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"""DeepFashion text-search comparison demo for CLIP vs Hyper3-CLIP in HyperView."""
from __future__ import annotations
import os
import re
import time
from collections import Counter
from pathlib import Path
from typing import Any
from datasets import load_dataset
from PIL import Image, ImageOps
import hyperview as hv
from hyperview.core.sample import Sample
SPACE_DIR = Path(__file__).resolve().parent
SPACE_HOST = os.environ.get("HYPERVIEW_HOST", "127.0.0.1")
SPACE_PORT = int(os.environ.get("HYPERVIEW_PORT", "6262"))
WORKSPACE_ID = os.environ.get("HYPERVIEW_WORKSPACE_ID", "fashion-retail-search-v062-samples-visible")
DATASET_NAME = os.environ.get("HYPERVIEW_DATASET_NAME", "deepfashion_text_search_clip_hyper3clip")
EXTENSION_DIR = SPACE_DIR / ".hyperview" / "extensions" / "fashion-search-readout"
HF_DATASET = os.environ.get("DEEPFASHION_HF_DATASET", "Marqo/deepfashion-inshop")
HF_SPLIT = os.environ.get("DEEPFASHION_HF_SPLIT", "data")
SAMPLES_PER_CATEGORY = int(os.environ.get("DEEPFASHION_SAMPLES_PER_CATEGORY", "45"))
MAX_SAMPLES = int(os.environ.get("DEEPFASHION_MAX_SAMPLES", "700"))
IMAGE_MAX_SIZE = (768, 768)
FORCE_SAMPLE_REFRESH = os.environ.get("HYPERVIEW_DEEPFASHION_FORCE_REFRESH", "").lower() in {
"1",
"true",
"yes",
}
ENABLE_CONTEXT_MAPS = os.environ.get("FASHION_ENABLE_CONTEXT_MAPS", "1").lower() in {
"1",
"true",
"yes",
}
EMBEDDING_MAX_ATTEMPTS = max(1, int(os.environ.get("HYPERVIEW_EMBEDDING_MAX_ATTEMPTS", "4")))
EMBEDDING_RETRY_DELAY_SECONDS = float(os.environ.get("HYPERVIEW_EMBEDDING_RETRY_DELAY_SECONDS", "15"))
DEFAULT_EXAMPLE_ID = os.environ.get("FASHION_DEFAULT_EXAMPLE_ID", "light-denim-leggings")
MODEL_SPECS = [
{
"key": "clip",
"display_name": os.environ.get("FASHION_BASELINE_DISPLAY_NAME", "CLIP"),
"button_label": os.environ.get("FASHION_BASELINE_BUTTON_LABEL", "Inspect CLIP neighbors"),
"provider": os.environ.get("FASHION_BASELINE_PROVIDER", "embed-anything"),
"model": os.environ.get("FASHION_BASELINE_MODEL", "openai/clip-vit-base-patch32"),
"layout": os.environ.get("FASHION_BASELINE_LAYOUT", "euclidean:2d"),
"geometry": os.environ.get("FASHION_BASELINE_GEOMETRY", "euclidean"),
"layout_dimension": int(os.environ.get("FASHION_BASELINE_LAYOUT_DIMENSION", "2")),
"metric": os.environ.get("FASHION_BASELINE_METRIC", "cosine"),
"panel_title": os.environ.get("FASHION_BASELINE_PANEL_TITLE", "CLIP - Fashion Catalog Map"),
},
{
"key": "candidate",
"display_name": os.environ.get("FASHION_CANDIDATE_DISPLAY_NAME", "Hyper3-CLIP"),
"button_label": os.environ.get("FASHION_CANDIDATE_BUTTON_LABEL", "Inspect Hyper3-CLIP neighbors"),
"provider": os.environ.get("FASHION_CANDIDATE_PROVIDER", "hyper-models"),
"model": os.environ.get("FASHION_CANDIDATE_MODEL", "hyper3-clip-v0.5"),
"layout": os.environ.get("FASHION_CANDIDATE_LAYOUT", "poincare:2d"),
"geometry": os.environ.get("FASHION_CANDIDATE_GEOMETRY", "poincare"),
"layout_dimension": int(os.environ.get("FASHION_CANDIDATE_LAYOUT_DIMENSION", "2")),
"metric": os.environ.get("FASHION_CANDIDATE_METRIC", "cosine"),
"panel_title": os.environ.get("FASHION_CANDIDATE_PANEL_TITLE", "Hyper3-CLIP - Fashion Catalog Map"),
},
]
TEXT_SEARCH_EXAMPLES = [
{
"id": "light-denim-leggings",
"title": "Light denim leggings",
"targetItemId": "WOMEN_Leggings_id_00001867_02_3_back",
"targetProduct": "WOMEN_Leggings_id_00001867_02",
"targetTitle": "women's light denim leggings",
"family": "Specific typed product search",
"query": "women's light denim leggings with skinny fit, zipper details, five-pocket construction, pockets",
"hyper3Rank": 1,
"clipRank": 32,
"hyper3Text": "Exact target is the first result.",
"clipText": "Top results drift to dark denim, black, and similar blue leggings before the exact item appears.",
},
{
"id": "olive-navy-pants",
"title": "Olive and navy drawstring pants",
"targetItemId": "MEN_Pants_id_00001468_03_6_flat",
"targetProduct": "MEN_Pants_id_00001468_03",
"targetTitle": "men's olive and navy drawstring pants",
"family": "Specific typed product search",
"query": "men's olive and navy pants with drawstring waist, pockets, striped pattern, knit fabric",
"hyper3Rank": 1,
"clipRank": 56,
"hyper3Text": "Exact target is the first result.",
"clipText": "CLIP ranks burgundy pants and visually similar pants before the requested product.",
},
{
"id": "cream-blue-halter-blouse",
"title": "Cream and blue halter blouse",
"targetItemId": "WOMEN_Blouses_Shirts_id_00007161_02_1_front",
"targetProduct": "WOMEN_Blouses_Shirts_id_00007161_02",
"targetTitle": "cream and blue halter blouse",
"family": "Attribute-heavy apparel search",
"query": "women's cream and blue blouse with halter neckline, floral pattern, striped pattern, tribal print",
"hyper3Rank": 4,
"clipRank": 33,
"hyper3Text": "Target views appear in the top 10.",
"clipText": "CLIP retrieves broadly similar tops but misses the exact blouse in the first screen.",
},
]
DEMO_RESULT_ITEM_IDS = {
"MEN_Pants_id_00001468_03_6_flat",
"MEN_Pants_id_00001468_04_6_flat",
"MEN_Pants_id_00004045_03_2_side",
"MEN_Pants_id_00004045_04_1_front",
"MEN_Pants_id_00004045_09_3_back",
"MEN_Pants_id_00004045_11_1_front",
"MEN_Pants_id_00004045_11_2_side",
"MEN_Pants_id_00004045_12_1_front",
"MEN_Pants_id_00004045_12_2_side",
"MEN_Pants_id_00004045_12_3_back",
"MEN_Pants_id_00004045_12_7_additional",
"MEN_Shirts_Polos_id_00007027_01_6_flat",
"MEN_Sweaters_id_00005177_03_2_side",
"MEN_Sweaters_id_00005177_03_3_back",
"MEN_Sweaters_id_00005177_03_4_full",
"WOMEN_Blouses_Shirts_id_00003641_01_1_front",
"WOMEN_Blouses_Shirts_id_00006345_01_7_additional",
"WOMEN_Blouses_Shirts_id_00007049_01_7_additional",
"WOMEN_Blouses_Shirts_id_00007161_02_1_front",
"WOMEN_Cardigans_id_00000521_02_3_back",
"WOMEN_Denim_id_00000152_04_1_front",
"WOMEN_Denim_id_00000152_04_2_side",
"WOMEN_Denim_id_00002338_02_7_additional",
"WOMEN_Denim_id_00002338_03_1_front",
"WOMEN_Denim_id_00002338_03_3_back",
"WOMEN_Denim_id_00002338_03_7_additional",
"WOMEN_Denim_id_00005673_02_3_back",
"WOMEN_Dresses_id_00006961_02_1_front",
"WOMEN_Leggings_id_00001412_01_2_side",
"WOMEN_Leggings_id_00001867_02_3_back",
"WOMEN_Leggings_id_00002130_02_2_side",
"WOMEN_Leggings_id_00003850_01_2_side",
"WOMEN_Leggings_id_00003908_07_2_side",
"WOMEN_Leggings_id_00003908_08_2_side",
"WOMEN_Leggings_id_00004562_01_3_back",
"WOMEN_Pants_id_00000053_02_1_front",
"WOMEN_Pants_id_00001574_02_3_back",
"WOMEN_Rompers_Jumpsuits_id_00004432_02_3_back",
"WOMEN_Rompers_Jumpsuits_id_00004653_02_2_side",
"WOMEN_Rompers_Jumpsuits_id_00005484_01_3_back",
"WOMEN_Sweaters_id_00003304_01_1_front",
"WOMEN_Sweaters_id_00003304_01_2_side",
"WOMEN_Tees_Tanks_id_00000676_01_1_front",
"WOMEN_Tees_Tanks_id_00000676_01_2_side",
}
def media_root() -> Path:
root = Path(os.environ.get("HYPERVIEW_MEDIA_DIR", str(SPACE_DIR / "demo_data" / "media")))
path = root / DATASET_NAME
path.mkdir(parents=True, exist_ok=True)
return path
def product_key(item_id: str) -> str:
return re.sub(r"_\d+_[A-Za-z]+$", "", str(item_id))
def safe_sample_id(item_id: str) -> str:
return re.sub(r"[^A-Za-z0-9_.-]+", "_", str(item_id)).strip("_")[:96]
def readable(value: Any) -> str:
text = str(value or "").replace("_", " ").replace("-", " ")
return re.sub(r"\s+", " ", text).strip()
def save_image(image: Image.Image, destination: Path) -> None:
if destination.exists() and destination.stat().st_size > 0 and not FORCE_SAMPLE_REFRESH:
return
tmp_path = destination.with_suffix(destination.suffix + ".tmp")
image = ImageOps.exif_transpose(image).convert("RGB")
image.thumbnail(IMAGE_MAX_SIZE, Image.Resampling.LANCZOS)
image.save(tmp_path, format="JPEG", quality=92, optimize=True)
tmp_path.replace(destination)
def select_deepfashion_records() -> list[dict[str, Any]]:
print(f"Loading DeepFashion split {HF_SPLIT!r} from {HF_DATASET}...", flush=True)
source = load_dataset(HF_DATASET, split=HF_SPLIT)
required_products = {example["targetProduct"] for example in TEXT_SEARCH_EXAMPLES}
required_item_ids = {example["targetItemId"] for example in TEXT_SEARCH_EXAMPLES} | DEMO_RESULT_ITEM_IDS
selected: list[dict[str, Any]] = []
seen: set[str] = set()
category_counts: Counter[str] = Counter()
for index, row in enumerate(source):
item_id = str(row["item_ID"])
category = str(row.get("category2") or "unknown")
product = product_key(item_id)
required = product in required_products or item_id in required_item_ids
balanced = category_counts[category] < SAMPLES_PER_CATEGORY and len(selected) < MAX_SAMPLES
if not required and not balanced:
continue
if item_id in seen:
continue
selected.append({"index": index, **row})
seen.add(item_id)
category_counts[category] += 1
missing = sorted(required_item_ids - seen)
if missing:
raise RuntimeError(f"Missing required demo items from DeepFashion: {missing}")
print(f"Selected {len(selected)} DeepFashion images: {dict(category_counts)}", flush=True)
return selected
def add_deepfashion_samples(dataset: hv.Dataset) -> None:
existing_ids = {sample.id for sample in dataset.samples}
media_dir = media_root()
added = 0
updated = 0
skipped_existing = 0
records = select_deepfashion_records()
samples: list[Sample] = []
for record in records:
item_id = str(record["item_ID"])
sample_id = safe_sample_id(item_id)
existed = sample_id in existing_ids
if existed and not FORCE_SAMPLE_REFRESH:
skipped_existing += 1
continue
destination = media_dir / f"{sample_id}.jpg"
save_image(record["image"], destination)
category = readable(record.get("category2") or "unknown").lower()
color = readable(record.get("color") or "unknown")
metadata = {
"item_id": item_id,
"product_key": product_key(item_id),
"gender": readable(record.get("category1") or "unknown"),
"category": category,
"subcategory": readable(record.get("category3") or "unknown"),
"color": color,
"description": readable(record.get("description") or ""),
"text": readable(record.get("text") or ""),
"source_dataset": HF_DATASET,
"split": HF_SPLIT,
}
samples.append(
Sample(
id=sample_id,
filepath=str(destination),
label=category,
metadata=metadata,
)
)
if existed:
updated += 1
else:
existing_ids.add(sample_id)
added += 1
dataset.add_samples(samples, skip_existing=False)
if skipped_existing:
print(f"Skipped {skipped_existing} existing DeepFashion sample rows.", flush=True)
print(f"Prepared DeepFashion samples ({added} added, {updated} updated).", flush=True)
def compute_embeddings_with_retry(dataset: hv.Dataset, spec: dict[str, Any]) -> str:
for attempt in range(1, EMBEDDING_MAX_ATTEMPTS + 1):
try:
return dataset.compute_embeddings(
model=spec["model"],
provider=spec["provider"],
batch_size=32,
show_progress=True,
)
except BaseException as exc:
if isinstance(exc, (KeyboardInterrupt, SystemExit)):
raise
if attempt >= EMBEDDING_MAX_ATTEMPTS:
raise
delay = EMBEDDING_RETRY_DELAY_SECONDS * attempt
print(
f"Embedding load failed for {spec['display_name']} "
f"({type(exc).__name__}: {exc}). Retrying in {delay:.0f}s "
f"({attempt + 1}/{EMBEDDING_MAX_ATTEMPTS})...",
flush=True,
)
time.sleep(delay)
raise RuntimeError(f"Failed to compute embeddings for {spec['display_name']}")
def ensure_layouts(dataset: hv.Dataset) -> dict[str, str]:
layouts: dict[str, str] = {}
for spec in MODEL_SPECS:
print(f"Ensuring {spec['display_name']} embeddings...", flush=True)
space_key = compute_embeddings_with_retry(dataset, spec)
print(f"Ensuring {spec['display_name']} layout...", flush=True)
layout_key = dataset.compute_visualization(
space_key=space_key,
layout=spec["layout"],
n_neighbors=20,
min_dist=0.08,
metric=spec["metric"],
)
spec["layout_key"] = layout_key
layouts[spec["key"]] = layout_key
return layouts
def build_dataset() -> tuple[hv.Dataset, dict[str, str]]:
dataset = hv.Dataset(DATASET_NAME)
add_deepfashion_samples(dataset)
if ENABLE_CONTEXT_MAPS:
layouts = ensure_layouts(dataset)
else:
layouts = {}
return dataset, layouts
def model_panel_props(layouts: dict[str, str]) -> list[dict[str, Any]]:
props = []
for spec in MODEL_SPECS:
layout_key = layouts.get(spec["key"])
props.append(
{
"key": spec["key"],
"displayName": spec["display_name"],
"buttonLabel": spec["button_label"],
"layoutKey": layout_key,
}
)
return props
def neighbor_summary(dataset: hv.Dataset, sample_id: str, model_key: str) -> dict[str, Any]:
spec = next((item for item in MODEL_SPECS if item["key"] == model_key), None)
if spec is None:
return {}
query = dataset[sample_id]
layout_key = spec.get("layout_key")
if layout_key is None:
return {}
neighbors = dataset.find_similar(sample_id, k=10, layout_key=str(layout_key))
query_product = query.metadata.get("product_key")
query_category = query.metadata.get("category")
product_hits = sum(1 for sample, _distance in neighbors if sample.metadata.get("product_key") == query_product)
category_hits = sum(1 for sample, _distance in neighbors if sample.metadata.get("category") == query_category)
return {"productHits": product_hits, "categoryHits": category_hits, "total": len(neighbors)}
def build_examples(dataset: hv.Dataset) -> list[dict[str, Any]]:
examples = []
for item in TEXT_SEARCH_EXAMPLES:
sample_id = safe_sample_id(item["targetItemId"])
if sample_id not in {sample.id for sample in dataset.samples}:
continue
examples.append(
{
"id": item["id"],
"title": item["title"],
"family": item["family"],
"query": item["query"],
"queryId": sample_id,
"targetTitle": item["targetTitle"],
"summaries": {
"clip": {
"rank": item["clipRank"],
"text": item["clipText"],
**neighbor_summary(dataset, sample_id, "clip"),
},
"candidate": {
"rank": item["hyper3Rank"],
"text": item["hyper3Text"],
**neighbor_summary(dataset, sample_id, "candidate"),
},
},
}
)
return examples
def build_demo_view(dataset: hv.Dataset, layouts: dict[str, str]) -> hv.ui.View:
shared_props = {
"models": model_panel_props(layouts),
"examples": build_examples(dataset),
"initialExampleId": DEFAULT_EXAMPLE_ID,
"metrics": {
"typedQueryCount": 180,
"typedCandidateImages": 1120,
"hit10Hyper3Only": 23,
"hit10ClipOnly": 19,
"strongHyper3Wins": 13,
"strongClipWins": 9,
"imageRetrievalMapHyper3": 0.407,
"imageRetrievalMapClip": 0.240,
"typedHit1Hyper3": 0.244,
"typedHit1Clip": 0.233,
"typedHit10Hyper3": 0.572,
"typedHit10Clip": 0.550,
"typedCategoryP10Hyper3": 0.594,
"typedCategoryP10Clip": 0.561,
"typedMrrHyper3": 0.358,
"typedMrrClip": 0.344,
},
}
results_panel = hv.ui.ExtensionPanel(
id="fashion-ranked-results",
title="Ranked Search Results",
extension="fashion-search-readout",
panel="fashion-comparison",
position="center",
layout=hv.ui.PanelLayout(
width=int(os.environ.get("FASHION_RESULTS_WIDTH", "620")),
min_width=500,
),
props={
**shared_props,
"mode": "results",
},
)
samples_panel = hv.ui.Samples(
id="grid",
title="Samples",
position="center",
reference_panel_id="fashion-ranked-results",
direction="right",
layout=hv.ui.PanelLayout(
width=int(os.environ.get("FASHION_SAMPLES_WIDTH", "660")),
min_width=420,
min_height=480,
),
)
if not ENABLE_CONTEXT_MAPS:
return hv.ui.View(results_panel, samples_panel, active_panel="fashion-ranked-results")
clip_spec = MODEL_SPECS[0]
candidate_spec = MODEL_SPECS[1]
map_layout = hv.ui.PanelLayout(
height=int(os.environ.get("FASHION_MAP_HEIGHT", "180")),
min_height=150,
min_width=220,
)
clip_map = hv.ui.Scatter(
id="fashion-map-clip",
title="Context Map: CLIP",
layout_key=layouts["clip"],
position="center",
reference_panel_id="grid",
direction="below",
geometry=clip_spec["geometry"],
layout_dimension=clip_spec["layout_dimension"],
layout=map_layout,
)
candidate_map = hv.ui.Scatter(
id="fashion-map-hyper3",
title="Context Map: Hyper3",
layout_key=layouts["candidate"],
position="center",
reference_panel_id="fashion-map-clip",
direction="right",
geometry=candidate_spec["geometry"],
layout_dimension=candidate_spec["layout_dimension"],
layout=map_layout,
)
return hv.ui.View(
results_panel,
samples_panel,
clip_map,
candidate_map,
active_panel="fashion-ranked-results",
)
def initial_target_sample_id() -> str | None:
example = next(
(item for item in TEXT_SEARCH_EXAMPLES if item["id"] == DEFAULT_EXAMPLE_ID),
TEXT_SEARCH_EXAMPLES[0] if TEXT_SEARCH_EXAMPLES else None,
)
if example is None:
return None
return safe_sample_id(example["targetItemId"])
def launch_demo(dataset: hv.Dataset, layouts: dict[str, str]) -> hv.Session:
session = hv.launch(
dataset,
host=SPACE_HOST,
port=SPACE_PORT,
open_browser=False,
workspace_id=WORKSPACE_ID,
block=False,
)
print("Installing DeepFashion demo extension...", flush=True)
session.ui.add_extension(EXTENSION_DIR, workspace_id=WORKSPACE_ID)
print("Applying DeepFashion retail search demo view...", flush=True)
session.ui.apply_view(build_demo_view(dataset, layouts), workspace_id=WORKSPACE_ID)
if ENABLE_CONTEXT_MAPS and layouts:
session.ui.set_active_layout(layouts["clip"], workspace_id=WORKSPACE_ID)
sample_id = initial_target_sample_id()
if sample_id:
session.ui.set_selection([sample_id], workspace_id=WORKSPACE_ID)
print(f"\nHyperView DeepFashion text-search demo is running at {session.url}", flush=True)
if ENABLE_CONTEXT_MAPS:
print(" Samples and nearest neighbors stay visible; scatter maps use the actual CLIP/Hyper3 layouts.", flush=True)
else:
print(" Samples stay visible; ranked text-search results are the main demo.", flush=True)
return session
def main() -> None:
dataset, layouts = build_dataset()
if layouts:
print("Layouts:", flush=True)
for spec in MODEL_SPECS:
print(f" {spec['display_name']}: {layouts[spec['key']]}", flush=True)
else:
print("Context maps disabled; skipping embedding/layout startup.", flush=True)
session = launch_demo(dataset, layouts)
session.wait()
if __name__ == "__main__":
main()
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