+
+def _load_products_from_json():
+    path = DATA_DIR / "demo_products.json"
+    try:
+        if path.exists():
+            products = json.loads(path.read_text(encoding="utf-8"))
+            if isinstance(products, list) and products:
+                return [_coerce_product(p, i + 1) for i, p in enumerate(products)]
+    except Exception:
+        pass
+    return []
+
+
+def _load_products_from_explanation_csv():
+    path = REPORT_DIR / "explanation_attention_summary.csv"
+    if not path.exists():
+        return []
+    products = {}
+    try:
+        with path.open("r", encoding="utf-8", newline="") as fh:
+            for row in csv.DictReader(fh):
+                example_id = str(row.get("example") or "").strip()
+                if not example_id or example_id in products:
+                    continue
+                numeric = _parse_numeric_blob(row.get("numeric"))
+                category = str(row.get("category") or "Clothing").strip() or "Clothing"
+                features = str(row.get("features") or "").strip()
+                categories = str(row.get("categories") or category).strip()
+                review = str(row.get("text") or "").strip()
+                source = "real held-out explanation example"
+                item = {
+                    "name": f"Real Review {int(float(example_id)):02d} - {category}",
+                    "category": category,
+                    "features": features[:700],
+                    "categories": categories[:300],
+                    "price": numeric.get("price", 0.0),
+                    "average_rating": numeric.get("average_rating", _safe_float(row.get("rating"), 0.0)),
+                    "rating_number": numeric.get("rating_number", 0.0),
+                    "review": review,
+                    "source": source,
+                }
+                item["tags"] = _tags_from_text(" ".join([features, categories, review]))
+                products[example_id] = _coerce_product(item, int(float(example_id)))
+    except Exception:
+        return []
+    return list(products.values())
+
+
+def _coerce_product(item, idx=0):
+    features = str(item.get("features") or item.get("features_text") or "")
+    categories = str(item.get("categories") or item.get("categories_text") or "")
+    review = str(item.get("review") or item.get("review_text") or "")
+    category = str(item.get("category") or (categories.split(">")[-1].strip() if categories else "Clothing"))
+    name = str(item.get("name") or item.get("title") or f"Product Example {idx:02d}")
+    tags = item.get("tags") or _tags_from_text(" ".join([features, categories, review]))
+    return {
+        "name": name,
+        "category": category,
+        "features": features,
+        "categories": categories,
+        "price": _safe_float(item.get("price")),
+        "average_rating": _safe_float(item.get("average_rating")),
+        "rating_number": _safe_float(item.get("rating_number")),
+        "review": review,
+        "tags": list(tags) if isinstance(tags, (list, tuple)) else _tags_from_text(tags),
+        "source": str(item.get("source") or "demo product"),
+    }
+
+
+def _load_products():
+    products = _load_products_from_json() or _load_products_from_explanation_csv()
+    if products:
+        return products
+    return FALLBACK_PRODUCTS
+
+
+PRODUCTS = _load_products()
+
+
 def _safe_float(value: Any, default: float = 0.0) -> float:
     try:
         if value in (None, ""):
@@ -105,6 +218,27 @@ def _load_json(name: str) -> Dict[str, Any]:
         return {}
 
 
+def _load_csv_rows(name: str, columns: List[str], limit: int | None = None) -> List[List[Any]]:
+    path = REPORT_DIR / name
+    if not path.exists():
+        return []
+    rows = []
+    try:
+        with path.open("r", encoding="utf-8", newline="") as fh:
+            for row in csv.DictReader(fh):
+                rows.append([row.get(col, "") for col in columns])
+                if limit and len(rows) >= limit:
+                    break
+    except Exception:
+        return []
+    return rows
+
+
+def _report_image(name: str):
+    path = REPORT_DIR / name
+    return str(path) if path.exists() else None
+
+
 @lru_cache(maxsize=1)
 def _predictor() -> AspectPredictor:
     return AspectPredictor(checkpoint_dir=CHECKPOINT_DIR)
@@ -180,6 +314,64 @@ def _overall_html(result: Dict[str, Any]) -> str:
     return f'<div class="overall-card"><div class="small-label">Overall sentiment</div><div class="overall-main">{_chip(label)} <span class="conf">confidence {conf:.2f}</span></div>{"".join(bars)}</div>'
 
 
+def _join_aspects(items: List[str]) -> str:
+    if not items:
+        return "-"
+    if len(items) == 1:
+        return items[0]
+    if len(items) == 2:
+        return f"{items[0]} and {items[1]}"
+    return ", ".join(items[:-1]) + f", and {items[-1]}"
+
+
+def _recommendation_sentence(result: Dict[str, Any]) -> str:
+    details = result.get("aspect_details", {})
+    positive = []
+    negative = []
+    low_risk = []
+    for aspect in ASPECTS:
+        d = details.get(aspect, {})
+        label = d.get("label", result.get("aspects", {}).get(aspect, "Unknown"))
+        conf = _safe_float(d.get("confidence"))
+        if label == "Positive":
+            positive.append(aspect)
+        elif label == "Negative":
+            negative.append(aspect)
+        elif label in {"Neutral", "Not_Mentioned"} or conf < 0.60:
+            low_risk.append(aspect)
+
+    if positive and negative:
+        return (
+            f"This product is recommended because {_join_aspects(positive[:3])} "
+            f"{'is' if len(positive[:3]) == 1 else 'are'} positive, while "
+            f"{_join_aspects(negative[:2])} should be checked as potential risk."
+        )
+    if positive:
+        remaining = [a for a in ASPECTS if a not in positive]
+        return (
+            f"This product is recommended because {_join_aspects(positive[:3])} "
+            f"{'is' if len(positive[:3]) == 1 else 'are'} positive, while "
+            f"{_join_aspects((low_risk or remaining)[:3])} has low risk."
+        )
+    if negative:
+        return (
+            f"This product is not a strong recommendation because "
+            f"{_join_aspects(negative[:3])} shows negative sentiment risk."
+        )
+    overall = result.get("overall", {}).get("label", "Neutral")
+    return f"This product is a cautious recommendation because the overall signal is {overall} and no strong aspect risk dominates."
+
+
+def _recommendation_html(result: Dict[str, Any], product_name: str = "") -> str:
+    sentence = _recommendation_sentence(result)
+    title = f"Recommendation reason for {_esc(product_name)}" if product_name else "Recommendation reason"
+    return (
+        f'<div class="recommendation-card"><div class="small-label">{title}</div>'
+        f'<b>{_esc(sentence)}</b>'
+        f'<p class="muted">This explanation is generated from the live model output: overall sentiment, six aspect labels, and confidence scores.</p></div>'
+    )
+
+
 def _aspect_cards(result: Dict[str, Any], review: str, selected_aspect: str) -> str:
     details = result.get("aspect_details", {})
     sources = result.get("top_meta_source_by_aspect", {})
@@ -212,7 +404,7 @@ def consumer_product_view(product_name: str, selected_aspect: str) -> Tuple[str,
         result = _predict_product(product_name)
     except Exception:
         return _error_html(traceback.format_exc(limit=5)), "", "", []
-    detail = f'<div class="product-card"><h3>{_esc(product["name"])}</h3><p class="muted">{_esc(product["categories"])}</p><div class="meta-pills"><span>Price: ${_safe_float(product["price"]):.2f}</span><span>Rating: {_safe_float(product["average_rating"]):.1f}</span><span>Reviews: {int(_safe_float(product["rating_number"]))}</span></div><p><b>Metadata:</b> {_esc(product["features"])}</p>{_overall_html(result)}</div>'
+    detail = f'<div class="product-card"><h3>{_esc(product["name"])}</h3><p class="muted">{_esc(product["categories"])}</p><div class="meta-pills"><span>Source: {_esc(product.get("source", "demo"))}</span><span>Price: ${_safe_float(product["price"]):.2f}</span><span>Rating: {_safe_float(product["average_rating"]):.1f}</span><span>Reviews: {int(_safe_float(product["rating_number"]))}</span></div><p><b>Metadata:</b> {_esc(product["features"])}</p>{_recommendation_html(result, product["name"])}{_overall_html(result)}</div>'
     evidence = f'<h4>Key review evidence</h4>{_highlight_review(product["review"], selected_aspect)}'
     return detail, _aspect_cards(result, product["review"], selected_aspect), evidence, _aspect_rows(result, product["review"])
 
@@ -242,10 +434,14 @@ def filter_products(aspect: str, sentiment: str, category: str, tags: List[str],
         rows.append([product["name"], product["category"], label, round(conf, 4), product["average_rating"], product["price"], product["features"]])
         score = conf + 0.03 * _safe_float(product["average_rating"])
         if best is None or score > best[0]:
-            best = (score, product["name"], label, conf)
+            best = (score, product["name"], label, conf, result)
     summary = '<div class="note-card">No product matched the current filters.</div>'
     if best:
-        summary = f'<div class="note-card"><b>Best match:</b> {_esc(best[1])} - {_esc(aspect)} is {_esc(best[2])} with confidence {best[3]:.2f}.</div>'
+        summary = (
+            f'<div class="note-card"><b>Best match:</b> {_esc(best[1])} - '
+            f'{_esc(aspect)} is {_esc(best[2])} with confidence {best[3]:.2f}.'
+            f'{_recommendation_html(best[4], best[1])}</div>'
+        )
     return rows, summary
 
 
@@ -273,12 +469,21 @@ def research_cards_html() -> str:
 
 
 def overall_metric_rows() -> List[List[Any]]:
+    csv_rows = _load_csv_rows("overall_model_comparison.csv", ["model", "macro_f1", "accuracy"])
+    if csv_rows:
+        return [[r[0], _num(r[1]), _num(r[2])] for r in csv_rows]
     cmp = _payload()["eval"].get("overall_3class_comparison", {})
     pairs = [("TF-IDF + Logistic Regression", "Baseline_1_TFIDF_LogReg"), ("BERT Overall Classifier", "Baseline_2_BERT_overall_3class"), ("Proposed BERT + Metadata Fusion", "Proposed_BERT_Meta_Fusion__overall_head")]
     return [[name, _num(cmp.get(key, {}).get("macro_f1")), _num(cmp.get(key, {}).get("accuracy"))] for name, key in pairs]
 
 
 def aspect_metric_rows() -> List[List[Any]]:
+    csv_rows = _load_csv_rows(
+        "aspect_level_proposed_vs_no_meta.csv",
+        ["aspect", "acsa_no_meta_macro_f1", "proposed_macro_f1", "delta_macro_f1", "acsa_no_meta_accuracy", "proposed_accuracy", "delta_accuracy"],
+    )
+    if csv_rows:
+        return [[r[0], _num(r[1]), _num(r[2]), f"{_safe_float(r[3]):+.4f}", _num(r[4]), _num(r[5]), f"{_safe_float(r[6]):+.4f}"] for r in csv_rows]
     data = _payload()
     proposed = data["proposed"].get("per_aspect", {})
     no_meta = data["no_meta"].get("per_aspect", {})
@@ -298,6 +503,40 @@ def ablation_rows() -> List[List[Any]]:
     return [[labels.get(k, k), _num(ab.get(k, {}).get("mean_macro_f1")), _num(ab.get(k, {}).get("mean_accuracy"))] for k in labels if k in ab]
 
 
+def meta_source_rows() -> List[List[Any]]:
+    rows = _load_csv_rows(
+        "explanation_attention_meta_source_summary.csv",
+        ["aspect", "top_meta_source", "source_share", "mean_top_meta_weight", "mean_attention_focus"],
+    )
+    if not rows:
+        rows = _load_csv_rows(
+            "explanation_ig_meta_source_summary.csv",
+            ["aspect", "top_meta_source", "source_share", "mean_top_meta_weight", "mean_attention_focus"],
+        )
+    return [[r[0], r[1], _pct(r[2]), _num(r[3]), _num(r[4])] for r in rows]
+
+
+def report_asset_rows() -> List[List[Any]]:
+    groups = [
+        ("Evaluation", "overall_model_comparison.csv, aspect_level_proposed_vs_no_meta.csv"),
+        ("Confusion matrices", "confusion_matrix_proposed_overall_head.png and per-aspect PNGs"),
+        ("Ablation", "ablation_summary.json, ablation_A1/A2/A3.json"),
+        ("Visualization", "aspect_distribution.png and category_aspect_*_heatmap.png"),
+        ("Explanation", "explanation_*_summary.csv and explanation_*_meta_source_summary.csv"),
+    ]
+    return [[name, assets] for name, assets in groups]
+
+
+def refresh_research_outputs():
+    return (
+        research_cards_html(),
+        overall_metric_rows(),
+        aspect_metric_rows(),
+        ablation_rows(),
+        meta_source_rows(),
+    )
+
+
 def merchant_product_scores(metric: str) -> List[List[Any]]:
     rows = []
     for product in PRODUCTS:
@@ -366,20 +605,25 @@ def _status_html() -> str:
         missing.append("data/meta_encoder.pkl")
     if missing:
         return '<div class="status bad"><b>Model artifacts missing:</b> ' + _esc(", ".join(missing)) + '</div>'
-    return '<div class="status ok"><b>Model ready.</b> 10W0715 checkpoint and metadata encoder are available.</div>'
+    return '<div class="status ok"><b>Model ready.</b> 10W0716 checkpoint and metadata encoder are available.</div>'
 
 
 CSS = """
-:root { --accent:#f97316; --ink:#0f172a; --muted:#475569; --line:#dbe3ef; }
+:root { --accent:#f97316; --ink:#0f172a; --muted:#475569; --line:#dbe3ef; --panel:#ffffff; }
 .gradio-container { max-width:1240px !important; margin:auto !important; color:var(--ink); }
-#hero { border:1px solid var(--line); background:#f8fbff; border-radius:8px; padding:22px 28px; margin:10px 0 22px; }
-#hero h1 { margin:0 0 8px; font-size:30px; line-height:1.15; }
-#hero p { margin:0; color:#334155; }
+#hero { border:1px solid #bfdbfe; background:#eff6ff; border-left:6px solid var(--accent); border-radius:8px; padding:18px 22px; margin:8px 0 14px; box-shadow:0 1px 6px rgba(15,23,42,.06); }
+#hero .eyebrow { margin:0 0 7px; color:#9a3412; font-size:13px; font-weight:800; letter-spacing:.04em; text-transform:uppercase; }
+#hero h1 { margin:0 0 8px; font-size:28px; line-height:1.15; color:#0f172a; font-weight:800; }
+#hero p { margin:0; color:#1e3a8a; max-width:920px; }
+#hero .hero-pills { display:flex; flex-wrap:wrap; gap:8px; margin-top:12px; }
+#hero .hero-pills span { color:#1e293b; background:#ffffff; border:1px solid #bfdbfe; border-radius:999px; padding:5px 10px; font-size:13px; font-weight:600; }
 button.primary, .gradio-button.primary { background:var(--accent) !important; border-color:var(--accent) !important; color:white !important; font-weight:700 !important; }
-.status { border-radius:8px; padding:12px 14px; margin:8px 0 18px; border:1px solid var(--line); }
+.status { border-radius:8px; padding:10px 13px; margin:6px 0 14px; border:1px solid var(--line); }
 .status.ok { background:#ecfdf5; border-color:#86efac; color:#065f46; }
 .status.bad { background:#fff1f2; border-color:#fda4af; color:#991b1b; }
 .product-card, .overall-card, .note-card { border:1px solid var(--line); border-radius:8px; padding:16px; background:white; }
+.recommendation-card { border:1px solid #fed7aa; border-left:5px solid var(--accent); background:#fff7ed; border-radius:8px; padding:13px 14px; margin:12px 0; }
+.recommendation-card p { margin:7px 0 0; }
 .muted { color:var(--muted); }
 .meta-pills { display:flex; flex-wrap:wrap; gap:8px; margin:10px 0; }
 .meta-pills span { background:#f1f5f9; border:1px solid #e2e8f0; border-radius:999px; padding:5px 9px; font-size:13px; }
@@ -389,8 +633,8 @@ button.primary, .gradio-button.primary { background:var(--accent) !important; bo
 .prob-row { display:grid; grid-template-columns:82px 1fr 44px; gap:8px; align-items:center; font-size:13px; margin:6px 0; }
 .bar { height:8px; background:#e2e8f0; border-radius:999px; overflow:hidden; }
 .bar i { display:block; height:100%; background:var(--accent); }
-.aspect-grid { display:grid; grid-template-columns:repeat(3, minmax(0, 1fr)); gap:12px; }
-.aspect-card { border:1px solid var(--line); border-top:4px solid #64748b; border-radius:8px; padding:13px; background:white; min-height:165px; }
+.aspect-grid { display:grid; grid-template-columns:repeat(3, minmax(0, 1fr)); gap:10px; }
+.aspect-card { border:1px solid var(--line); border-top:4px solid #64748b; border-radius:8px; padding:12px; background:white; min-height:145px; }
 .aspect-card.focus { border-top-color:var(--accent); box-shadow:0 2px 10px rgba(15,23,42,.08); }
 .aspect-card.dim { opacity:.66; }
 .aspect-head { display:flex; justify-content:space-between; align-items:center; margin-bottom:8px; }
@@ -400,8 +644,8 @@ button.primary, .gradio-button.primary { background:var(--accent) !important; bo
 .evidence-token { color:#1d4ed8; background:#eef2ff; border:1px solid #bfdbfe; border-radius:999px; padding:3px 8px; font-size:12px; }
 .review-box { border:1px dashed #cbd5e1; background:#f8fafc; border-radius:8px; padding:14px; line-height:1.6; }
 mark { background:#fde68a; color:#111827; border-radius:4px; padding:1px 3px; }
-.metric-grid { display:grid; grid-template-columns:repeat(3, 1fr); gap:14px; margin:8px 0 18px; }
-.metric { border:1px solid var(--line); border-radius:8px; padding:16px; background:white; }
+.metric-grid { display:grid; grid-template-columns:repeat(3, 1fr); gap:12px; margin:8px 0 12px; }
+.metric { border:1px solid var(--line); border-radius:8px; padding:14px; background:white; }
 .metric span { display:block; color:#334155; font-size:13px; }
 .metric b { display:block; font-size:28px; margin:6px 0; }
 .metric small { color:#475569; }
@@ -409,6 +653,7 @@ mark { background:#fde68a; color:#111827; border-radius:4px; padding:1px 3px; }
 .kv-table { width:100%; border-collapse:collapse; }
 .kv-table td { border-bottom:1px solid #e2e8f0; padding:9px 12px; }
 .kv-table td:first-child { width:220px; color:#334155; font-weight:700; background:#f8fafc; }
+.compact-note { color:#475569; font-size:13px; margin:4px 0 10px; }
 @media (max-width:860px) { .aspect-grid, .metric-grid { grid-template-columns:1fr; } }
 """
 
@@ -417,7 +662,7 @@ def build_app() -> gr.Blocks:
     categories = ["All"] + sorted({p["category"] for p in PRODUCTS})
     tags = sorted({tag for p in PRODUCTS for tag in p.get("tags", [])})
     with gr.Blocks(css=CSS, title="Clothing Sentiment Analysis") as demo:
-        gr.HTML('<div id="hero"><h1>Aspect-Level Sentiment Analysis for Clothing Reviews</h1><p>BERT + metadata cross-attention fusion for consumer decision support and merchant diagnostics.</p></div>')
+        gr.HTML('<div id="hero"><div class="eyebrow">BERT + Metadata Cross-Attention</div><h1>Clothing Review Sentiment Intelligence App</h1><p>Explore overall sentiment, six aspect-level opinions, metadata-driven risks, and updated 10W experiment reports in a compact customer decision-support prototype.</p><div class="hero-pills"><span>Consumer decision support</span><span>Merchant diagnostics</span><span>Research dashboard</span></div></div>')
         gr.HTML(_status_html())
         with gr.Tabs():
             with gr.Tab("Consumer Interface"):
@@ -430,17 +675,18 @@ def build_app() -> gr.Blocks:
                     with gr.Column(scale=6):
                         aspect_html = gr.HTML()
                 consumer_table = gr.Dataframe(headers=["Aspect", "Prediction", "Confidence", "Key review evidence"], datatype=["str", "str", "number", "str"], label="Aspect-level result table", interactive=False)
-                gr.Markdown("### Product Finder")
-                with gr.Row():
-                    filter_aspect = gr.Dropdown(["Overall"] + ASPECTS, value="Overall", label="Target metric")
-                    filter_sentiment = gr.Radio(["Any", "Positive", "Negative", "Not_Mentioned", "Neutral"], value="Any", label="Preferred prediction")
-                    filter_category = gr.Dropdown(categories, value="All", label="Category")
-                with gr.Row():
-                    filter_tags = gr.CheckboxGroup(tags, label="Required metadata tags")
-                    min_rating = gr.Slider(3.0, 5.0, value=4.0, step=0.1, label="Minimum product rating")
-                filter_btn = gr.Button("Filter Products", variant="primary")
-                filter_summary = gr.HTML()
-                filter_table = gr.Dataframe(headers=["Product", "Category", "Prediction", "Confidence", "Rating", "Price", "Metadata"], datatype=["str", "str", "str", "number", "number", "number", "str"], interactive=False, label="Filtered product candidates")
+                with gr.Accordion("Product Finder filters", open=False):
+                    gr.HTML('<div class="compact-note">Optional: filter products by aspect sentiment, category, tags, and minimum rating.</div>')
+                    with gr.Row():
+                        filter_aspect = gr.Dropdown(["Overall"] + ASPECTS, value="Overall", label="Target metric")
+                        filter_sentiment = gr.Radio(["Any", "Positive", "Negative", "Not_Mentioned", "Neutral"], value="Any", label="Preferred prediction")
+                        filter_category = gr.Dropdown(categories, value="All", label="Category")
+                    with gr.Row():
+                        filter_tags = gr.CheckboxGroup(tags, label="Required metadata tags")
+                        min_rating = gr.Slider(3.0, 5.0, value=4.0, step=0.1, label="Minimum product rating")
+                    filter_btn = gr.Button("Filter Products", variant="primary")
+                    filter_summary = gr.HTML()
+                    filter_table = gr.Dataframe(headers=["Product", "Category", "Prediction", "Confidence", "Rating", "Price", "Metadata"], datatype=["str", "str", "str", "number", "number", "number", "str"], interactive=False, label="Filtered product candidates")
                 product_select.change(consumer_product_view, [product_select, consumer_aspect], [product_detail, aspect_html, evidence_html, consumer_table])
                 consumer_aspect.change(consumer_product_view, [product_select, consumer_aspect], [product_detail, aspect_html, evidence_html, consumer_table])
                 filter_btn.click(filter_products, [filter_aspect, filter_sentiment, filter_category, filter_tags, min_rating], [filter_table, filter_summary])
@@ -455,48 +701,57 @@ def build_app() -> gr.Blocks:
                         merchant_metric = gr.Dropdown(["Overall"] + ASPECTS, value="Overall", label="Choose overall or aspect")
                         merchant_scores = gr.Dataframe(headers=["Product", "Category", "Metric", "Prediction", "Confidence", "Price", "Rating"], value=[["Click Refresh Product Scores", "", "", "", 0.0, 0.0, 0.0]], datatype=["str", "str", "str", "str", "number", "number", "number"], interactive=False)
                         refresh_scores = gr.Button("Refresh Product Scores", variant="primary")
-                gr.Markdown("### New Product Metadata Risk Screening")
-                with gr.Row():
-                    new_features = gr.Textbox("Cotton Polyester Blend, Slim Fit, Graphic Print, Machine Wash", label="New product features")
-                    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")
-                    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")
-                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)
-                gr.Markdown("### External Review Prediction")
-                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=4)
-                with gr.Row():
-                    ext_features = gr.Textbox("Cotton Blend, Slim Fit, Zipper Closure", label="Product features")
-                    ext_categories = gr.Textbox("Clothing > Women > Jackets", label="Product categories")
-                with gr.Row():
-                    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")
-                ext_overall = gr.HTML()
-                ext_aspects = gr.HTML()
-                ext_table = gr.Dataframe(headers=["Aspect", "Prediction", "Confidence", "Key review evidence"], value=[["Click Analyze External Review", "", 0.0, ""]], datatype=["str", "str", "number", "str"], interactive=False)
+                with gr.Accordion("New Product Metadata Risk Screening", open=False):
+                    with gr.Row():
+                        new_features = gr.Textbox("Cotton Polyester Blend, Slim Fit, Graphic Print, Machine Wash", label="New product features")
+                        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")
+                        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")
+                    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)
+                with gr.Accordion("External Review Prediction", open=False):
+                    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=4)
+                    with gr.Row():
+                        ext_features = gr.Textbox("Cotton Blend, Slim Fit, Zipper Closure", label="Product features")
+                        ext_categories = gr.Textbox("Clothing > Women > Jackets", label="Product categories")
+                    with gr.Row():
+                        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")
+                    ext_overall = gr.HTML()
+                    ext_aspects = gr.HTML()
+                    ext_table = gr.Dataframe(headers=["Aspect", "Prediction", "Confidence", "Key review evidence"], value=[["Click Analyze External Review", "", 0.0, ""]], datatype=["str", "str", "number", "str"], interactive=False)
                 refresh_scores.click(merchant_product_scores, merchant_metric, merchant_scores)
                 merchant_metric.change(merchant_product_scores, merchant_metric, merchant_scores)
                 screen_btn.click(screen_new_product, [new_features, new_categories, new_price, new_rating, new_count, new_focus], [risk_summary, risk_table])
                 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])
 
             with gr.Tab("Research Metrics"):
-                gr.Markdown("Metrics are loaded from the bundled 10W0715 experiment reports.")
+                gr.Markdown("Metrics are loaded from the updated 10W experiment reports. Tables use CSV outputs when available, with JSON fallback.")
                 research_cards = gr.HTML(research_cards_html())
-                gr.Markdown("### 1. Overall Sentiment Metrics")
-                overall_table = gr.Dataframe(headers=["Model", "Macro-F1", "Accuracy"], value=overall_metric_rows(), interactive=False)
-                gr.Markdown("### 2. Aspect-Level Overall Comparison")
-                aspect_table = gr.Dataframe(headers=["Aspect", "No-meta F1", "Proposed F1", "F1 Delta", "No-meta Acc", "Proposed Acc", "Acc Delta"], value=aspect_metric_rows(), interactive=False)
-                gr.Markdown("### 3. Ablation Comparison")
-                ablation_table = gr.Dataframe(headers=["Variant", "Mean Macro-F1", "Mean Accuracy"], value=ablation_rows(), interactive=False)
+                with gr.Accordion("Performance comparison", open=True):
+                    overall_table = gr.Dataframe(headers=["Model", "Macro-F1", "Accuracy"], value=overall_metric_rows(), interactive=False)
+                    aspect_table = gr.Dataframe(headers=["Aspect", "No-meta F1", "Proposed F1", "F1 Delta", "No-meta Acc", "Proposed Acc", "Acc Delta"], value=aspect_metric_rows(), interactive=False)
+                with gr.Accordion("Ablation and metadata source summary", open=False):
+                    ablation_table = gr.Dataframe(headers=["Variant", "Mean Macro-F1", "Mean Accuracy"], value=ablation_rows(), interactive=False)
+                    meta_source_table = gr.Dataframe(headers=["Aspect", "Top Metadata Source", "Source Share", "Mean Weight", "Mean Focus"], value=meta_source_rows(), interactive=False)
+                with gr.Accordion("Figures from updated reports", open=False):
+                    with gr.Row():
+                        gr.Image(value=_report_image("aspect_distribution.png"), label="Aspect sentiment distribution", interactive=False)
+                        gr.Image(value=_report_image("category_aspect_negative_heatmap.png"), label="Negative share by category x aspect", interactive=False)
+                    with gr.Row():
+                        gr.Image(value=_report_image("category_aspect_positive_heatmap.png"), label="Positive share by category x aspect", interactive=False)
+                        gr.Image(value=_report_image("confusion_matrix_proposed_overall_head.png"), label="Proposed overall confusion matrix", interactive=False)
+                with gr.Accordion("Report file groups", open=False):
+                    gr.Dataframe(headers=["Group", "Files"], value=report_asset_rows(), interactive=False)
                 refresh_research = gr.Button("Refresh Research Metrics", variant="primary")
-                refresh_research.click(lambda: (research_cards_html(), overall_metric_rows(), aspect_metric_rows(), ablation_rows()), outputs=[research_cards, overall_table, aspect_table, ablation_table])
+                refresh_research.click(refresh_research_outputs, outputs=[research_cards, overall_table, aspect_table, ablation_table, meta_source_table])
         demo.load(consumer_product_view, [product_select, consumer_aspect], [product_detail, aspect_html, evidence_html, consumer_table])
         demo.load(merchant_product_scores, merchant_metric, merchant_scores)
         demo.load(filter_products, [filter_aspect, filter_sentiment, filter_category, filter_tags, min_rating], [filter_table, filter_summary])
@@ -506,5 +761,5 @@ def build_app() -> gr.Blocks:
 demo = build_app()
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(ssr_mode=False)
 

data/demo_products.json

@@ -0,0 +1,165 @@
+[
+  {
+    "name": "Real Review 01 - Strands",
+    "category": "Strands",
+    "features": "TRENDY NECKLACES: Delicate layered chain necklace features mixed faceted beads, delicate stone accents, lovely flowers and heart embellished with woven mixed multi-colored charms. Hand crafted from polished gold-tone metal, glass and plastic LIGHTWEIGHT NECKLACES : Effortless and lightweight necklaces easy to put on and take off. Necklace has an adjustable lobster clasp closure MEASUREMENTS : 16in length with adjusta",
+    "categories": "Clothing, Shoes & Jewelry > Women > Jewelry > Necklaces > Strands",
+    "price": 35.99,
+    "average_rating": 4.4,
+    "rating_number": 4695.0,
+    "review": "Bad quality control I ordered despite all the bad reviews. Well that didn’t work as it was just pack in a baggie as like most of the reviews state. Then the blue and white flowers with charms were all attached backwards. I am in the process of returning it. I will not be asking for an exchange as I do not wish to keep returning this item until I get a good one. So very sad as this could have been a beautiful necklace. Plus with as expensive as this is you would think it would have better packaging. I have bought for much cheaper with nice packaging. Unfortunately I will not be buying any more of this brand as it had put a bad taste in my mouth. Do better!",
+    "source": "real held-out explanation example",
+    "tags": [
+      "jewelry"
+    ]
+  },
+  {
+    "name": "Real Review 02 - Rash Guard Shirts",
+    "category": "Rash Guard Shirts",
+    "features": "100% Polyester Pull On closure Machine Wash The fabric rating UPF 50+ protects your skin from the harmful UVA/UVB rays,keeping you cool while outdoors in the direct sunlight.Great for swimming as a cover shirt,light shirt for hiking. Technical fabric wicks moisture away from your skin and dries quickly for extra comfort, keep you cool and dry on running or workout. Raglan sleeves and no tag collar/flat-seam construct",
+    "categories": "Clothing, Shoes & Jewelry > Women > Clothing > Swimsuits & Cover Ups > Rash Guard Shirts",
+    "price": 19.99,
+    "average_rating": 4.4,
+    "rating_number": 2481.0,
+    "review": "Very small for size and returned Too tight for size and Iborderwd extra large.",
+    "source": "real held-out explanation example",
+    "tags": [
+      "polyester",
+      "shirt",
+      "jewelry"
+    ]
+  },
+  {
+    "name": "Real Review 03 - Identification",
+    "category": "Identification",
+    "features": "Medical Bracelets Size: Width 0.55 Inches(14mm), Length 6-8.2 inches(150-215mm). Material: 316L Stainless Steel O Chain, Never Rust, Sturdy and Durable, High Quality. PEOPLE WITH THE FOLLOWING CONDITIONS SHOULD WEAR A MEDICAL ID JEWELRY: Alzheimer's, Autism/Special Needs Children, Blood Disorders, Blood Thinners, Diabetes, Dementia, Drug Allergies (i.e. Penicillin, Morphine, Sulfa), Emphysema/Breathing Disorders, Epi",
+    "categories": "Clothing, Shoes & Jewelry > Women > Jewelry > Bracelets > Identification",
+    "price": 13.99,
+    "average_rating": 4.1,
+    "rating_number": 2743.0,
+    "review": "Not great Not comfortable to wear. The information portion of the bracelet has very sharp edges. Adjusting the length requires a tool. You can't push down the locking part by hand. So broken clasp after long wait. They did issue a refund without requiring me to return the item",
+    "source": "real held-out explanation example",
+    "tags": [
+      "jewelry"
+    ]
+  },
+  {
+    "name": "Real Review 04 - Wallets",
+    "category": "Wallets",
+    "features": "Aluminum lining Wet Wipe Clean High grade plastic wallet with aluminum shell, customed design. Integrated RFID repellant technology protects you from Credit-Card scanning thieves. Seven secure slots fold out accordion style and hold up to 9 cards. If there is any issue with quality of wallet, please contact us and we will replace it. Standard Size: 2.75\" x 4.25\" x 0.75\"",
+    "categories": "Clothing, Shoes & Jewelry > Men > Accessories > Wallets, Card Cases & Money Organizers > Wallets",
+    "price": 8.99,
+    "average_rating": 4.1,
+    "rating_number": 793.0,
+    "review": "Two Stars loved it until the pin fell out after having it only a month",
+    "source": "real held-out explanation example",
+    "tags": [
+      "wallet",
+      "jewelry"
+    ]
+  },
+  {
+    "name": "Real Review 05 - Cocktail",
+    "category": "Cocktail",
+    "features": "Fabric type: Great Elasticity, 69% Cotton, 26% Nylon, 5% Spandex. Hand Wash Only, Low Temperature for Ironing. Hidden back zipper closure Sweetheart neckline, above the knee, hidden back zipper, sexy v-back, cold shoulder, short sleeves A-line dress. A full skater skirt creates a flattering fit and flare silhouette make you look chic yet elegant. This semi-formal party dress suitable for cocktail party, wedding guest",
+    "categories": "Clothing, Shoes & Jewelry > Women > Clothing > Dresses > Cocktail",
+    "price": 0.0,
+    "average_rating": 4.2,
+    "rating_number": 4399.0,
+    "review": "Okay Not the best quality, pretty skimpy",
+    "source": "real held-out explanation example",
+    "tags": [
+      "cotton",
+      "dress",
+      "jewelry",
+      "formal"
+    ]
+  },
+  {
+    "name": "Real Review 06 - Aprons",
+    "category": "Aprons",
+    "features": "65% Polyester, 35% Cotton Imported waist tie closure Durable Cooking Apron - Chef Works aprons are pre-tested for strength and durability. Use as a waitress apron in front of house or chef apron in back of house. Comfortable Fit - Waist tie offers the ability to fit to various body sizes with comfort and versatility. This is a unisex apron women and men can both wear. Full-Body Coverage - 34-inch height by 27-inch wi",
+    "categories": "Clothing, Shoes & Jewelry > Uniforms, Work & Safety > Clothing > Food Service > Aprons",
+    "price": 16.99,
+    "average_rating": 4.6,
+    "rating_number": 2464.0,
+    "review": "Okay don't have very much to say about this on ipad version, other than its okay",
+    "source": "real held-out explanation example",
+    "tags": [
+      "cotton",
+      "polyester",
+      "jewelry"
+    ]
+  },
+  {
+    "name": "Real Review 07 - Shoe Horns & Boot Jacks",
+    "category": "Shoe Horns & Boot Jacks",
+    "features": "",
+    "categories": "Clothing, Shoes & Jewelry > Shoe, Jewelry & Watch Accessories > Shoe Care & Accessories > Shoe Horns & Boot Jacks",
+    "price": 0.0,
+    "average_rating": 3.9,
+    "rating_number": 29.0,
+    "review": "At the current price point, this is a decent shoe horn I was originally going to rate this 3 stars. However, for the current price of $7.99, I think this is a decent shoe horn. It is metal with a silicone covering on the upper part. It is also perfect for young children or adults who do not mind bending down to use a shoe horn. It is certainly better than plastic. This is packaged with a big cheap cinch-tie bag. I have no idea why this is included as probably 30 of these shoe horns could fit in that bag. This horn has the potential to bend because it is not super sturdy. I have a similar one with a wooden handle and that one is all bent just from use. My recommendation if you want a great shoe horn that is sturdier and can be used standing up, is to get this [[ASIN:B01M154SED 21\" heavy duty one]] for the current price of $20.99 plus 5% off. I own one of these and had I purchased that one first, I would have never ordered any other. Do note, however, that it is very large and comes up to one's knees but it is fantastic. In summary, this one is decent and functional at the low price point but for something more sturdy and that will last longer, look into the other one I recommended.",
+    "source": "real held-out explanation example",
+    "tags": [
+      "jewelry"
+    ]
+  },
+  {
+    "name": "Real Review 08 - T-Shirts",
+    "category": "T-Shirts",
+    "features": "100% Polyester Imported UA Base 2.0 Active Baselayer is lightweight, flexible & breathable for high activity performance in cool conditions UA Scent Control Technology to keep you undetected in all pursuits Soft, brushed grid interior traps air against your skin, closely managing your body's microclimate to keep you warm & comfortable Material wicks sweat & dries really fast 4-way stretch construction moves better in",
+    "categories": "Clothing, Shoes & Jewelry > Men > Clothing > Active > Active Shirts & Tees > T-Shirts",
+    "price": 49.99,
+    "average_rating": 4.8,
+    "rating_number": 911.0,
+    "review": "Good Base Layer This is a comfortable shirt. Ideal as a base layer it is made of soft, stretchy material. It is not too tight. It conforms to the body and keeps you warm and dry. Great for cold weather.",
+    "source": "real held-out explanation example",
+    "tags": [
+      "polyester",
+      "stretch",
+      "soft",
+      "breathable",
+      "warm",
+      "shirt",
+      "jewelry"
+    ]
+  },
+  {
+    "name": "Real Review 09 - Fashion Sneakers",
+    "category": "Fashion Sneakers",
+    "features": "Imported Synthetic sole TPR Outsole, Stretch Cotton Laces Closure, Removable Twill Covered EVA, Vegan YOUR NEW FAVORITE WOMEN'S SNEAKERS: Enjoy walking around with the Vegan Pismo Casual Sneakers with stretch laces, metal eyelets, and eco-friendly canvas uppers available in different colors to match your daily fashion mood. VIONIC WOMEN SHOES: Vionic brings together style and science, combining innovative biomechanic",
+    "categories": "Clothing, Shoes & Jewelry > Women > Shoes > Fashion Sneakers",
+    "price": 49.0,
+    "average_rating": 4.6,
+    "rating_number": 4004.0,
+    "review": "problem for people with a high instep i love the softness and flexibility of this shoe. i have a high instep, and these shoes have elastic laces. i ordered a wide (i do not have a wide foot) in order to not have the elastic laces cut off my circulation. you can cut the laces out and put in regular laces. shoes would look cute with pink, yellow, or orange laces. red even! feels good in my arch, and pretty comfy overall.",
+    "source": "real held-out explanation example",
+    "tags": [
+      "cotton",
+      "stretch",
+      "soft",
+      "sneakers",
+      "jewelry",
+      "casual"
+    ]
+  },
+  {
+    "name": "Real Review 10 - Board Shorts",
+    "category": "Board Shorts",
+    "features": "88% Polyester, 12% Spandex Imported Drawstring closure Machine Wash Kanu comfort tech qucik dry UPF 50+ Stretch Microfiber Cargo Pocket for storage.",
+    "categories": "Clothing, Shoes & Jewelry > Women > Clothing > Swimsuits & Cover Ups > Board Shorts",
+    "price": 0.0,
+    "average_rating": 4.1,
+    "rating_number": 4126.0,
+    "review": "Cool, relaxed comfort The Kanu Surf Women's plus size Marina solid stretch Boardshort is perfect for lounging around the house or walking the dog. It is exceptionally cool during the summer and has that extra stretch for comfort. Also, like the elastic waistband in the back.",
+    "source": "real held-out explanation example",
+    "tags": [
+      "polyester",
+      "stretch",
+      "shorts",
+      "jewelry",
+      "plus size"
+    ]
+  }
+]

reports/aspect_level_proposed_vs_no_meta.csv

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reports/explanation_attention_meta_source_summary.csv

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reports/explanation_ig_meta_source_summary.csv

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reports/overall_model_comparison.csv

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reports_for_frontend/01_evaluation/aspect_level_proposed_vs_no_meta.csv

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reports_for_frontend/01_evaluation/evaluation_comparison.json

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+}

reports_for_frontend/01_evaluation/overall_model_comparison.csv

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+model,macro_f1,accuracy
+Baseline_2_BERT_overall_3class,0.7284109895875363,0.8962
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+Baseline_3_BERT_ACSA_no_meta__aggregated_to_overall,0.503449963221516,0.6606

reports_for_frontend/01_evaluation/per_aspect_acsa_no_meta.json

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+  "description": "Replace Cross-Attention fusion with [text;meta] concat + Linear; both meta sources kept."
+}

reports_for_frontend/03_ablation/ablation_summary.json

@@ -0,0 +1,122 @@
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+}

reports_for_frontend/04_visualization/category_aspect_aggregation.csv

@@ -0,0 +1,61 @@
+category,aspect,n_total,n_mentioned,positive_share,negative_share
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reports_for_frontend/05_explanation/explanation_attention_meta_source_summary.csv

@@ -0,0 +1,7 @@
+aspect,top_meta_source,n_rows,mean_top_meta_weight,mean_attention_focus,mean_attention_margin,mean_attention_entropy,aspect_total,source_share
+APPEARANCE,features,50,0.5535691463947296,0.3152554518269078,0.16856914888540328,0.6847445481730923,50,1.0
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reports_for_frontend/05_explanation/explanation_ig_meta_source_summary.csv

@@ -0,0 +1,7 @@
+aspect,top_meta_source,n_rows,mean_top_meta_weight,mean_attention_focus,mean_attention_margin,mean_attention_entropy,aspect_total,source_share
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