Clean push: CoVE API

.gitignore

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+# Ignore Python cache and compiled files
+__pycache__/
+*.py[cod]
+*.ipynb_checkpoints/
+
+# Ignore virtual environments
+.venv/
+env/
+venv/
+
+# Ignore Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+
+# Ignore system files
+.DS_Store
+
+# Ignore binary/model/index files
+*.faiss
+*.npy
+*.pt
+*.onnx
+*.bin
+*.ckpt
+*.pkl
+*.jpg
+*.png
+*.gif
+*.jpeg
+
+# Ignore log files and outputs
+*.log
+*.csv
+logs/
+output/
+
+# Ignore Hugging Face cache (optional)
+~/.cache/huggingface/
+
+# Ignore datasets or raw data (optional)
+data/raw/
+data/interim/
+data/processed/*/*.faiss

README.md

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+# CoVE API (FastAPI) for Hugging Face Spaces
+
+This Space exposes the backend FastAPI service for MMR-Agentic-CoVE recommender system. It supports `/recommend` and `/chat_recommend` endpoints, enriched with metadata and FAISS search.
+
+It is meant to be paired with the [Gradio frontend in `cove-ui`](https://huggingface.co/spaces/yourusername/cove-ui).

api/.ipynb_checkpoints/app_api-checkpoint.py

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+# api/app_api.py  (Part 1/5)
+
+from __future__ import annotations
+
+import os
+import time
+import inspect
+import ast
+import math
+import re
+import traceback
+from typing import Any, Dict, List, Optional
+import json
+import numpy as np
+from starlette.responses import Response
+
+import pandas as pd
+from fastapi import FastAPI, Query
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field
+from pathlib import Path  # NEW
+
+from src.utils.paths import get_processed_path
+from src.service.recommender import recommend_for_user, RecommendConfig, FusionWeights
+from src.agents.chat_agent import ChatAgent, ChatAgentConfig
+
+# ---------- NEW: light config for logs location ----------
+LOGS_DIR = Path(os.getenv("LOGS_DIR", "logs"))
+
+# Instantiate the chat agent used by /chat_recommend
+CHAT_AGENT = ChatAgent(ChatAgentConfig())
+
+# =========================
+# Introspection (agentz)
+# =========================
+def _agent_introspection():
+    try:
+        fn = getattr(ChatAgent, "reply", None)
+        code = getattr(fn, "__code__", None)
+        file_path = getattr(code, "co_filename", None)
+        mtime = None
+        if file_path and os.path.exists(file_path):
+            mtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(os.path.getmtime(file_path)))
+        sig = str(inspect.signature(ChatAgent.reply)) if hasattr(ChatAgent, "reply") else "N/A"
+        return {
+            "class": str(CHAT_AGENT.__class__),
+            "module": ChatAgent.__module__,
+            "file": file_path,
+            "file_mtime": mtime,
+            "reply_signature": sig,
+            "has_debug_attr_on_instance": hasattr(CHAT_AGENT, "debug"),
+        }
+    except Exception as e:
+        return {"error": f"{type(e).__name__}: {e}"}
+
+# ---------- NEW: metrics helper (reads logs/metrics.csv if present) ----------
+def _latest_metrics_for(dataset: str, fusion: str, k: int, faiss_name: Optional[str]) -> Dict[str, Any]:
+    """
+    Heuristic: read logs/metrics.csv and pick the newest row that matches dataset and (faiss_name in model/run_name)
+    or at least matches fusion. Returns keys suitable for the UI:
+      {"hit@k": <float|str>, "ndcg@k": <float|str>, "memory_mb": <float|str>}
+    If nothing found, returns {}.
+    """
+    csv_fp = LOGS_DIR / "metrics.csv"
+    if not csv_fp.exists():
+        return {}
+    try:
+        df = pd.read_csv(csv_fp)
+    except Exception:
+        return {}
+
+    try:
+        if "dataset" in df.columns:
+            df = df[df["dataset"].astype(str).str.lower() == str(dataset).lower()]
+
+        # Prefer matching K if available
+        if "k" in df.columns:
+            with_k = df[df["k"].astype(str) == str(int(k))]
+            if not with_k.empty:
+                df = with_k
+
+        # newest first if timestamp
+        if "timestamp" in df.columns:
+            try:
+                df = df.sort_values("timestamp", ascending=False)
+            except Exception:
+                pass
+
+        def _row_matches(row) -> bool:
+            text = " ".join(str(row.get(c, "")) for c in ["model", "run_name"])
+            if faiss_name:
+                return faiss_name in text
+            return str(fusion).lower() in text.lower()
+
+        pick = None
+        for _, r in df.iterrows():
+            if _row_matches(r):
+                pick = r
+                break
+        if pick is None and len(df):
+            pick = df.iloc[0]
+
+        if pick is None:
+            return {}
+
+        def _safe_float(v):
+            try:
+                f = float(v)
+                if not math.isfinite(f):
+                    return None
+                return f
+            except Exception:
+                return None
+
+        return {
+            "hit@k": _safe_float(pick.get("hit")),
+            "ndcg@k": _safe_float(pick.get("ndcg")),
+            "memory_mb": _safe_float(pick.get("memory_mb")),
+        }
+    except Exception:
+        return {}
+
+# =========================
+# Helpers (parsing/cleanup)
+# =========================
+_PRICE_RE = re.compile(r"\$?\s*([0-9]+(?:\.[0-9]+)?)")
+_STOPWORDS = {"under","below","less","than","max","upto","up","to","recommend","something","for","me","need","budget","cheap","please","soap","shampoos"}
+
+def _parse_price_cap(text: str) -> Optional[float]:
+    m = _PRICE_RE.search(text or "")
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def _parse_keyword(text: str) -> Optional[str]:
+    t = (text or "").lower()
+    t = _PRICE_RE.sub(" ", t)
+    for w in re.findall(r"[a-z][a-z0-9\-]+", t):
+        if w in _STOPWORDS:
+            continue
+        return w
+    return None
+
+def _parse_listlike_string(s: str) -> List[str]:
+    """Parse strings like "['A','B']" or '["A"]' into ['A','B']; otherwise a best-effort list."""
+    if not isinstance(s, str):
+        return []
+    t = s.strip()
+    if (t.startswith("[") and t.endswith("]")) or (t.startswith("(") and t.endswith(")")):
+        try:
+            val = ast.literal_eval(t)
+            if isinstance(val, (list, tuple, set)):
+                return [str(x).strip() for x in val if x is not None and str(x).strip()]
+        except Exception:
+            pass
+    if re.search(r"[>|,/;]+", t):
+        return [p.strip() for p in re.split(r"[>|,/;]+", t) if p.strip()]
+    return [t] if t else []
+
+def _normalize_categories_in_place(items):
+    """
+    Force each item's 'categories' into a clean List[str].
+    Supports None, stringified lists, nested containers, etc.
+    """
+    def _as_list_from_string(s: str) -> List[str]:
+        s = (s or "").strip()
+        if not s:
+            return []
+        if (s.startswith("[") and s.endswith("]")) or (s.startswith("(") and s.endswith(")")):
+            try:
+                parsed = ast.literal_eval(s)
+                if isinstance(parsed, (list, tuple, set)):
+                    return [str(x).strip() for x in parsed if x is not None and str(x).strip()]
+            except Exception:
+                pass
+        return [s]
+
+    for r in items or []:
+        cats = r.get("categories")
+        out: List[str] = []
+        if cats is None:
+            out = []
+        elif isinstance(cats, str):
+            out = _as_list_from_string(cats)
+        elif isinstance(cats, (list, tuple, set)):
+            tmp: List[str] = []
+            for c in cats:
+                if c is None:
+                    continue
+                if isinstance(c, str):
+                    tmp.extend(_as_list_from_string(c))
+                elif isinstance(c, (list, tuple, set)):
+                    for y in c:
+                        if y is None:
+                            continue
+                        if isinstance(y, str):
+                            tmp.extend(_as_list_from_string(y))
+                        else:
+                            ys = str(y).strip()
+                            if ys:
+                                tmp.append(ys)
+                else:
+                    s = str(c).strip()
+                    if s:
+                        tmp.append(s)
+            seen = set()
+            out = []
+            for x in tmp:
+                if x and x not in seen:
+                    seen.add(x)
+                    out.append(x)
+        else:
+            s = str(cats).strip()
+            out = [s] if s else []
+        r["categories"] = out
+
+def _first_image_url_from_row(row: pd.Series) -> Optional[str]:
+    """
+    Return a single best image URL from several possible columns or formats:
+      - 'image_url' scalar string or list
+      - 'imageURL' / 'imageURLHighRes' (AMZ style) with lists or stringified lists
+    """
+    candidates: List[Any] = []
+    for col in ["image_url", "imageURLHighRes", "imageURL"]:
+        if col in row.index:
+            candidates.append(row[col])
+
+    urls: List[str] = []
+    for v in candidates:
+        if v is None:
+            continue
+        if isinstance(v, str):
+            vv = v.strip()
+            if (vv.startswith("[") and vv.endswith("]")) or (vv.startswith("(") and vv.endswith(")")):
+                try:
+                    lst = ast.literal_eval(vv)
+                    if isinstance(lst, (list, tuple, set)):
+                        urls.extend([str(x).strip() for x in lst if x])
+                except Exception:
+                    if vv:
+                        urls.append(vv)
+            else:
+                urls.append(vv)
+        elif isinstance(v, (list, tuple, set)):
+            urls.extend([str(x).strip() for x in v if x])
+        else:
+            s = str(v).strip()
+            if s:
+                urls.append(s)
+
+    for u in urls:
+        if u.lower().startswith("http"):
+            return u
+    return urls[0] if urls else None
+
+def _parse_rank_num(s: Any) -> Optional[int]:
+    """Extract numeric rank from strings like '2,938,573 in Beauty & Personal Care ('."""
+    if s is None or (isinstance(s, float) and not math.isfinite(s)):
+        return None
+    try:
+        if isinstance(s, (int, float)):
+            return int(s)
+        txt = str(s)
+        m = re.search(r"([\d,]+)", txt)
+        if not m:
+            return None
+        return int(m.group(1).replace(",", ""))
+    except Exception:
+        return None
+
+def _to_jsonable(obj: Any):
+    """Convert numpy/pandas and other non-JSON-serializable objects to plain Python types."""
+    try:
+        import numpy as np  # type: ignore
+    except Exception:
+        np = None  # type: ignore
+
+    if obj is None or isinstance(obj, (str, bool)):
+        return obj
+    if isinstance(obj, (int, float)):
+        if isinstance(obj, float) and not math.isfinite(obj):
+            return None
+        return obj
+
+    if np is not None:
+        if isinstance(obj, getattr(np, "integer", ())):
+            return int(obj)
+        if isinstance(obj, getattr(np, "floating", ())):
+            f = float(obj)
+            return None if not math.isfinite(f) else f
+        if isinstance(obj, getattr(np, "bool_", ())):
+            return bool(obj)
+
+    if isinstance(obj, dict):
+        return {str(k): _to_jsonable(v) for k, v in obj.items()}
+    if isinstance(obj, (list, tuple, set)):
+        return [_to_jsonable(v) for v in obj]
+
+    if isinstance(obj, pd.Series):
+        return {str(k): _to_jsonable(v) for k, v in obj.to_dict().items()}
+    if isinstance(obj, pd.DataFrame):
+        return [_to_jsonable(r) for r in obj.to_dict(orient="records")]
+
+    if hasattr(obj, "_asdict"):
+        return {str(k): _to_jsonable(v) for k, v in obj._asdict().items()}
+
+    return str(obj)
+
+# =========================
+# Catalog enrichment (API)
+# =========================
+def _load_catalog_like(dataset: str) -> pd.DataFrame:
+    """
+    Load an item catalog table for enrichment.
+    Preference:
+      1) items_catalog.parquet (enriched)
+      2) items_with_meta.parquet
+      3) joined.parquet (dedup on item_id)
+    Ensures presence of: item_id, title, brand, price, categories, image_url, rank.
+    """
+    proc = get_processed_path(dataset)
+    cands = [
+        proc / "items_catalog.parquet",
+        proc / "items_with_meta.parquet",
+        proc / "joined.parquet",
+    ]
+    df = pd.DataFrame()
+    for fp in cands:
+        if fp.exists():
+            try:
+                df = pd.read_parquet(fp)
+                break
+            except Exception:
+                pass
+
+    if df.empty:
+        return pd.DataFrame(columns=["item_id","title","brand","price","categories","image_url","rank"])
+
+    # If we loaded joined.parquet, dedup rows to unique item_id
+    if "item_id" in df.columns and df["item_id"].duplicated().any():
+        df = df.dropna(subset=["item_id"]).drop_duplicates(subset=["item_id"])
+
+    # Guarantee columns exist
+    for c in ["item_id","title","brand","price","categories","image_url","imageURL","imageURLHighRes","rank","rank_num"]:
+        if c not in df.columns:
+            df[c] = None
+
+    # Normalize derived columns
+    df["item_id"] = df["item_id"].astype(str)
+
+    # Best-effort image_url column
+    img_urls: List[Optional[str]] = []
+    for row in df.itertuples(index=False):
+        r = pd.Series(row._asdict() if hasattr(row, "_asdict") else row._asdict())
+        img_urls.append(_first_image_url_from_row(r))
+    df["image_url_best"] = img_urls
+
+    # Best-effort numeric rank
+    if "rank_num" in df.columns:
+        need = df["rank_num"].isna()
+        if "rank" in df.columns and need.any():
+            df.loc[need, "rank_num"] = df.loc[need, "rank"].map(_parse_rank_num)
+    else:
+        df["rank_num"] = df["rank"].map(_parse_rank_num)
+
+    return df[["item_id","title","brand","price","categories","image_url_best","rank","rank_num"]].rename(
+        columns={"image_url_best":"image_url"}
+    )
+
+
+def _enrich_with_catalog(dataset: str, recs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    if not recs:
+        return recs
+    try:
+        proc = get_processed_path(dataset)
+
+        # Load sources and keep extra image columns if present
+        sources: List[pd.DataFrame] = []
+        for name in ["items_catalog.parquet", "items_with_meta.parquet", "joined.parquet"]:
+            fp = proc / name
+            if fp.exists():
+                try:
+                    df = pd.read_parquet(fp)
+                    keep = [c for c in [
+                        "item_id","title","brand","price","categories","image_url","rank","rank_num",
+                        "imageURLHighRes","imageURL"  # extra image columns from raw meta
+                    ] if c in df.columns]
+                    if "item_id" in keep:
+                        slim = df[keep].copy()
+                        slim["item_id"] = slim["item_id"].astype(str)
+                        sources.append(slim.set_index("item_id", drop=False))
+                except Exception:
+                    pass
+        if not sources:
+            return recs
+
+        import ast, math, re
+
+        def _pick_non_empty(*vals):
+            for v in vals:
+                if v is None:
+                    continue
+                if isinstance(v, float) and not math.isfinite(v):
+                    continue
+                s = v.strip() if isinstance(v, str) else v
+                if s == "" or s == "nan":
+                    continue
+                return v
+            return None
+
+        def _pick_price(*vals):
+            for v in vals:
+                try:
+                    if v in (None, "", "nan"):
+                        continue
+                    f = float(v)
+                    if math.isfinite(f):
+                        return f
+                except Exception:
+                    continue
+            return None
+
+        def _norm_categories(v):
+            if v is None:
+                return []
+            if isinstance(v, (list, tuple, set)):
+                return [str(x).strip() for x in v if x is not None and str(x).strip()]
+            if isinstance(v, str):
+                s = v.strip()
+                if not s or s == "[]":
+                    return []
+                try:
+                    parsed = ast.literal_eval(s)
+                    if isinstance(parsed, (list, tuple, set)):
+                        return [str(x).strip() for x in parsed if x is not None and str(x).strip()]
+                except Exception:
+                    return [s]
+            return []
+
+        def _pick_categories(*vals):
+            for v in vals:
+                cats = _norm_categories(v)
+                if cats:
+                    return cats
+            return []
+
+        def _first_url_from_list(v):
+            if isinstance(v, (list, tuple)):
+                for u in v:
+                    if isinstance(u, str) and u.strip():
+                        return u.strip()
+            return None
+
+        def _pick_image_url(cand_image_url, cand_highres, cand_image):
+            # priority: explicit image_url (string), then imageURLHighRes[0], then imageURL[0]
+            if isinstance(cand_image_url, str) and cand_image_url.strip():
+                return cand_image_url.strip()
+            u = _first_url_from_list(cand_highres)
+            if u:
+                return u
+            u = _first_url_from_list(cand_image)
+            if u:
+                return u
+            if isinstance(cand_image_url, list):
+                u = _first_url_from_list(cand_image_url)
+                if u:
+                    return u
+            return None
+
+        def _pick_rank(*vals):
+            for v in vals:
+                if v is None or (isinstance(v, float) and not math.isfinite(v)):
+                    continue
+                if isinstance(v, (int, float)):
+                    return int(v)
+                if isinstance(v, str):
+                    m = re.search(r"[\d,]+", v)
+                    if m:
+                        try:
+                            return int(m.group(0).replace(",", ""))
+                        except Exception:
+                            pass
+            return None
+
+        def _lookup(iid: str, col: str):
+            for src in sources:
+                if iid in src.index and col in src.columns:
+                    return src.at[iid, col]
+            return None
+
+        out = []
+        for r in recs:
+            iid = str(r.get("item_id", ""))
+            if not iid:
+                out.append(r); continue
+
+            title = _pick_non_empty(r.get("title"), _lookup(iid, "title"))
+            brand = _pick_non_empty(r.get("brand"), _lookup(iid, "brand"))
+            price = _pick_price(r.get("price"), _lookup(iid, "price"))
+            cats  = _pick_categories(r.get("categories"), _lookup(iid, "categories"))
+            img   = _pick_image_url(
+                _lookup(iid, "image_url"),
+                _lookup(iid, "imageURLHighRes"),
+                _lookup(iid, "imageURL"),
+            )
+            rank  = _pick_rank(r.get("rank"), _lookup(iid, "rank_num"), _lookup(iid, "rank"))
+
+            if not cats and dataset.lower() == "beauty":
+                cats = ["Beauty & Personal Care"]
+
+            rr = {**r}
+            if title is not None: rr["title"] = title
+            if brand is not None: rr["brand"] = brand
+            rr["price"] = price
+            rr["categories"] = cats
+            rr["image_url"] = img
+            rr["rank"] = rank
+            out.append(rr)
+        return out
+    except Exception:
+        return recs
+    
+# =========================
+# FastAPI app
+# =========================
+app = FastAPI(title="MMR-Agentic-CoVE API", version="1.0.5")  # bumped
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # tighten for prod
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+from fastapi import HTTPException
+
+def _discover_faiss_names_api(dataset: str) -> List[str]:
+    proc = get_processed_path(dataset)
+    idx_dir = proc / "index"
+    if not idx_dir.exists():
+        return []
+    names: List[str] = []
+    for p in sorted(idx_dir.glob("items_*.faiss")):
+        # only keep this dataset's indices: items_<dataset>_*.faiss
+        if not p.stem.startswith(f"items_{dataset}_"):
+            continue
+        # expose the name AFTER 'items_'
+        names.append(p.stem[len("items_"):])  # e.g. beauty_concat
+    return names
+
+@app.get("/faiss")
+def list_faiss(dataset: str = Query(..., description="Dataset name")):
+    try:
+        names = _discover_faiss_names_api(dataset)
+        return {"dataset": dataset, "indexes": names}
+    except Exception as e:
+        tb = traceback.format_exc(limit=2)
+        return JSONResponse(status_code=500, content={"detail": f"/faiss failed: {e}", "traceback": tb})
+
+@app.get("/defaults")
+def get_defaults(dataset: str = Query(..., description="Dataset name")):
+    """
+    Return defaults.json contents (if present) so the UI can auto-fill
+    weights, k, and a suggested FAISS name.
+    """
+    try:
+        proc = get_processed_path(dataset)
+        fp = proc / "index" / "defaults.json"
+        if not fp.exists():
+            return {"dataset": dataset, "defaults": {}}
+        try:
+            payload = json.loads(fp.read_text())
+        except Exception:
+            payload = {}
+        return {"dataset": dataset, "defaults": payload}
+    except Exception as e:
+        tb = traceback.format_exc(limit=2)
+        raise HTTPException(status_code=500, detail={"error": str(e), "traceback": tb})
+
+# =========================
+# Schemas
+# =========================
+class RecommendIn(BaseModel):
+    dataset: str
+    user_id: str
+    k: int = 10
+    fusion: str = Field(default="weighted", pattern="^(concat|weighted)$")
+    # If any of these are None, the service will fall back to defaults.json (or the internal fallback).
+    w_text: Optional[float] = None
+    w_image: Optional[float] = None
+    w_meta: Optional[float] = None
+    use_faiss: bool = False
+    faiss_name: Optional[str] = None
+    exclude_seen: bool = True
+    alpha: Optional[float] = None  # legacy/no-op but accepted
+    # Optional passthrough for future CoVE handling (UI may send it; safe to ignore)
+    cove: Optional[str] = None     # NEW (optional, ignored by service unless you wire it)
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+class ChatIn(BaseModel):
+    messages: List[ChatMessage]
+    dataset: Optional[str] = None
+    user_id: Optional[str] = None
+    k: int = 5
+    use_faiss: bool = False
+    faiss_name: Optional[str] = None
+
+# =========================
+# JSON helpers
+# =========================
+def _np_default(o):
+    if isinstance(o, (np.integer,)):
+        return int(o)
+    if isinstance(o, (np.floating,)):
+        return float(o)
+    if isinstance(o, (np.ndarray,)):
+        return o.tolist()
+    return str(o)
+
+# =========================
+# Endpoints (info)
+# =========================
+
+@app.get("/users")
+def list_users(dataset: str = Query(..., description="Dataset name, e.g., 'beauty'")):
+    """
+    Return available user_ids (and optional display names if user_map.parquet exists).
+    """
+    try:
+        proc = get_processed_path(dataset)
+        fp_ids = proc / "user_text_emb.parquet"
+        if not fp_ids.exists():
+            return JSONResponse(
+                status_code=400,
+                content={"detail": f"Unknown dataset '{dataset}' or missing '{fp_ids.name}' in {proc}."},
+            )
+
+        # Load ids
+        df_ids = pd.read_parquet(fp_ids, columns=["user_id"])
+        users = sorted(df_ids["user_id"].astype(str).unique().tolist())
+
+        # Optional names
+        names: Dict[str, str] = {}
+        try:
+            umap_fp = proc / "user_map.parquet"
+            if umap_fp.exists():
+                umap = pd.read_parquet(umap_fp)
+                if {"user_id", "user_name"} <= set(umap.columns):
+                    umap["user_id"] = umap["user_id"].astype(str)
+                    umap = umap.dropna(subset=["user_id"]).drop_duplicates("user_id")
+                    names = dict(zip(umap["user_id"], umap["user_name"].fillna("").astype(str)))
+        except Exception:
+            names = {}
+
+        return {"dataset": dataset, "count": len(users), "users": users, "names": names}
+
+    except Exception as e:
+        tb = traceback.format_exc(limit=2)
+        return JSONResponse(status_code=500, content={"detail": f"/users failed: {e}", "traceback": tb})
+
+@app.get("/agentz")
+def agentz():
+    return _agent_introspection()
+
+    
+# api/app_api.py  (Part 4/5)
+
+@app.post("/recommend")
+def make_recommend(body: RecommendIn):
+    """
+    Core recommendation endpoint.
+    - Validates dataset files exist
+    - Optionally validates FAISS index if use_faiss=true
+    - Calls service.recommender.recommend_for_user
+    - Enriches with catalog info
+    - Normalizes JSON (numpy/pandas safe)
+    - NEW: adds 'metrics' block (hit@k, ndcg@k, memory_mb) if found
+    """
+    try:
+        # --- Preflight dataset/file check (mirrors /users) ---
+        proc = get_processed_path(body.dataset)
+        user_fp = proc / "user_text_emb.parquet"
+        if not user_fp.exists():
+            return JSONResponse(
+                status_code=400,
+                content={"detail": f"Unknown dataset '{body.dataset}' or missing '{user_fp.name}' in {proc}."},
+            )
+
+        # --- Build service config ---
+        cfg = RecommendConfig(
+            dataset=body.dataset,
+            user_id=str(body.user_id),
+            k=int(body.k),
+            fusion=body.fusion,
+            weights=FusionWeights(text=body.w_text, image=body.w_image, meta=body.w_meta),
+            alpha=body.alpha,                # legacy; ignored by service
+            use_faiss=body.use_faiss,
+            faiss_name=body.faiss_name,
+            exclude_seen=body.exclude_seen,
+        )
+
+        # --- Optional FAISS check (if explicit name given) ---
+        if cfg.use_faiss and cfg.faiss_name:
+            index_path = proc / "index" / f"items_{cfg.faiss_name}.faiss"
+            if not index_path.exists():
+                return JSONResponse(
+                    status_code=400,
+                    content={"detail": f"FAISS index not found: {index_path}. Build it or set use_faiss=false."},
+                )
+
+        # --- Call recommender service ---
+        out = recommend_for_user(cfg)
+
+        # Normalize list key
+        recs = out.get("results")
+        if recs is None:
+            recs = out.get("recommendations", [])
+        recs = list(recs or [])[: int(cfg.k)]
+
+        # Enrich & normalize
+        recs = _enrich_with_catalog(body.dataset, recs)
+        _normalize_categories_in_place(recs)
+
+        # Final coercions
+        for r in recs:
+            # rank
+            rn = r.get("rank_num")
+            if rn is not None:
+                try: r["rank"] = int(rn)
+                except Exception: r["rank"] = None
+            else:
+                rv = r.get("rank")
+                if isinstance(rv, str):
+                    m = re.search(r"[\d,]+", rv); r["rank"] = int(m.group(0).replace(",", "")) if m else None
+                elif isinstance(rv, (int, float)):
+                    try: r["rank"] = int(rv)
+                    except Exception: r["rank"] = None
+                else:
+                    r["rank"] = None
+
+            # price
+            v = r.get("price")
+            try:
+                rv = float(v) if v not in (None, "", "nan") else None
+                r["price"] = rv if (rv is None or math.isfinite(rv)) else None
+            except Exception:
+                r["price"] = None
+
+            # score
+            v = r.get("score")
+            try:
+                rv = float(v) if v not in (None, "", "nan") else None
+                r["score"] = rv if (rv is None or math.isfinite(rv)) else None
+            except Exception:
+                r["score"] = None
+
+            # image_url
+            v = r.get("image_url")
+            if isinstance(v, list):
+                r["image_url"] = next((u for u in v if isinstance(u, str) and u.strip()), None)
+            elif isinstance(v, str):
+                r["image_url"] = v.strip() or None
+            else:
+                r["image_url"] = None
+
+            # guard
+            cats = r.get("categories")
+            if isinstance(cats, list) and len(cats) == 1 and isinstance(cats[0], str) and cats[0].strip() == "[]":
+                r["categories"] = []
+
+        # Put normalized list back
+        out["results"] = _to_jsonable(recs)
+        out["recommendations"] = _to_jsonable(recs)
+
+        # ---------- NEW: attach metrics if we can find them ----------
+        try:
+            metrics = _latest_metrics_for(
+                dataset=body.dataset,
+                fusion=body.fusion,
+                k=int(body.k),
+                faiss_name=body.faiss_name,
+            )
+            if metrics:
+                out["metrics"] = metrics
+        except Exception:
+            # swallow — metrics are optional
+            pass
+
+        return JSONResponse(content=_to_jsonable(out))
+
+    except FileNotFoundError:
+        return JSONResponse(status_code=400, content={"detail": f"Dataset '{body.dataset}' not found or incomplete."})
+    except ValueError as e:
+        return JSONResponse(status_code=400, content={"detail": f"/recommend failed: {e}"})
+    except Exception as e:
+        tb = traceback.format_exc(limit=5)
+        return JSONResponse(status_code=500, content={"detail": f"/recommend failed: {e}", "traceback": tb})
+    
+# api/app_api.py  (Part 5/5)
+
+@app.post("/chat_recommend")
+def chat_recommend(body: ChatIn):
+    # Tolerant parse of messages
+    msgs: List[Dict[str, str]] = []
+    for m in body.messages:
+        if isinstance(m, dict):
+            msgs.append({"role": m.get("role"), "content": m.get("content")})
+        else:
+            d = m.model_dump() if hasattr(m, "model_dump") else m.dict()
+            msgs.append({"role": d.get("role"), "content": d.get("content")})
+
+    try:
+        out: Dict[str, Any] = {"reply": "", "recommendations": []}
+        recs: List[Dict[str, Any]] = []
+
+        # 1) Ask the agent
+        if hasattr(CHAT_AGENT, "reply"):
+            candidate_kwargs = {
+                "messages": msgs,
+                "dataset": body.dataset,
+                "user_id": body.user_id,
+                "k": body.k,
+                "use_faiss": body.use_faiss,
+                "faiss_name": body.faiss_name,
+            }
+            sig = inspect.signature(CHAT_AGENT.reply)
+            allowed = set(sig.parameters.keys())
+            safe_kwargs = {k: v for k, v in candidate_kwargs.items() if k in allowed}
+            agent_out = CHAT_AGENT.reply(**safe_kwargs)
+            if isinstance(agent_out, dict):
+                out.update(agent_out)
+                recs = agent_out.get("recommendations") or []
+            else:
+                out["reply"] = str(agent_out) if agent_out is not None else ""
+
+        recs = [dict(r) if not isinstance(r, dict) else r for r in (recs or [])]
+
+        # 2) Fallback
+        if not recs:
+            cfg = RecommendConfig(
+                dataset=body.dataset or "beauty",
+                user_id=str(body.user_id or ""),
+                k=int(body.k or 5),
+                fusion="weighted",
+                weights=FusionWeights(text=1.0, image=0.2, meta=0.2),
+                alpha=None,
+                use_faiss=False,
+                faiss_name=None,
+                exclude_seen=True,
+            )
+            try:
+                reco_out = recommend_for_user(cfg)
+                recs = reco_out.get("results") or reco_out.get("recommendations") or []
+                recs = [dict(r) if not isinstance(r, dict) else r for r in recs]
+                if not out.get("reply"):
+                    out["reply"] = "Here are some items you might like."
+            except Exception:
+                pass
+
+        # 3) Enrich + normalize (like /recommend)
+        ds = body.dataset or "beauty"
+        recs = _enrich_with_catalog(ds, recs)
+        _normalize_categories_in_place(recs)
+
+        for r in recs:
+            # price
+            v = r.get("price")
+            try:
+                rv = float(v) if v not in (None, "", "nan") else None
+                r["price"] = rv if (rv is None or math.isfinite(rv)) else None
+            except Exception:
+                r["price"] = None
+            # score
+            v = r.get("score")
+            try:
+                rv = float(v) if v not in (None, "", "nan") else None
+                r["score"] = rv if (rv is None or math.isfinite(rv)) else None
+            except Exception:
+                r["score"] = None
+            # rank
+            rn = r.get("rank_num")
+            if rn is not None:
+                try: r["rank"] = int(rn)
+                except Exception: r["rank"] = None
+            else:
+                rv = r.get("rank")
+                if isinstance(rv, str):
+                    m = re.search(r"[\d,]+", rv); r["rank"] = int(m.group(0).replace(",", "")) if m else None
+                elif isinstance(rv, (int, float)):
+                    try: r["rank"] = int(rv)
+                    except Exception: r["rank"] = None
+                else:
+                    r["rank"] = None
+            # image_url (string)
+            v = r.get("image_url")
+            if isinstance(v, list):
+                r["image_url"] = next((u for u in v if isinstance(u, str) and u.strip()), None)
+            elif isinstance(v, str):
+                r["image_url"] = v.strip() or None
+            else:
+                r["image_url"] = None
+
+        # 4) Lightweight chat constraints (budget/keyword) — unchanged
+        last = (msgs[-1]["content"] if msgs else "") or ""
+        cap = _parse_price_cap(last)
+        kw  = _parse_keyword(last)
+        if cap is not None:
+            recs = [r for r in recs if (r.get("price") is not None and r["price"] <= cap)]
+        if kw:
+            lowkw = kw.lower()
+            def _matches(item: Dict[str, Any]) -> bool:
+                fields = [str(item.get("brand") or ""), str(item.get("item_id") or "")]
+                fields.extend(item.get("categories") or [])
+                return lowkw in " ".join(fields).lower()
+            filtered = [r for r in recs if _matches(r)]
+            if filtered:
+                recs = filtered
+
+        out["recommendations"] = recs
+        out["results"] = recs
+
+        # ---------- NEW: attach metrics (optional best-effort) ----------
+        try:
+            metrics = _latest_metrics_for(
+                dataset=ds,
+                fusion="weighted",   # chat uses weighted defaults for now
+                k=int(body.k or 5),
+                faiss_name=body.faiss_name,
+            )
+            if metrics:
+                out["metrics"] = metrics
+        except Exception:
+            pass
+
+        return JSONResponse(content=_to_jsonable(out))
+
+    except Exception as e:
+        tb = traceback.format_exc(limit=5)
+        return JSONResponse(status_code=400, content={"detail": f"/chat_recommend failed: {e}", "traceback": tb})
+
+# =========================
+# Health & root
+# =========================
+@app.get("/healthz")
+def healthz():
+    return {"ok": True, "service": "MMR-Agentic-CoVE API", "version": getattr(app, "version", None) or "unknown"}
+
+@app.get("/")
+def root():
+    return {"ok": True, "service": "MMR-Agentic-CoVE API"}

api/app_api.py

@@ -0,0 +1,958 @@
+# api/app_api.py  (Part 1/5)
+
+from __future__ import annotations
+
+import os
+import time
+import inspect
+import ast
+import math
+import re
+import traceback
+from typing import Any, Dict, List, Optional
+import json
+import numpy as np
+from starlette.responses import Response
+
+import pandas as pd
+from fastapi import FastAPI, Query
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field
+from pathlib import Path  # NEW
+
+from src.utils.paths import get_processed_path
+from src.service.recommender import recommend_for_user, RecommendConfig, FusionWeights
+from src.agents.chat_agent import ChatAgent, ChatAgentConfig
+
+# ---------- NEW: light config for logs location ----------
+LOGS_DIR = Path(os.getenv("LOGS_DIR", "logs"))
+
+# Instantiate the chat agent used by /chat_recommend
+CHAT_AGENT = ChatAgent(ChatAgentConfig())
+
+# =========================
+# Introspection (agentz)
+# =========================
+def _agent_introspection():
+    try:
+        fn = getattr(ChatAgent, "reply", None)
+        code = getattr(fn, "__code__", None)
+        file_path = getattr(code, "co_filename", None)
+        mtime = None
+        if file_path and os.path.exists(file_path):
+            mtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(os.path.getmtime(file_path)))
+        sig = str(inspect.signature(ChatAgent.reply)) if hasattr(ChatAgent, "reply") else "N/A"
+        return {
+            "class": str(CHAT_AGENT.__class__),
+            "module": ChatAgent.__module__,
+            "file": file_path,
+            "file_mtime": mtime,
+            "reply_signature": sig,
+            "has_debug_attr_on_instance": hasattr(CHAT_AGENT, "debug"),
+        }
+    except Exception as e:
+        return {"error": f"{type(e).__name__}: {e}"}
+
+# ---------- NEW: metrics helper (reads logs/metrics.csv if present) ----------
+def _latest_metrics_for(dataset: str, fusion: str, k: int, faiss_name: Optional[str]) -> Dict[str, Any]:
+    """
+    Heuristic: read logs/metrics.csv and pick the newest row that matches dataset and (faiss_name in model/run_name)
+    or at least matches fusion. Returns keys suitable for the UI:
+      {"hit@k": <float|str>, "ndcg@k": <float|str>, "memory_mb": <float|str>}
+    If nothing found, returns {}.
+    """
+    csv_fp = LOGS_DIR / "metrics.csv"
+    if not csv_fp.exists():
+        return {}
+    try:
+        df = pd.read_csv(csv_fp)
+    except Exception:
+        return {}
+
+    try:
+        if "dataset" in df.columns:
+            df = df[df["dataset"].astype(str).str.lower() == str(dataset).lower()]
+
+        # Prefer matching K if available
+        if "k" in df.columns:
+            with_k = df[df["k"].astype(str) == str(int(k))]
+            if not with_k.empty:
+                df = with_k
+
+        # newest first if timestamp
+        if "timestamp" in df.columns:
+            try:
+                df = df.sort_values("timestamp", ascending=False)
+            except Exception:
+                pass
+
+        def _row_matches(row) -> bool:
+            text = " ".join(str(row.get(c, "")) for c in ["model", "run_name"])
+            if faiss_name:
+                return faiss_name in text
+            return str(fusion).lower() in text.lower()
+
+        pick = None
+        for _, r in df.iterrows():
+            if _row_matches(r):
+                pick = r
+                break
+        if pick is None and len(df):
+            pick = df.iloc[0]
+
+        if pick is None:
+            return {}
+
+        def _safe_float(v):
+            try:
+                f = float(v)
+                if not math.isfinite(f):
+                    return None
+                return f
+            except Exception:
+                return None
+
+        return {
+            "hit@k": _safe_float(pick.get("hit")),
+            "ndcg@k": _safe_float(pick.get("ndcg")),
+            "memory_mb": _safe_float(pick.get("memory_mb")),
+        }
+    except Exception:
+        return {}
+
+# =========================
+# Helpers (parsing/cleanup)
+# =========================
+_PRICE_RE = re.compile(r"\$?\s*([0-9]+(?:\.[0-9]+)?)")
+_STOPWORDS = {"under","below","less","than","max","upto","up","to","recommend","something","for","me","need","budget","cheap","please","soap","shampoos"}
+
+def _parse_price_cap(text: str) -> Optional[float]:
+    m = _PRICE_RE.search(text or "")
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def _parse_keyword(text: str) -> Optional[str]:
+    t = (text or "").lower()
+    t = _PRICE_RE.sub(" ", t)
+    for w in re.findall(r"[a-z][a-z0-9\-]+", t):
+        if w in _STOPWORDS:
+            continue
+        return w
+    return None
+
+def _parse_listlike_string(s: str) -> List[str]:
+    """Parse strings like "['A','B']" or '["A"]' into ['A','B']; otherwise a best-effort list."""
+    if not isinstance(s, str):
+        return []
+    t = s.strip()
+    if (t.startswith("[") and t.endswith("]")) or (t.startswith("(") and t.endswith(")")):
+        try:
+            val = ast.literal_eval(t)
+            if isinstance(val, (list, tuple, set)):
+                return [str(x).strip() for x in val if x is not None and str(x).strip()]
+        except Exception:
+            pass
+    if re.search(r"[>|,/;]+", t):
+        return [p.strip() for p in re.split(r"[>|,/;]+", t) if p.strip()]
+    return [t] if t else []
+
+def _normalize_categories_in_place(items):
+    """
+    Force each item's 'categories' into a clean List[str].
+    Supports None, stringified lists, nested containers, etc.
+    """
+    def _as_list_from_string(s: str) -> List[str]:
+        s = (s or "").strip()
+        if not s:
+            return []
+        if (s.startswith("[") and s.endswith("]")) or (s.startswith("(") and s.endswith(")")):
+            try:
+                parsed = ast.literal_eval(s)
+                if isinstance(parsed, (list, tuple, set)):
+                    return [str(x).strip() for x in parsed if x is not None and str(x).strip()]
+            except Exception:
+                pass
+        return [s]
+
+    for r in items or []:
+        cats = r.get("categories")
+        out: List[str] = []
+        if cats is None:
+            out = []
+        elif isinstance(cats, str):
+            out = _as_list_from_string(cats)
+        elif isinstance(cats, (list, tuple, set)):
+            tmp: List[str] = []
+            for c in cats:
+                if c is None:
+                    continue
+                if isinstance(c, str):
+                    tmp.extend(_as_list_from_string(c))
+                elif isinstance(c, (list, tuple, set)):
+                    for y in c:
+                        if y is None:
+                            continue
+                        if isinstance(y, str):
+                            tmp.extend(_as_list_from_string(y))
+                        else:
+                            ys = str(y).strip()
+                            if ys:
+                                tmp.append(ys)
+                else:
+                    s = str(c).strip()
+                    if s:
+                        tmp.append(s)
+            seen = set()
+            out = []
+            for x in tmp:
+                if x and x not in seen:
+                    seen.add(x)
+                    out.append(x)
+        else:
+            s = str(cats).strip()
+            out = [s] if s else []
+        r["categories"] = out
+
+def _first_image_url_from_row(row: pd.Series) -> Optional[str]:
+    """
+    Return a single best image URL from several possible columns or formats:
+      - 'image_url' scalar string or list
+      - 'imageURL' / 'imageURLHighRes' (AMZ style) with lists or stringified lists
+    """
+    candidates: List[Any] = []
+    for col in ["image_url", "imageURLHighRes", "imageURL"]:
+        if col in row.index:
+            candidates.append(row[col])
+
+    urls: List[str] = []
+    for v in candidates:
+        if v is None:
+            continue
+        if isinstance(v, str):
+            vv = v.strip()
+            if (vv.startswith("[") and vv.endswith("]")) or (vv.startswith("(") and vv.endswith(")")):
+                try:
+                    lst = ast.literal_eval(vv)
+                    if isinstance(lst, (list, tuple, set)):
+                        urls.extend([str(x).strip() for x in lst if x])
+                except Exception:
+                    if vv:
+                        urls.append(vv)
+            else:
+                urls.append(vv)
+        elif isinstance(v, (list, tuple, set)):
+            urls.extend([str(x).strip() for x in v if x])
+        else:
+            s = str(v).strip()
+            if s:
+                urls.append(s)
+
+    for u in urls:
+        if u.lower().startswith("http"):
+            return u
+    return urls[0] if urls else None
+
+def _parse_rank_num(s: Any) -> Optional[int]:
+    """Extract numeric rank from strings like '2,938,573 in Beauty & Personal Care ('."""
+    if s is None or (isinstance(s, float) and not math.isfinite(s)):
+        return None
+    try:
+        if isinstance(s, (int, float)):
+            return int(s)
+        txt = str(s)
+        m = re.search(r"([\d,]+)", txt)
+        if not m:
+            return None
+        return int(m.group(1).replace(",", ""))
+    except Exception:
+        return None
+
+def _to_jsonable(obj: Any):
+    """Convert numpy/pandas and other non-JSON-serializable objects to plain Python types."""
+    try:
+        import numpy as np  # type: ignore
+    except Exception:
+        np = None  # type: ignore
+
+    if obj is None or isinstance(obj, (str, bool)):
+        return obj
+    if isinstance(obj, (int, float)):
+        if isinstance(obj, float) and not math.isfinite(obj):
+            return None
+        return obj
+
+    if np is not None:
+        if isinstance(obj, getattr(np, "integer", ())):
+            return int(obj)
+        if isinstance(obj, getattr(np, "floating", ())):
+            f = float(obj)
+            return None if not math.isfinite(f) else f
+        if isinstance(obj, getattr(np, "bool_", ())):
+            return bool(obj)
+
+    if isinstance(obj, dict):
+        return {str(k): _to_jsonable(v) for k, v in obj.items()}
+    if isinstance(obj, (list, tuple, set)):
+        return [_to_jsonable(v) for v in obj]
+
+    if isinstance(obj, pd.Series):
+        return {str(k): _to_jsonable(v) for k, v in obj.to_dict().items()}
+    if isinstance(obj, pd.DataFrame):
+        return [_to_jsonable(r) for r in obj.to_dict(orient="records")]
+
+    if hasattr(obj, "_asdict"):
+        return {str(k): _to_jsonable(v) for k, v in obj._asdict().items()}
+
+    return str(obj)
+
+# =========================
+# Catalog enrichment (API)
+# =========================
+def _load_catalog_like(dataset: str) -> pd.DataFrame:
+    """
+    Load an item catalog table for enrichment.
+    Preference:
+      1) items_catalog.parquet (enriched)
+      2) items_with_meta.parquet
+      3) joined.parquet (dedup on item_id)
+    Ensures presence of: item_id, title, brand, price, categories, image_url, rank.
+    """
+    proc = get_processed_path(dataset)
+    cands = [
+        proc / "items_catalog.parquet",
+        proc / "items_with_meta.parquet",
+        proc / "joined.parquet",
+    ]
+    df = pd.DataFrame()
+    for fp in cands:
+        if fp.exists():
+            try:
+                df = pd.read_parquet(fp)
+                break
+            except Exception:
+                pass
+
+    if df.empty:
+        return pd.DataFrame(columns=["item_id","title","brand","price","categories","image_url","rank"])
+
+    # If we loaded joined.parquet, dedup rows to unique item_id
+    if "item_id" in df.columns and df["item_id"].duplicated().any():
+        df = df.dropna(subset=["item_id"]).drop_duplicates(subset=["item_id"])
+
+    # Guarantee columns exist
+    for c in ["item_id","title","brand","price","categories","image_url","imageURL","imageURLHighRes","rank","rank_num"]:
+        if c not in df.columns:
+            df[c] = None
+
+    # Normalize derived columns
+    df["item_id"] = df["item_id"].astype(str)
+
+    # Best-effort image_url column
+    img_urls: List[Optional[str]] = []
+    for row in df.itertuples(index=False):
+        r = pd.Series(row._asdict() if hasattr(row, "_asdict") else row._asdict())
+        img_urls.append(_first_image_url_from_row(r))
+    df["image_url_best"] = img_urls
+
+    # Best-effort numeric rank
+    if "rank_num" in df.columns:
+        need = df["rank_num"].isna()
+        if "rank" in df.columns and need.any():
+            df.loc[need, "rank_num"] = df.loc[need, "rank"].map(_parse_rank_num)
+    else:
+        df["rank_num"] = df["rank"].map(_parse_rank_num)
+
+    return df[["item_id","title","brand","price","categories","image_url_best","rank","rank_num"]].rename(
+        columns={"image_url_best":"image_url"}
+    )
+
+
+def _enrich_with_catalog(dataset: str, recs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    if not recs:
+        return recs
+    try:
+        proc = get_processed_path(dataset)
+
+        # Load sources and keep extra image columns if present
+        sources: List[pd.DataFrame] = []
+        for name in ["items_catalog.parquet", "items_with_meta.parquet", "joined.parquet"]:
+            fp = proc / name
+            if fp.exists():
+                try:
+                    df = pd.read_parquet(fp)
+                    keep = [c for c in [
+                        "item_id","title","brand","price","categories","image_url","rank","rank_num",
+                        "imageURLHighRes","imageURL"  # extra image columns from raw meta
+                    ] if c in df.columns]
+                    if "item_id" in keep:
+                        slim = df[keep].copy()
+                        slim["item_id"] = slim["item_id"].astype(str)
+                        sources.append(slim.set_index("item_id", drop=False))
+                except Exception:
+                    pass
+        if not sources:
+            return recs
+
+        import ast, math, re
+
+        def _pick_non_empty(*vals):
+            for v in vals:
+                if v is None:
+                    continue
+                if isinstance(v, float) and not math.isfinite(v):
+                    continue
+                s = v.strip() if isinstance(v, str) else v
+                if s == "" or s == "nan":
+                    continue
+                return v
+            return None
+
+        def _pick_price(*vals):
+            for v in vals:
+                try:
+                    if v in (None, "", "nan"):
+                        continue
+                    f = float(v)
+                    if math.isfinite(f):
+                        return f
+                except Exception:
+                    continue
+            return None
+
+        def _norm_categories(v):
+            if v is None:
+                return []
+            if isinstance(v, (list, tuple, set)):
+                return [str(x).strip() for x in v if x is not None and str(x).strip()]
+            if isinstance(v, str):
+                s = v.strip()
+                if not s or s == "[]":
+                    return []
+                try:
+                    parsed = ast.literal_eval(s)
+                    if isinstance(parsed, (list, tuple, set)):
+                        return [str(x).strip() for x in parsed if x is not None and str(x).strip()]
+                except Exception:
+                    return [s]
+            return []
+
+        def _pick_categories(*vals):
+            for v in vals:
+                cats = _norm_categories(v)
+                if cats:
+                    return cats
+            return []
+
+        def _first_url_from_list(v):
+            if isinstance(v, (list, tuple)):
+                for u in v:
+                    if isinstance(u, str) and u.strip():
+                        return u.strip()
+            return None
+
+        def _pick_image_url(cand_image_url, cand_highres, cand_image):
+            # priority: explicit image_url (string), then imageURLHighRes[0], then imageURL[0]
+            if isinstance(cand_image_url, str) and cand_image_url.strip():
+                return cand_image_url.strip()
+            u = _first_url_from_list(cand_highres)
+            if u:
+                return u
+            u = _first_url_from_list(cand_image)
+            if u:
+                return u
+            if isinstance(cand_image_url, list):
+                u = _first_url_from_list(cand_image_url)
+                if u:
+                    return u
+            return None
+
+        def _pick_rank(*vals):
+            for v in vals:
+                if v is None or (isinstance(v, float) and not math.isfinite(v)):
+                    continue
+                if isinstance(v, (int, float)):
+                    return int(v)
+                if isinstance(v, str):
+                    m = re.search(r"[\d,]+", v)
+                    if m:
+                        try:
+                            return int(m.group(0).replace(",", ""))
+                        except Exception:
+                            pass
+            return None
+
+        def _lookup(iid: str, col: str):
+            for src in sources:
+                if iid in src.index and col in src.columns:
+                    return src.at[iid, col]
+            return None
+
+        out = []
+        for r in recs:
+            iid = str(r.get("item_id", ""))
+            if not iid:
+                out.append(r); continue
+
+            title = _pick_non_empty(r.get("title"), _lookup(iid, "title"))
+            brand = _pick_non_empty(r.get("brand"), _lookup(iid, "brand"))
+            price = _pick_price(r.get("price"), _lookup(iid, "price"))
+            cats  = _pick_categories(r.get("categories"), _lookup(iid, "categories"))
+            img   = _pick_image_url(
+                _lookup(iid, "image_url"),
+                _lookup(iid, "imageURLHighRes"),
+                _lookup(iid, "imageURL"),
+            )
+            rank  = _pick_rank(r.get("rank"), _lookup(iid, "rank_num"), _lookup(iid, "rank"))
+
+            if not cats and dataset.lower() == "beauty":
+                cats = ["Beauty & Personal Care"]
+
+            rr = {**r}
+            if title is not None: rr["title"] = title
+            if brand is not None: rr["brand"] = brand
+            rr["price"] = price
+            rr["categories"] = cats
+            rr["image_url"] = img
+            rr["rank"] = rank
+            out.append(rr)
+        return out
+    except Exception:
+        return recs
+    
+# =========================
+# FastAPI app
+# =========================
+app = FastAPI(title="MMR-Agentic-CoVE API", version="1.0.5")  # bumped
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # tighten for prod
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+from fastapi import HTTPException
+
+def _discover_faiss_names_api(dataset: str) -> List[str]:
+    proc = get_processed_path(dataset)
+    idx_dir = proc / "index"
+    if not idx_dir.exists():
+        return []
+    names: List[str] = []
+    for p in sorted(idx_dir.glob("items_*.faiss")):
+        # only keep this dataset's indices: items_<dataset>_*.faiss
+        if not p.stem.startswith(f"items_{dataset}_"):
+            continue
+        # expose the name AFTER 'items_'
+        names.append(p.stem[len("items_"):])  # e.g. beauty_concat
+    return names
+
+@app.get("/faiss")
+def list_faiss(dataset: str = Query(..., description="Dataset name")):
+    try:
+        names = _discover_faiss_names_api(dataset)
+        return {"dataset": dataset, "indexes": names}
+    except Exception as e:
+        tb = traceback.format_exc(limit=2)
+        return JSONResponse(status_code=500, content={"detail": f"/faiss failed: {e}", "traceback": tb})
+
+@app.get("/defaults")
+def get_defaults(dataset: str = Query(..., description="Dataset name")):
+    """
+    Return defaults.json contents (if present) so the UI can auto-fill
+    weights, k, and a suggested FAISS name.
+    """
+    try:
+        proc = get_processed_path(dataset)
+        fp = proc / "index" / "defaults.json"
+        if not fp.exists():
+            return {"dataset": dataset, "defaults": {}}
+        try:
+            payload = json.loads(fp.read_text())
+        except Exception:
+            payload = {}
+        return {"dataset": dataset, "defaults": payload}
+    except Exception as e:
+        tb = traceback.format_exc(limit=2)
+        raise HTTPException(status_code=500, detail={"error": str(e), "traceback": tb})
+
+# =========================
+# Schemas
+# =========================
+class RecommendIn(BaseModel):
+    dataset: str
+    user_id: str
+    k: int = 10
+    fusion: str = Field(default="weighted", pattern="^(concat|weighted)$")
+    # If any of these are None, the service will fall back to defaults.json (or the internal fallback).
+    w_text: Optional[float] = None
+    w_image: Optional[float] = None
+    w_meta: Optional[float] = None
+    use_faiss: bool = False
+    faiss_name: Optional[str] = None
+    exclude_seen: bool = True
+    alpha: Optional[float] = None  # legacy/no-op but accepted
+    # Optional passthrough for future CoVE handling (UI may send it; safe to ignore)
+    cove: Optional[str] = None     # NEW (optional, ignored by service unless you wire it)
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+class ChatIn(BaseModel):
+    messages: List[ChatMessage]
+    dataset: Optional[str] = None
+    user_id: Optional[str] = None
+    k: int = 5
+    use_faiss: bool = False
+    faiss_name: Optional[str] = None
+
+# =========================
+# JSON helpers
+# =========================
+def _np_default(o):
+    if isinstance(o, (np.integer,)):
+        return int(o)
+    if isinstance(o, (np.floating,)):
+        return float(o)
+    if isinstance(o, (np.ndarray,)):
+        return o.tolist()
+    return str(o)
+
+# =========================
+# Endpoints (info)
+# =========================
+
+@app.get("/users")
+def list_users(dataset: str = Query(..., description="Dataset name, e.g., 'beauty'")):
+    """
+    Return available user_ids (and optional display names if user_map.parquet exists).
+    """
+    try:
+        proc = get_processed_path(dataset)
+        fp_ids = proc / "user_text_emb.parquet"
+        if not fp_ids.exists():
+            return JSONResponse(
+                status_code=400,
+                content={"detail": f"Unknown dataset '{dataset}' or missing '{fp_ids.name}' in {proc}."},
+            )
+
+        # Load ids
+        df_ids = pd.read_parquet(fp_ids, columns=["user_id"])
+        users = sorted(df_ids["user_id"].astype(str).unique().tolist())
+
+        # Optional names
+        names: Dict[str, str] = {}
+        try:
+            umap_fp = proc / "user_map.parquet"
+            if umap_fp.exists():
+                umap = pd.read_parquet(umap_fp)
+                if {"user_id", "user_name"} <= set(umap.columns):
+                    umap["user_id"] = umap["user_id"].astype(str)
+                    umap = umap.dropna(subset=["user_id"]).drop_duplicates("user_id")
+                    names = dict(zip(umap["user_id"], umap["user_name"].fillna("").astype(str)))
+        except Exception:
+            names = {}
+
+        return {"dataset": dataset, "count": len(users), "users": users, "names": names}
+
+    except Exception as e:
+        tb = traceback.format_exc(limit=2)
+        return JSONResponse(status_code=500, content={"detail": f"/users failed: {e}", "traceback": tb})
+
+@app.get("/agentz")
+def agentz():
+    return _agent_introspection()
+
+    
+# api/app_api.py  (Part 4/5)
+
+@app.post("/recommend")
+def make_recommend(body: RecommendIn):
+    """
+    Core recommendation endpoint.
+    - Validates dataset files exist
+    - Optionally validates FAISS index if use_faiss=true
+    - Calls service.recommender.recommend_for_user
+    - Enriches with catalog info
+    - Normalizes JSON (numpy/pandas safe)
+    - NEW: adds 'metrics' block (hit@k, ndcg@k, memory_mb) if found
+    """
+    try:
+        # --- Preflight dataset/file check (mirrors /users) ---
+        proc = get_processed_path(body.dataset)
+        user_fp = proc / "user_text_emb.parquet"
+        if not user_fp.exists():
+            return JSONResponse(
+                status_code=400,
+                content={"detail": f"Unknown dataset '{body.dataset}' or missing '{user_fp.name}' in {proc}."},
+            )
+
+        # --- Build service config ---
+        cfg = RecommendConfig(
+            dataset=body.dataset,
+            user_id=str(body.user_id),
+            k=int(body.k),
+            fusion=body.fusion,
+            weights=FusionWeights(text=body.w_text, image=body.w_image, meta=body.w_meta),
+            alpha=body.alpha,                # legacy; ignored by service
+            use_faiss=body.use_faiss,
+            faiss_name=body.faiss_name,
+            exclude_seen=body.exclude_seen,
+        )
+
+        # --- Optional FAISS check (if explicit name given) ---
+        if cfg.use_faiss and cfg.faiss_name:
+            index_path = proc / "index" / f"items_{cfg.faiss_name}.faiss"
+            if not index_path.exists():
+                return JSONResponse(
+                    status_code=400,
+                    content={"detail": f"FAISS index not found: {index_path}. Build it or set use_faiss=false."},
+                )
+
+        # --- Call recommender service ---
+        out = recommend_for_user(cfg)
+
+        # Normalize list key
+        recs = out.get("results")
+        if recs is None:
+            recs = out.get("recommendations", [])
+        recs = list(recs or [])[: int(cfg.k)]
+
+        # Enrich & normalize
+        recs = _enrich_with_catalog(body.dataset, recs)
+        _normalize_categories_in_place(recs)
+
+        # Final coercions
+        for r in recs:
+            # rank
+            rn = r.get("rank_num")
+            if rn is not None:
+                try: r["rank"] = int(rn)
+                except Exception: r["rank"] = None
+            else:
+                rv = r.get("rank")
+                if isinstance(rv, str):
+                    m = re.search(r"[\d,]+", rv); r["rank"] = int(m.group(0).replace(",", "")) if m else None
+                elif isinstance(rv, (int, float)):
+                    try: r["rank"] = int(rv)
+                    except Exception: r["rank"] = None
+                else:
+                    r["rank"] = None
+
+            # price
+            v = r.get("price")
+            try:
+                rv = float(v) if v not in (None, "", "nan") else None
+                r["price"] = rv if (rv is None or math.isfinite(rv)) else None
+            except Exception:
+                r["price"] = None
+
+            # score
+            v = r.get("score")
+            try:
+                rv = float(v) if v not in (None, "", "nan") else None
+                r["score"] = rv if (rv is None or math.isfinite(rv)) else None
+            except Exception:
+                r["score"] = None
+
+            # image_url
+            v = r.get("image_url")
+            if isinstance(v, list):
+                r["image_url"] = next((u for u in v if isinstance(u, str) and u.strip()), None)
+            elif isinstance(v, str):
+                r["image_url"] = v.strip() or None
+            else:
+                r["image_url"] = None
+
+            # guard
+            cats = r.get("categories")
+            if isinstance(cats, list) and len(cats) == 1 and isinstance(cats[0], str) and cats[0].strip() == "[]":
+                r["categories"] = []
+
+        # Put normalized list back
+        out["results"] = _to_jsonable(recs)
+        out["recommendations"] = _to_jsonable(recs)
+
+        # ---------- NEW: attach metrics if we can find them ----------
+        try:
+            metrics = _latest_metrics_for(
+                dataset=body.dataset,
+                fusion=body.fusion,
+                k=int(body.k),
+                faiss_name=body.faiss_name,
+            )
+            if metrics:
+                out["metrics"] = metrics
+        except Exception:
+            # swallow — metrics are optional
+            pass
+
+        return JSONResponse(content=_to_jsonable(out))
+
+    except FileNotFoundError:
+        return JSONResponse(status_code=400, content={"detail": f"Dataset '{body.dataset}' not found or incomplete."})
+    except ValueError as e:
+        return JSONResponse(status_code=400, content={"detail": f"/recommend failed: {e}"})
+    except Exception as e:
+        tb = traceback.format_exc(limit=5)
+        return JSONResponse(status_code=500, content={"detail": f"/recommend failed: {e}", "traceback": tb})
+    
+# api/app_api.py  (Part 5/5)
+
+@app.post("/chat_recommend")
+def chat_recommend(body: ChatIn):
+    # Tolerant parse of messages
+    msgs: List[Dict[str, str]] = []
+    for m in body.messages:
+        if isinstance(m, dict):
+            msgs.append({"role": m.get("role"), "content": m.get("content")})
+        else:
+            d = m.model_dump() if hasattr(m, "model_dump") else m.dict()
+            msgs.append({"role": d.get("role"), "content": d.get("content")})
+
+    try:
+        out: Dict[str, Any] = {"reply": "", "recommendations": []}
+        recs: List[Dict[str, Any]] = []
+
+        # 1) Ask the agent
+        if hasattr(CHAT_AGENT, "reply"):
+            candidate_kwargs = {
+                "messages": msgs,
+                "dataset": body.dataset,
+                "user_id": body.user_id,
+                "k": body.k,
+                "use_faiss": body.use_faiss,
+                "faiss_name": body.faiss_name,
+            }
+            sig = inspect.signature(CHAT_AGENT.reply)
+            allowed = set(sig.parameters.keys())
+            safe_kwargs = {k: v for k, v in candidate_kwargs.items() if k in allowed}
+            agent_out = CHAT_AGENT.reply(**safe_kwargs)
+            if isinstance(agent_out, dict):
+                out.update(agent_out)
+                recs = agent_out.get("recommendations") or []
+            else:
+                out["reply"] = str(agent_out) if agent_out is not None else ""
+
+        recs = [dict(r) if not isinstance(r, dict) else r for r in (recs or [])]
+
+        # 2) Fallback
+        if not recs:
+            cfg = RecommendConfig(
+                dataset=body.dataset or "beauty",
+                user_id=str(body.user_id or ""),
+                k=int(body.k or 5),
+                fusion="weighted",
+                weights=FusionWeights(text=1.0, image=0.2, meta=0.2),
+                alpha=None,
+                use_faiss=False,
+                faiss_name=None,
+                exclude_seen=True,
+            )
+            try:
+                reco_out = recommend_for_user(cfg)
+                recs = reco_out.get("results") or reco_out.get("recommendations") or []
+                recs = [dict(r) if not isinstance(r, dict) else r for r in recs]
+                if not out.get("reply"):
+                    out["reply"] = "Here are some items you might like."
+            except Exception:
+                pass
+
+        # 3) Enrich + normalize (like /recommend)
+        ds = body.dataset or "beauty"
+        recs = _enrich_with_catalog(ds, recs)
+        _normalize_categories_in_place(recs)
+
+        for r in recs:
+            # price
+            v = r.get("price")
+            try:
+                rv = float(v) if v not in (None, "", "nan") else None
+                r["price"] = rv if (rv is None or math.isfinite(rv)) else None
+            except Exception:
+                r["price"] = None
+            # score
+            v = r.get("score")
+            try:
+                rv = float(v) if v not in (None, "", "nan") else None
+                r["score"] = rv if (rv is None or math.isfinite(rv)) else None
+            except Exception:
+                r["score"] = None
+            # rank
+            rn = r.get("rank_num")
+            if rn is not None:
+                try: r["rank"] = int(rn)
+                except Exception: r["rank"] = None
+            else:
+                rv = r.get("rank")
+                if isinstance(rv, str):
+                    m = re.search(r"[\d,]+", rv); r["rank"] = int(m.group(0).replace(",", "")) if m else None
+                elif isinstance(rv, (int, float)):
+                    try: r["rank"] = int(rv)
+                    except Exception: r["rank"] = None
+                else:
+                    r["rank"] = None
+            # image_url (string)
+            v = r.get("image_url")
+            if isinstance(v, list):
+                r["image_url"] = next((u for u in v if isinstance(u, str) and u.strip()), None)
+            elif isinstance(v, str):
+                r["image_url"] = v.strip() or None
+            else:
+                r["image_url"] = None
+
+        # 4) Lightweight chat constraints (budget/keyword) — unchanged
+        last = (msgs[-1]["content"] if msgs else "") or ""
+        cap = _parse_price_cap(last)
+        kw  = _parse_keyword(last)
+        if cap is not None:
+            recs = [r for r in recs if (r.get("price") is not None and r["price"] <= cap)]
+        if kw:
+            lowkw = kw.lower()
+            def _matches(item: Dict[str, Any]) -> bool:
+                fields = [str(item.get("brand") or ""), str(item.get("item_id") or "")]
+                fields.extend(item.get("categories") or [])
+                return lowkw in " ".join(fields).lower()
+            filtered = [r for r in recs if _matches(r)]
+            if filtered:
+                recs = filtered
+
+        out["recommendations"] = recs
+        out["results"] = recs
+
+        # ---------- NEW: attach metrics (optional best-effort) ----------
+        try:
+            metrics = _latest_metrics_for(
+                dataset=ds,
+                fusion="weighted",   # chat uses weighted defaults for now
+                k=int(body.k or 5),
+                faiss_name=body.faiss_name,
+            )
+            if metrics:
+                out["metrics"] = metrics
+        except Exception:
+            pass
+
+        return JSONResponse(content=_to_jsonable(out))
+
+    except Exception as e:
+        tb = traceback.format_exc(limit=5)
+        return JSONResponse(status_code=400, content={"detail": f"/chat_recommend failed: {e}", "traceback": tb})
+
+# =========================
+# Health & root
+# =========================
+@app.get("/healthz")
+def healthz():
+    return {"ok": True, "service": "MMR-Agentic-CoVE API", "version": getattr(app, "version", None) or "unknown"}
+
+@app.get("/")
+def root():
+    return {"ok": True, "service": "MMR-Agentic-CoVE API"}

app.py

@@ -1 +1,3 @@
-from api.app_api import app
+# app.py
+
+from api.app_api import app  # This is your FastAPI instance

data/processed/beauty/cove/.ipynb_checkpoints/datamaps-checkpoint.json

@@ -0,0 +1,184 @@
+{
+  "token_prefix": "<|I_",
+  "token_suffix": "|>",
+  "item_to_token": {
+    "B0009RF9DW": "<|I_B0009RF9DW|>",
+    "B000FI4S1E": "<|I_B000FI4S1E|>",
+    "B000URXP6E": "<|I_B000URXP6E|>",
+    "B0012Y0ZG2": "<|I_B0012Y0ZG2|>",
+    "B00W259T7G": "<|I_B00W259T7G|>",
+    "B000VV1YOY": "<|I_B000VV1YOY|>",
+    "B001LNODUS": "<|I_B001LNODUS|>",
+    "B019FWRG3C": "<|I_B019FWRG3C|>",
+    "B00006L9LC": "<|I_B00006L9LC|>",
+    "B001OHV1H4": "<|I_B001OHV1H4|>",
+    "B00VG1AV5Q": "<|I_B00VG1AV5Q|>",
+    "B01DLR9IDI": "<|I_B01DLR9IDI|>",
+    "B00CQ0LN80": "<|I_B00CQ0LN80|>",
+    "B00HLXEXDO": "<|I_B00HLXEXDO|>",
+    "B000X7ST9Y": "<|I_B000X7ST9Y|>",
+    "B001E5PLCM": "<|I_B001E5PLCM|>",
+    "B00DY59MB6": "<|I_B00DY59MB6|>",
+    "B00JF2GVWK": "<|I_B00JF2GVWK|>",
+    "B00L1I1VMG": "<|I_B00L1I1VMG|>",
+    "B0010ZBORW": "<|I_B0010ZBORW|>",
+    "B00QXW95Q4": "<|I_B00QXW95Q4|>",
+    "B006IB5T4W": "<|I_B006IB5T4W|>",
+    "B01E7UKR38": "<|I_B01E7UKR38|>",
+    "B002GP80EU": "<|I_B002GP80EU|>",
+    "B00N2WQ2IW": "<|I_B00N2WQ2IW|>",
+    "B00AKP21KM": "<|I_B00AKP21KM|>",
+    "B00RZYW4RG": "<|I_B00RZYW4RG|>",
+    "B000GLRREU": "<|I_B000GLRREU|>",
+    "B000FOI48G": "<|I_B000FOI48G|>",
+    "B016V8YWBC": "<|I_B016V8YWBC|>",
+    "B0013NB7DW": "<|I_B0013NB7DW|>",
+    "B019809F9Y": "<|I_B019809F9Y|>",
+    "B000NKJIXM": "<|I_B000NKJIXM|>",
+    "B00EYZY6LQ": "<|I_B00EYZY6LQ|>",
+    "B001E96LUO": "<|I_B001E96LUO|>",
+    "B000LIBUBY": "<|I_B000LIBUBY|>",
+    "B000VUXCGI": "<|I_B000VUXCGI|>",
+    "B00IJHY54S": "<|I_B00IJHY54S|>",
+    "B00MTR49IG": "<|I_B00MTR49IG|>",
+    "B00UWB35UY": "<|I_B00UWB35UY|>",
+    "B000PKKAGO": "<|I_B000PKKAGO|>",
+    "B0017TZD7S": "<|I_B0017TZD7S|>",
+    "B008YQM4A6": "<|I_B008YQM4A6|>",
+    "B019LAI4HU": "<|I_B019LAI4HU|>",
+    "B00CZH3K1C": "<|I_B00CZH3K1C|>",
+    "B00VARTPKS": "<|I_B00VARTPKS|>",
+    "B001ET7FZE": "<|I_B001ET7FZE|>",
+    "B01BNEYGQU": "<|I_B01BNEYGQU|>",
+    "B007R6UXNY": "<|I_B007R6UXNY|>",
+    "B004KEJ65C": "<|I_B004KEJ65C|>",
+    "B004CALFE4": "<|I_B004CALFE4|>",
+    "B001F51RAG": "<|I_B001F51RAG|>",
+    "B00NT0AR7E": "<|I_B00NT0AR7E|>",
+    "B00B9V9ASM": "<|I_B00B9V9ASM|>",
+    "B00112DRHY": "<|I_B00112DRHY|>",
+    "B019V2KYZS": "<|I_B019V2KYZS|>",
+    "B0002JHI1I": "<|I_B0002JHI1I|>",
+    "B0006O10P4": "<|I_B0006O10P4|>",
+    "B00120VWTK": "<|I_B00120VWTK|>",
+    "B01DKQAXC0": "<|I_B01DKQAXC0|>",
+    "B000V5Z4J6": "<|I_B000V5Z4J6|>",
+    "B000WYJTZG": "<|I_B000WYJTZG|>",
+    "B00157OBRU": "<|I_B00157OBRU|>",
+    "B0012XPRO8": "<|I_B0012XPRO8|>",
+    "B00GHJOM2U": "<|I_B00GHJOM2U|>",
+    "B002RZZXYE": "<|I_B002RZZXYE|>",
+    "B00MGK9Z8U": "<|I_B00MGK9Z8U|>",
+    "B000W0C07Y": "<|I_B000W0C07Y|>",
+    "B00021DJ32": "<|I_B00021DJ32|>",
+    "B006WYJM8Y": "<|I_B006WYJM8Y|>",
+    "B00EF1QRMU": "<|I_B00EF1QRMU|>",
+    "B00BSE3III": "<|I_B00BSE3III|>",
+    "B0000530HU": "<|I_B0000530HU|>",
+    "B000FTYALG": "<|I_B000FTYALG|>",
+    "B000YB70PS": "<|I_B000YB70PS|>",
+    "B00155Z6V2": "<|I_B00155Z6V2|>",
+    "B0091OCA86": "<|I_B0091OCA86|>",
+    "B00B7V273E": "<|I_B00B7V273E|>",
+    "B000WR2HB6": "<|I_B000WR2HB6|>",
+    "B0014SQQ3M": "<|I_B0014SQQ3M|>",
+    "B0011FYB5I": "<|I_B0011FYB5I|>",
+    "B001QY8QXM": "<|I_B001QY8QXM|>",
+    "B007V6JNE0": "<|I_B007V6JNE0|>",
+    "B000X2FPXC": "<|I_B000X2FPXC|>",
+    "B00126LYJM": "<|I_B00126LYJM|>"
+  },
+  "token_to_item": {
+    "<|I_B0009RF9DW|>": "B0009RF9DW",
+    "<|I_B000FI4S1E|>": "B000FI4S1E",
+    "<|I_B000URXP6E|>": "B000URXP6E",
+    "<|I_B0012Y0ZG2|>": "B0012Y0ZG2",
+    "<|I_B00W259T7G|>": "B00W259T7G",
+    "<|I_B000VV1YOY|>": "B000VV1YOY",
+    "<|I_B001LNODUS|>": "B001LNODUS",
+    "<|I_B019FWRG3C|>": "B019FWRG3C",
+    "<|I_B00006L9LC|>": "B00006L9LC",
+    "<|I_B001OHV1H4|>": "B001OHV1H4",
+    "<|I_B00VG1AV5Q|>": "B00VG1AV5Q",
+    "<|I_B01DLR9IDI|>": "B01DLR9IDI",
+    "<|I_B00CQ0LN80|>": "B00CQ0LN80",
+    "<|I_B00HLXEXDO|>": "B00HLXEXDO",
+    "<|I_B000X7ST9Y|>": "B000X7ST9Y",
+    "<|I_B001E5PLCM|>": "B001E5PLCM",
+    "<|I_B00DY59MB6|>": "B00DY59MB6",
+    "<|I_B00JF2GVWK|>": "B00JF2GVWK",
+    "<|I_B00L1I1VMG|>": "B00L1I1VMG",
+    "<|I_B0010ZBORW|>": "B0010ZBORW",
+    "<|I_B00QXW95Q4|>": "B00QXW95Q4",
+    "<|I_B006IB5T4W|>": "B006IB5T4W",
+    "<|I_B01E7UKR38|>": "B01E7UKR38",
+    "<|I_B002GP80EU|>": "B002GP80EU",
+    "<|I_B00N2WQ2IW|>": "B00N2WQ2IW",
+    "<|I_B00AKP21KM|>": "B00AKP21KM",
+    "<|I_B00RZYW4RG|>": "B00RZYW4RG",
+    "<|I_B000GLRREU|>": "B000GLRREU",
+    "<|I_B000FOI48G|>": "B000FOI48G",
+    "<|I_B016V8YWBC|>": "B016V8YWBC",
+    "<|I_B0013NB7DW|>": "B0013NB7DW",
+    "<|I_B019809F9Y|>": "B019809F9Y",
+    "<|I_B000NKJIXM|>": "B000NKJIXM",
+    "<|I_B00EYZY6LQ|>": "B00EYZY6LQ",
+    "<|I_B001E96LUO|>": "B001E96LUO",
+    "<|I_B000LIBUBY|>": "B000LIBUBY",
+    "<|I_B000VUXCGI|>": "B000VUXCGI",
+    "<|I_B00IJHY54S|>": "B00IJHY54S",
+    "<|I_B00MTR49IG|>": "B00MTR49IG",
+    "<|I_B00UWB35UY|>": "B00UWB35UY",
+    "<|I_B000PKKAGO|>": "B000PKKAGO",
+    "<|I_B0017TZD7S|>": "B0017TZD7S",
+    "<|I_B008YQM4A6|>": "B008YQM4A6",
+    "<|I_B019LAI4HU|>": "B019LAI4HU",
+    "<|I_B00CZH3K1C|>": "B00CZH3K1C",
+    "<|I_B00VARTPKS|>": "B00VARTPKS",
+    "<|I_B001ET7FZE|>": "B001ET7FZE",
+    "<|I_B01BNEYGQU|>": "B01BNEYGQU",
+    "<|I_B007R6UXNY|>": "B007R6UXNY",
+    "<|I_B004KEJ65C|>": "B004KEJ65C",
+    "<|I_B004CALFE4|>": "B004CALFE4",
+    "<|I_B001F51RAG|>": "B001F51RAG",
+    "<|I_B00NT0AR7E|>": "B00NT0AR7E",
+    "<|I_B00B9V9ASM|>": "B00B9V9ASM",
+    "<|I_B00112DRHY|>": "B00112DRHY",
+    "<|I_B019V2KYZS|>": "B019V2KYZS",
+    "<|I_B0002JHI1I|>": "B0002JHI1I",
+    "<|I_B0006O10P4|>": "B0006O10P4",
+    "<|I_B00120VWTK|>": "B00120VWTK",
+    "<|I_B01DKQAXC0|>": "B01DKQAXC0",
+    "<|I_B000V5Z4J6|>": "B000V5Z4J6",
+    "<|I_B000WYJTZG|>": "B000WYJTZG",
+    "<|I_B00157OBRU|>": "B00157OBRU",
+    "<|I_B0012XPRO8|>": "B0012XPRO8",
+    "<|I_B00GHJOM2U|>": "B00GHJOM2U",
+    "<|I_B002RZZXYE|>": "B002RZZXYE",
+    "<|I_B00MGK9Z8U|>": "B00MGK9Z8U",
+    "<|I_B000W0C07Y|>": "B000W0C07Y",
+    "<|I_B00021DJ32|>": "B00021DJ32",
+    "<|I_B006WYJM8Y|>": "B006WYJM8Y",
+    "<|I_B00EF1QRMU|>": "B00EF1QRMU",
+    "<|I_B00BSE3III|>": "B00BSE3III",
+    "<|I_B0000530HU|>": "B0000530HU",
+    "<|I_B000FTYALG|>": "B000FTYALG",
+    "<|I_B000YB70PS|>": "B000YB70PS",
+    "<|I_B00155Z6V2|>": "B00155Z6V2",
+    "<|I_B0091OCA86|>": "B0091OCA86",
+    "<|I_B00B7V273E|>": "B00B7V273E",
+    "<|I_B000WR2HB6|>": "B000WR2HB6",
+    "<|I_B0014SQQ3M|>": "B0014SQQ3M",
+    "<|I_B0011FYB5I|>": "B0011FYB5I",
+    "<|I_B001QY8QXM|>": "B001QY8QXM",
+    "<|I_B007V6JNE0|>": "B007V6JNE0",
+    "<|I_B000X2FPXC|>": "B000X2FPXC",
+    "<|I_B00126LYJM|>": "B00126LYJM"
+  },
+  "num_items": 85,
+  "num_users": 991,
+  "min_seq_len": 2,
+  "max_seq_len": 200,
+  "seq_count": 991,
+  "avg_seq_len": 5.316851664984863
+}

data/processed/beauty/cove/.ipynb_checkpoints/item_token_map-checkpoint.csv

@@ -0,0 +1,86 @@
+item_id,token
+B0009RF9DW,<|I_B0009RF9DW|>
+B000FI4S1E,<|I_B000FI4S1E|>
+B000URXP6E,<|I_B000URXP6E|>
+B0012Y0ZG2,<|I_B0012Y0ZG2|>
+B00W259T7G,<|I_B00W259T7G|>
+B000VV1YOY,<|I_B000VV1YOY|>
+B001LNODUS,<|I_B001LNODUS|>
+B019FWRG3C,<|I_B019FWRG3C|>
+B00006L9LC,<|I_B00006L9LC|>
+B001OHV1H4,<|I_B001OHV1H4|>
+B00VG1AV5Q,<|I_B00VG1AV5Q|>
+B01DLR9IDI,<|I_B01DLR9IDI|>
+B00CQ0LN80,<|I_B00CQ0LN80|>
+B00HLXEXDO,<|I_B00HLXEXDO|>
+B000X7ST9Y,<|I_B000X7ST9Y|>
+B001E5PLCM,<|I_B001E5PLCM|>
+B00DY59MB6,<|I_B00DY59MB6|>
+B00JF2GVWK,<|I_B00JF2GVWK|>
+B00L1I1VMG,<|I_B00L1I1VMG|>
+B0010ZBORW,<|I_B0010ZBORW|>
+B00QXW95Q4,<|I_B00QXW95Q4|>
+B006IB5T4W,<|I_B006IB5T4W|>
+B01E7UKR38,<|I_B01E7UKR38|>
+B002GP80EU,<|I_B002GP80EU|>
+B00N2WQ2IW,<|I_B00N2WQ2IW|>
+B00AKP21KM,<|I_B00AKP21KM|>
+B00RZYW4RG,<|I_B00RZYW4RG|>
+B000GLRREU,<|I_B000GLRREU|>
+B000FOI48G,<|I_B000FOI48G|>
+B016V8YWBC,<|I_B016V8YWBC|>
+B0013NB7DW,<|I_B0013NB7DW|>
+B019809F9Y,<|I_B019809F9Y|>
+B000NKJIXM,<|I_B000NKJIXM|>
+B00EYZY6LQ,<|I_B00EYZY6LQ|>
+B001E96LUO,<|I_B001E96LUO|>
+B000LIBUBY,<|I_B000LIBUBY|>
+B000VUXCGI,<|I_B000VUXCGI|>
+B00IJHY54S,<|I_B00IJHY54S|>
+B00MTR49IG,<|I_B00MTR49IG|>
+B00UWB35UY,<|I_B00UWB35UY|>
+B000PKKAGO,<|I_B000PKKAGO|>
+B0017TZD7S,<|I_B0017TZD7S|>
+B008YQM4A6,<|I_B008YQM4A6|>
+B019LAI4HU,<|I_B019LAI4HU|>
+B00CZH3K1C,<|I_B00CZH3K1C|>
+B00VARTPKS,<|I_B00VARTPKS|>
+B001ET7FZE,<|I_B001ET7FZE|>
+B01BNEYGQU,<|I_B01BNEYGQU|>
+B007R6UXNY,<|I_B007R6UXNY|>
+B004KEJ65C,<|I_B004KEJ65C|>
+B004CALFE4,<|I_B004CALFE4|>
+B001F51RAG,<|I_B001F51RAG|>
+B00NT0AR7E,<|I_B00NT0AR7E|>
+B00B9V9ASM,<|I_B00B9V9ASM|>
+B00112DRHY,<|I_B00112DRHY|>
+B019V2KYZS,<|I_B019V2KYZS|>
+B0002JHI1I,<|I_B0002JHI1I|>
+B0006O10P4,<|I_B0006O10P4|>
+B00120VWTK,<|I_B00120VWTK|>
+B01DKQAXC0,<|I_B01DKQAXC0|>
+B000V5Z4J6,<|I_B000V5Z4J6|>
+B000WYJTZG,<|I_B000WYJTZG|>
+B00157OBRU,<|I_B00157OBRU|>
+B0012XPRO8,<|I_B0012XPRO8|>
+B00GHJOM2U,<|I_B00GHJOM2U|>
+B002RZZXYE,<|I_B002RZZXYE|>
+B00MGK9Z8U,<|I_B00MGK9Z8U|>
+B000W0C07Y,<|I_B000W0C07Y|>
+B00021DJ32,<|I_B00021DJ32|>
+B006WYJM8Y,<|I_B006WYJM8Y|>
+B00EF1QRMU,<|I_B00EF1QRMU|>
+B00BSE3III,<|I_B00BSE3III|>
+B0000530HU,<|I_B0000530HU|>
+B000FTYALG,<|I_B000FTYALG|>
+B000YB70PS,<|I_B000YB70PS|>
+B00155Z6V2,<|I_B00155Z6V2|>
+B0091OCA86,<|I_B0091OCA86|>
+B00B7V273E,<|I_B00B7V273E|>
+B000WR2HB6,<|I_B000WR2HB6|>
+B0014SQQ3M,<|I_B0014SQQ3M|>
+B0011FYB5I,<|I_B0011FYB5I|>
+B001QY8QXM,<|I_B001QY8QXM|>
+B007V6JNE0,<|I_B007V6JNE0|>
+B000X2FPXC,<|I_B000X2FPXC|>
+B00126LYJM,<|I_B00126LYJM|>

data/processed/beauty/cove/.ipynb_checkpoints/sequential_data-checkpoint.txt

@@ -0,0 +1,991 @@
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00W259T7G|> <|I_B000VV1YOY|> <|I_B000VV1YOY|> <|I_B001LNODUS|> <|I_B019FWRG3C|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00VG1AV5Q|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|>
+<|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B00CQ0LN80|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B00HLXEXDO|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00RZYW4RG|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B01E7UKR38|> <|I_B0010ZBORW|> <|I_B0010ZBORW|> <|I_B001LNODUS|> <|I_B019FWRG3C|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B004KEJ65C|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00JF2GVWK|>
+<|I_B001OHV1H4|> <|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B00IJHY54S|>
+<|I_B00W259T7G|> <|I_B001LNODUS|> <|I_B019FWRG3C|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00BSE3III|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B0010ZBORW|> <|I_B0010ZBORW|> <|I_B00W259T7G|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0013NB7DW|> <|I_B0013NB7DW|> <|I_B0013NB7DW|> <|I_B0013NB7DW|> <|I_B019809F9Y|> <|I_B019809F9Y|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00RZYW4RG|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00006L9LC|>
+<|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00RZYW4RG|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B0009RF9DW|> <|I_B000FI4S1E|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00L1I1VMG|> <|I_B001ET7FZE|> <|I_B01BNEYGQU|> <|I_B0010ZBORW|> <|I_B0010ZBORW|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00QXW95Q4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>

data/processed/beauty/cove/config.json

@@ -0,0 +1,3 @@
+{
+  "model_type": "gpt2"
+}

data/processed/beauty/cove/datamaps.json

@@ -0,0 +1,358 @@
+{
+  "token_prefix": "<|I_",
+  "token_suffix": "|>",
+  "item_to_token": {
+    "B0009RF9DW": "<|I_B0009RF9DW|>",
+    "B000FI4S1E": "<|I_B000FI4S1E|>",
+    "B000URXP6E": "<|I_B000URXP6E|>",
+    "B0012Y0ZG2": "<|I_B0012Y0ZG2|>",
+    "B00W259T7G": "<|I_B00W259T7G|>",
+    "B000VV1YOY": "<|I_B000VV1YOY|>",
+    "B001LNODUS": "<|I_B001LNODUS|>",
+    "B019FWRG3C": "<|I_B019FWRG3C|>",
+    "B00006L9LC": "<|I_B00006L9LC|>",
+    "B001OHV1H4": "<|I_B001OHV1H4|>",
+    "B00VG1AV5Q": "<|I_B00VG1AV5Q|>",
+    "B01DLR9IDI": "<|I_B01DLR9IDI|>",
+    "B00CQ0LN80": "<|I_B00CQ0LN80|>",
+    "B00HLXEXDO": "<|I_B00HLXEXDO|>",
+    "B000X7ST9Y": "<|I_B000X7ST9Y|>",
+    "B001E5PLCM": "<|I_B001E5PLCM|>",
+    "B00DY59MB6": "<|I_B00DY59MB6|>",
+    "B00JF2GVWK": "<|I_B00JF2GVWK|>",
+    "B00L1I1VMG": "<|I_B00L1I1VMG|>",
+    "B0010ZBORW": "<|I_B0010ZBORW|>",
+    "B00QXW95Q4": "<|I_B00QXW95Q4|>",
+    "B006IB5T4W": "<|I_B006IB5T4W|>",
+    "B01E7UKR38": "<|I_B01E7UKR38|>",
+    "B002GP80EU": "<|I_B002GP80EU|>",
+    "B00N2WQ2IW": "<|I_B00N2WQ2IW|>",
+    "B00AKP21KM": "<|I_B00AKP21KM|>",
+    "B00RZYW4RG": "<|I_B00RZYW4RG|>",
+    "B000GLRREU": "<|I_B000GLRREU|>",
+    "B000FOI48G": "<|I_B000FOI48G|>",
+    "B016V8YWBC": "<|I_B016V8YWBC|>",
+    "B0013NB7DW": "<|I_B0013NB7DW|>",
+    "B019809F9Y": "<|I_B019809F9Y|>",
+    "B000NKJIXM": "<|I_B000NKJIXM|>",
+    "B00EYZY6LQ": "<|I_B00EYZY6LQ|>",
+    "B001E96LUO": "<|I_B001E96LUO|>",
+    "B000LIBUBY": "<|I_B000LIBUBY|>",
+    "B000VUXCGI": "<|I_B000VUXCGI|>",
+    "B00IJHY54S": "<|I_B00IJHY54S|>",
+    "B00MTR49IG": "<|I_B00MTR49IG|>",
+    "B00UWB35UY": "<|I_B00UWB35UY|>",
+    "B000PKKAGO": "<|I_B000PKKAGO|>",
+    "B0017TZD7S": "<|I_B0017TZD7S|>",
+    "B008YQM4A6": "<|I_B008YQM4A6|>",
+    "B019LAI4HU": "<|I_B019LAI4HU|>",
+    "B00CZH3K1C": "<|I_B00CZH3K1C|>",
+    "B00VARTPKS": "<|I_B00VARTPKS|>",
+    "B001ET7FZE": "<|I_B001ET7FZE|>",
+    "B01BNEYGQU": "<|I_B01BNEYGQU|>",
+    "B007R6UXNY": "<|I_B007R6UXNY|>",
+    "B004KEJ65C": "<|I_B004KEJ65C|>",
+    "B004CALFE4": "<|I_B004CALFE4|>",
+    "B001F51RAG": "<|I_B001F51RAG|>",
+    "B00NT0AR7E": "<|I_B00NT0AR7E|>",
+    "B00B9V9ASM": "<|I_B00B9V9ASM|>",
+    "B00112DRHY": "<|I_B00112DRHY|>",
+    "B019V2KYZS": "<|I_B019V2KYZS|>",
+    "B0002JHI1I": "<|I_B0002JHI1I|>",
+    "B0006O10P4": "<|I_B0006O10P4|>",
+    "B00120VWTK": "<|I_B00120VWTK|>",
+    "B01DKQAXC0": "<|I_B01DKQAXC0|>",
+    "B000V5Z4J6": "<|I_B000V5Z4J6|>",
+    "B000WYJTZG": "<|I_B000WYJTZG|>",
+    "B00157OBRU": "<|I_B00157OBRU|>",
+    "B0012XPRO8": "<|I_B0012XPRO8|>",
+    "B00GHJOM2U": "<|I_B00GHJOM2U|>",
+    "B002RZZXYE": "<|I_B002RZZXYE|>",
+    "B00MGK9Z8U": "<|I_B00MGK9Z8U|>",
+    "B000W0C07Y": "<|I_B000W0C07Y|>",
+    "B00021DJ32": "<|I_B00021DJ32|>",
+    "B006WYJM8Y": "<|I_B006WYJM8Y|>",
+    "B00EF1QRMU": "<|I_B00EF1QRMU|>",
+    "B00BSE3III": "<|I_B00BSE3III|>",
+    "B0000530HU": "<|I_B0000530HU|>",
+    "B000FTYALG": "<|I_B000FTYALG|>",
+    "B000YB70PS": "<|I_B000YB70PS|>",
+    "B00155Z6V2": "<|I_B00155Z6V2|>",
+    "B0091OCA86": "<|I_B0091OCA86|>",
+    "B00B7V273E": "<|I_B00B7V273E|>",
+    "B000WR2HB6": "<|I_B000WR2HB6|>",
+    "B0014SQQ3M": "<|I_B0014SQQ3M|>",
+    "B0011FYB5I": "<|I_B0011FYB5I|>",
+    "B001QY8QXM": "<|I_B001QY8QXM|>",
+    "B007V6JNE0": "<|I_B007V6JNE0|>",
+    "B000X2FPXC": "<|I_B000X2FPXC|>",
+    "B00126LYJM": "<|I_B00126LYJM|>"
+  },
+  "token_to_item": {
+    "<|I_B0009RF9DW|>": "B0009RF9DW",
+    "<|I_B000FI4S1E|>": "B000FI4S1E",
+    "<|I_B000URXP6E|>": "B000URXP6E",
+    "<|I_B0012Y0ZG2|>": "B0012Y0ZG2",
+    "<|I_B00W259T7G|>": "B00W259T7G",
+    "<|I_B000VV1YOY|>": "B000VV1YOY",
+    "<|I_B001LNODUS|>": "B001LNODUS",
+    "<|I_B019FWRG3C|>": "B019FWRG3C",
+    "<|I_B00006L9LC|>": "B00006L9LC",
+    "<|I_B001OHV1H4|>": "B001OHV1H4",
+    "<|I_B00VG1AV5Q|>": "B00VG1AV5Q",
+    "<|I_B01DLR9IDI|>": "B01DLR9IDI",
+    "<|I_B00CQ0LN80|>": "B00CQ0LN80",
+    "<|I_B00HLXEXDO|>": "B00HLXEXDO",
+    "<|I_B000X7ST9Y|>": "B000X7ST9Y",
+    "<|I_B001E5PLCM|>": "B001E5PLCM",
+    "<|I_B00DY59MB6|>": "B00DY59MB6",
+    "<|I_B00JF2GVWK|>": "B00JF2GVWK",
+    "<|I_B00L1I1VMG|>": "B00L1I1VMG",
+    "<|I_B0010ZBORW|>": "B0010ZBORW",
+    "<|I_B00QXW95Q4|>": "B00QXW95Q4",
+    "<|I_B006IB5T4W|>": "B006IB5T4W",
+    "<|I_B01E7UKR38|>": "B01E7UKR38",
+    "<|I_B002GP80EU|>": "B002GP80EU",
+    "<|I_B00N2WQ2IW|>": "B00N2WQ2IW",
+    "<|I_B00AKP21KM|>": "B00AKP21KM",
+    "<|I_B00RZYW4RG|>": "B00RZYW4RG",
+    "<|I_B000GLRREU|>": "B000GLRREU",
+    "<|I_B000FOI48G|>": "B000FOI48G",
+    "<|I_B016V8YWBC|>": "B016V8YWBC",
+    "<|I_B0013NB7DW|>": "B0013NB7DW",
+    "<|I_B019809F9Y|>": "B019809F9Y",
+    "<|I_B000NKJIXM|>": "B000NKJIXM",
+    "<|I_B00EYZY6LQ|>": "B00EYZY6LQ",
+    "<|I_B001E96LUO|>": "B001E96LUO",
+    "<|I_B000LIBUBY|>": "B000LIBUBY",
+    "<|I_B000VUXCGI|>": "B000VUXCGI",
+    "<|I_B00IJHY54S|>": "B00IJHY54S",
+    "<|I_B00MTR49IG|>": "B00MTR49IG",
+    "<|I_B00UWB35UY|>": "B00UWB35UY",
+    "<|I_B000PKKAGO|>": "B000PKKAGO",
+    "<|I_B0017TZD7S|>": "B0017TZD7S",
+    "<|I_B008YQM4A6|>": "B008YQM4A6",
+    "<|I_B019LAI4HU|>": "B019LAI4HU",
+    "<|I_B00CZH3K1C|>": "B00CZH3K1C",
+    "<|I_B00VARTPKS|>": "B00VARTPKS",
+    "<|I_B001ET7FZE|>": "B001ET7FZE",
+    "<|I_B01BNEYGQU|>": "B01BNEYGQU",
+    "<|I_B007R6UXNY|>": "B007R6UXNY",
+    "<|I_B004KEJ65C|>": "B004KEJ65C",
+    "<|I_B004CALFE4|>": "B004CALFE4",
+    "<|I_B001F51RAG|>": "B001F51RAG",
+    "<|I_B00NT0AR7E|>": "B00NT0AR7E",
+    "<|I_B00B9V9ASM|>": "B00B9V9ASM",
+    "<|I_B00112DRHY|>": "B00112DRHY",
+    "<|I_B019V2KYZS|>": "B019V2KYZS",
+    "<|I_B0002JHI1I|>": "B0002JHI1I",
+    "<|I_B0006O10P4|>": "B0006O10P4",
+    "<|I_B00120VWTK|>": "B00120VWTK",
+    "<|I_B01DKQAXC0|>": "B01DKQAXC0",
+    "<|I_B000V5Z4J6|>": "B000V5Z4J6",
+    "<|I_B000WYJTZG|>": "B000WYJTZG",
+    "<|I_B00157OBRU|>": "B00157OBRU",
+    "<|I_B0012XPRO8|>": "B0012XPRO8",
+    "<|I_B00GHJOM2U|>": "B00GHJOM2U",
+    "<|I_B002RZZXYE|>": "B002RZZXYE",
+    "<|I_B00MGK9Z8U|>": "B00MGK9Z8U",
+    "<|I_B000W0C07Y|>": "B000W0C07Y",
+    "<|I_B00021DJ32|>": "B00021DJ32",
+    "<|I_B006WYJM8Y|>": "B006WYJM8Y",
+    "<|I_B00EF1QRMU|>": "B00EF1QRMU",
+    "<|I_B00BSE3III|>": "B00BSE3III",
+    "<|I_B0000530HU|>": "B0000530HU",
+    "<|I_B000FTYALG|>": "B000FTYALG",
+    "<|I_B000YB70PS|>": "B000YB70PS",
+    "<|I_B00155Z6V2|>": "B00155Z6V2",
+    "<|I_B0091OCA86|>": "B0091OCA86",
+    "<|I_B00B7V273E|>": "B00B7V273E",
+    "<|I_B000WR2HB6|>": "B000WR2HB6",
+    "<|I_B0014SQQ3M|>": "B0014SQQ3M",
+    "<|I_B0011FYB5I|>": "B0011FYB5I",
+    "<|I_B001QY8QXM|>": "B001QY8QXM",
+    "<|I_B007V6JNE0|>": "B007V6JNE0",
+    "<|I_B000X2FPXC|>": "B000X2FPXC",
+    "<|I_B00126LYJM|>": "B00126LYJM"
+  },
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+    "B007V6JNE0": 82,
+    "B000X2FPXC": 83,
+    "B00126LYJM": 84
+  },
+  "idx2item": {
+    "0": "B0009RF9DW",
+    "1": "B000FI4S1E",
+    "2": "B000URXP6E",
+    "3": "B0012Y0ZG2",
+    "4": "B00W259T7G",
+    "5": "B000VV1YOY",
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+    "7": "B019FWRG3C",
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+    "14": "B000X7ST9Y",
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+    "16": "B00DY59MB6",
+    "17": "B00JF2GVWK",
+    "18": "B00L1I1VMG",
+    "19": "B0010ZBORW",
+    "20": "B00QXW95Q4",
+    "21": "B006IB5T4W",
+    "22": "B01E7UKR38",
+    "23": "B002GP80EU",
+    "24": "B00N2WQ2IW",
+    "25": "B00AKP21KM",
+    "26": "B00RZYW4RG",
+    "27": "B000GLRREU",
+    "28": "B000FOI48G",
+    "29": "B016V8YWBC",
+    "30": "B0013NB7DW",
+    "31": "B019809F9Y",
+    "32": "B000NKJIXM",
+    "33": "B00EYZY6LQ",
+    "34": "B001E96LUO",
+    "35": "B000LIBUBY",
+    "36": "B000VUXCGI",
+    "37": "B00IJHY54S",
+    "38": "B00MTR49IG",
+    "39": "B00UWB35UY",
+    "40": "B000PKKAGO",
+    "41": "B0017TZD7S",
+    "42": "B008YQM4A6",
+    "43": "B019LAI4HU",
+    "44": "B00CZH3K1C",
+    "45": "B00VARTPKS",
+    "46": "B001ET7FZE",
+    "47": "B01BNEYGQU",
+    "48": "B007R6UXNY",
+    "49": "B004KEJ65C",
+    "50": "B004CALFE4",
+    "51": "B001F51RAG",
+    "52": "B00NT0AR7E",
+    "53": "B00B9V9ASM",
+    "54": "B00112DRHY",
+    "55": "B019V2KYZS",
+    "56": "B0002JHI1I",
+    "57": "B0006O10P4",
+    "58": "B00120VWTK",
+    "59": "B01DKQAXC0",
+    "60": "B000V5Z4J6",
+    "61": "B000WYJTZG",
+    "62": "B00157OBRU",
+    "63": "B0012XPRO8",
+    "64": "B00GHJOM2U",
+    "65": "B002RZZXYE",
+    "66": "B00MGK9Z8U",
+    "67": "B000W0C07Y",
+    "68": "B00021DJ32",
+    "69": "B006WYJM8Y",
+    "70": "B00EF1QRMU",
+    "71": "B00BSE3III",
+    "72": "B0000530HU",
+    "73": "B000FTYALG",
+    "74": "B000YB70PS",
+    "75": "B00155Z6V2",
+    "76": "B0091OCA86",
+    "77": "B00B7V273E",
+    "78": "B000WR2HB6",
+    "79": "B0014SQQ3M",
+    "80": "B0011FYB5I",
+    "81": "B001QY8QXM",
+    "82": "B007V6JNE0",
+    "83": "B000X2FPXC",
+    "84": "B00126LYJM"
+  },
+  "num_items": 85,
+  "num_users": 991,
+  "min_seq_len": 2,
+  "max_seq_len": 200,
+  "seq_count": 991,
+  "avg_seq_len": 5.316851664984863
+}

data/processed/beauty/cove/full_item_ids.json

@@ -0,0 +1 @@
+["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]

data/processed/beauty/cove/item_token_map.csv

@@ -0,0 +1,86 @@
+item_id,token
+B0009RF9DW,<|I_B0009RF9DW|>
+B000FI4S1E,<|I_B000FI4S1E|>
+B000URXP6E,<|I_B000URXP6E|>
+B0012Y0ZG2,<|I_B0012Y0ZG2|>
+B00W259T7G,<|I_B00W259T7G|>
+B000VV1YOY,<|I_B000VV1YOY|>
+B001LNODUS,<|I_B001LNODUS|>
+B019FWRG3C,<|I_B019FWRG3C|>
+B00006L9LC,<|I_B00006L9LC|>
+B001OHV1H4,<|I_B001OHV1H4|>
+B00VG1AV5Q,<|I_B00VG1AV5Q|>
+B01DLR9IDI,<|I_B01DLR9IDI|>
+B00CQ0LN80,<|I_B00CQ0LN80|>
+B00HLXEXDO,<|I_B00HLXEXDO|>
+B000X7ST9Y,<|I_B000X7ST9Y|>
+B001E5PLCM,<|I_B001E5PLCM|>
+B00DY59MB6,<|I_B00DY59MB6|>
+B00JF2GVWK,<|I_B00JF2GVWK|>
+B00L1I1VMG,<|I_B00L1I1VMG|>
+B0010ZBORW,<|I_B0010ZBORW|>
+B00QXW95Q4,<|I_B00QXW95Q4|>
+B006IB5T4W,<|I_B006IB5T4W|>
+B01E7UKR38,<|I_B01E7UKR38|>
+B002GP80EU,<|I_B002GP80EU|>
+B00N2WQ2IW,<|I_B00N2WQ2IW|>
+B00AKP21KM,<|I_B00AKP21KM|>
+B00RZYW4RG,<|I_B00RZYW4RG|>
+B000GLRREU,<|I_B000GLRREU|>
+B000FOI48G,<|I_B000FOI48G|>
+B016V8YWBC,<|I_B016V8YWBC|>
+B0013NB7DW,<|I_B0013NB7DW|>
+B019809F9Y,<|I_B019809F9Y|>
+B000NKJIXM,<|I_B000NKJIXM|>
+B00EYZY6LQ,<|I_B00EYZY6LQ|>
+B001E96LUO,<|I_B001E96LUO|>
+B000LIBUBY,<|I_B000LIBUBY|>
+B000VUXCGI,<|I_B000VUXCGI|>
+B00IJHY54S,<|I_B00IJHY54S|>
+B00MTR49IG,<|I_B00MTR49IG|>
+B00UWB35UY,<|I_B00UWB35UY|>
+B000PKKAGO,<|I_B000PKKAGO|>
+B0017TZD7S,<|I_B0017TZD7S|>
+B008YQM4A6,<|I_B008YQM4A6|>
+B019LAI4HU,<|I_B019LAI4HU|>
+B00CZH3K1C,<|I_B00CZH3K1C|>
+B00VARTPKS,<|I_B00VARTPKS|>
+B001ET7FZE,<|I_B001ET7FZE|>
+B01BNEYGQU,<|I_B01BNEYGQU|>
+B007R6UXNY,<|I_B007R6UXNY|>
+B004KEJ65C,<|I_B004KEJ65C|>
+B004CALFE4,<|I_B004CALFE4|>
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+B00B9V9ASM,<|I_B00B9V9ASM|>
+B00112DRHY,<|I_B00112DRHY|>
+B019V2KYZS,<|I_B019V2KYZS|>
+B0002JHI1I,<|I_B0002JHI1I|>
+B0006O10P4,<|I_B0006O10P4|>
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+B01DKQAXC0,<|I_B01DKQAXC0|>
+B000V5Z4J6,<|I_B000V5Z4J6|>
+B000WYJTZG,<|I_B000WYJTZG|>
+B00157OBRU,<|I_B00157OBRU|>
+B0012XPRO8,<|I_B0012XPRO8|>
+B00GHJOM2U,<|I_B00GHJOM2U|>
+B002RZZXYE,<|I_B002RZZXYE|>
+B00MGK9Z8U,<|I_B00MGK9Z8U|>
+B000W0C07Y,<|I_B000W0C07Y|>
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+B00BSE3III,<|I_B00BSE3III|>
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+B00126LYJM,<|I_B00126LYJM|>

data/processed/beauty/cove/model/README.md

@@ -0,0 +1,207 @@
+---
+base_model: gpt2
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:gpt2
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

data/processed/beauty/cove/model/adapter_config.json

@@ -0,0 +1,38 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "gpt2",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": true,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "c_attn"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

data/processed/beauty/cove/model/added_tokens.json

@@ -0,0 +1,87 @@
+{
+  "<|I_B0000530HU|>": 50329,
+  "<|I_B00006L9LC|>": 50265,
+  "<|I_B00021DJ32|>": 50325,
+  "<|I_B0002JHI1I|>": 50313,
+  "<|I_B0006O10P4|>": 50314,
+  "<|I_B0009RF9DW|>": 50257,
+  "<|I_B000FI4S1E|>": 50258,
+  "<|I_B000FOI48G|>": 50285,
+  "<|I_B000FTYALG|>": 50330,
+  "<|I_B000GLRREU|>": 50284,
+  "<|I_B000LIBUBY|>": 50292,
+  "<|I_B000NKJIXM|>": 50289,
+  "<|I_B000PKKAGO|>": 50297,
+  "<|I_B000URXP6E|>": 50259,
+  "<|I_B000V5Z4J6|>": 50317,
+  "<|I_B000VUXCGI|>": 50293,
+  "<|I_B000VV1YOY|>": 50262,
+  "<|I_B000W0C07Y|>": 50324,
+  "<|I_B000WR2HB6|>": 50335,
+  "<|I_B000WYJTZG|>": 50318,
+  "<|I_B000X2FPXC|>": 50340,
+  "<|I_B000X7ST9Y|>": 50271,
+  "<|I_B000YB70PS|>": 50331,
+  "<|I_B0010ZBORW|>": 50276,
+  "<|I_B00112DRHY|>": 50311,
+  "<|I_B0011FYB5I|>": 50337,
+  "<|I_B00120VWTK|>": 50315,
+  "<|I_B00126LYJM|>": 50341,
+  "<|I_B0012XPRO8|>": 50320,
+  "<|I_B0012Y0ZG2|>": 50260,
+  "<|I_B0013NB7DW|>": 50287,
+  "<|I_B0014SQQ3M|>": 50336,
+  "<|I_B00155Z6V2|>": 50332,
+  "<|I_B00157OBRU|>": 50319,
+  "<|I_B0017TZD7S|>": 50298,
+  "<|I_B001E5PLCM|>": 50272,
+  "<|I_B001E96LUO|>": 50291,
+  "<|I_B001ET7FZE|>": 50303,
+  "<|I_B001F51RAG|>": 50308,
+  "<|I_B001LNODUS|>": 50263,
+  "<|I_B001OHV1H4|>": 50266,
+  "<|I_B001QY8QXM|>": 50338,
+  "<|I_B002GP80EU|>": 50280,
+  "<|I_B002RZZXYE|>": 50322,
+  "<|I_B004CALFE4|>": 50307,
+  "<|I_B004KEJ65C|>": 50306,
+  "<|I_B006IB5T4W|>": 50278,
+  "<|I_B006WYJM8Y|>": 50326,
+  "<|I_B007R6UXNY|>": 50305,
+  "<|I_B007V6JNE0|>": 50339,
+  "<|I_B008YQM4A6|>": 50299,
+  "<|I_B0091OCA86|>": 50333,
+  "<|I_B00AKP21KM|>": 50282,
+  "<|I_B00B7V273E|>": 50334,
+  "<|I_B00B9V9ASM|>": 50310,
+  "<|I_B00BSE3III|>": 50328,
+  "<|I_B00CQ0LN80|>": 50269,
+  "<|I_B00CZH3K1C|>": 50301,
+  "<|I_B00DY59MB6|>": 50273,
+  "<|I_B00EF1QRMU|>": 50327,
+  "<|I_B00EYZY6LQ|>": 50290,
+  "<|I_B00GHJOM2U|>": 50321,
+  "<|I_B00HLXEXDO|>": 50270,
+  "<|I_B00IJHY54S|>": 50294,
+  "<|I_B00JF2GVWK|>": 50274,
+  "<|I_B00L1I1VMG|>": 50275,
+  "<|I_B00MGK9Z8U|>": 50323,
+  "<|I_B00MTR49IG|>": 50295,
+  "<|I_B00N2WQ2IW|>": 50281,
+  "<|I_B00NT0AR7E|>": 50309,
+  "<|I_B00QXW95Q4|>": 50277,
+  "<|I_B00RZYW4RG|>": 50283,
+  "<|I_B00UWB35UY|>": 50296,
+  "<|I_B00VARTPKS|>": 50302,
+  "<|I_B00VG1AV5Q|>": 50267,
+  "<|I_B00W259T7G|>": 50261,
+  "<|I_B016V8YWBC|>": 50286,
+  "<|I_B019809F9Y|>": 50288,
+  "<|I_B019FWRG3C|>": 50264,
+  "<|I_B019LAI4HU|>": 50300,
+  "<|I_B019V2KYZS|>": 50312,
+  "<|I_B01BNEYGQU|>": 50304,
+  "<|I_B01DKQAXC0|>": 50316,
+  "<|I_B01DLR9IDI|>": 50268,
+  "<|I_B01E7UKR38|>": 50279
+}

data/processed/beauty/cove/model/config.json

@@ -0,0 +1,39 @@
+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 50256,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "transformers_version": "4.56.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

data/processed/beauty/cove/model/generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": [
+    50256
+  ],
+  "pad_token_id": 50256,
+  "transformers_version": "4.56.0"
+}

data/processed/beauty/cove/model/special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

data/processed/beauty/cove/model/tokenizer_config.json

@@ -0,0 +1,23 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

data/processed/beauty/cove/model_hashed/.ipynb_checkpoints/config-checkpoint.json

@@ -0,0 +1,39 @@
+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 50256,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "transformers_version": "4.56.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

data/processed/beauty/cove/model_hashed/README.md

@@ -0,0 +1,207 @@
+---
+base_model: gpt2
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:gpt2
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

data/processed/beauty/cove/model_hashed/adapter_config.json

@@ -0,0 +1,36 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "gpt2",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": true,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "c_attn"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

data/processed/beauty/cove/model_hashed/config.json

@@ -0,0 +1,39 @@
+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 50256,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "transformers_version": "4.56.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

data/processed/beauty/cove/model_hashed/generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": [
+    50256
+  ],
+  "pad_token_id": 50256,
+  "transformers_version": "4.56.0"
+}

data/processed/beauty/cove/model_hashed/special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

data/processed/beauty/cove/model_hashed/tokenizer_config.json

@@ -0,0 +1,23 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

data/processed/beauty/cove/query_texts.txt

@@ -0,0 +1,991 @@
+B0013NB7DW B0013NB7DW B019809F9Y B0000530HU
+B0000530HU B000FTYALG B0013NB7DW B0013NB7DW
+B0000530HU B000FTYALG B0013NB7DW B0013NB7DW
+B0013NB7DW B0013NB7DW B019809F9Y B0000530HU
+B00006L9LC B0012Y0ZG2 B0012Y0ZG2 B001OHV1H4
+B00006L9LC B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
+B00006L9LC B000URXP6E B0012Y0ZG2 B0012Y0ZG2
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data/processed/beauty/cove/sequential_data.txt

@@ -0,0 +1,991 @@
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00W259T7G|> <|I_B000VV1YOY|> <|I_B000VV1YOY|> <|I_B001LNODUS|> <|I_B019FWRG3C|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00VG1AV5Q|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
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+<|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B00CQ0LN80|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B000X2FPXC|> <|I_B000X2FPXC|> <|I_B00DY59MB6|> <|I_B01E7UKR38|> <|I_B00W259T7G|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B01E7UKR38|> <|I_B0010ZBORW|> <|I_B0010ZBORW|> <|I_B001LNODUS|> <|I_B019FWRG3C|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00JF2GVWK|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B00IJHY54S|>
+<|I_B00W259T7G|> <|I_B001LNODUS|> <|I_B019FWRG3C|> <|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B00W259T7G|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00BSE3III|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00W259T7G|> <|I_B00W259T7G|> <|I_B0010ZBORW|> <|I_B0010ZBORW|> <|I_B00W259T7G|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B004KEJ65C|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0013NB7DW|> <|I_B0013NB7DW|> <|I_B0013NB7DW|> <|I_B0013NB7DW|> <|I_B019809F9Y|> <|I_B019809F9Y|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00RZYW4RG|>
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+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00006L9LC|>
+<|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|> <|I_B01DLR9IDI|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00RZYW4RG|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B0009RF9DW|> <|I_B000FI4S1E|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
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+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B00126LYJM|> <|I_B0012Y0ZG2|> <|I_B00126LYJM|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00RZYW4RG|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00L1I1VMG|> <|I_B001ET7FZE|> <|I_B01BNEYGQU|> <|I_B0010ZBORW|> <|I_B0010ZBORW|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|> <|I_B00QXW95Q4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B00006L9LC|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|> <|I_B001OHV1H4|>
+<|I_B0009RF9DW|> <|I_B000FI4S1E|> <|I_B000URXP6E|> <|I_B0012Y0ZG2|> <|I_B0012Y0ZG2|>

data/processed/beauty/cove/tokenizer_config.json

@@ -0,0 +1,7 @@
+{
+  "tokenizer_class": "GPT2Tokenizer",
+  "eos_token": "",
+  "bos_token": "",
+  "unk_token": "",
+  "pad_token": ""
+}

data/processed/beauty/cove_item_ids.json

@@ -0,0 +1 @@
+["B0000530HU", "B00006L9LC", "B00021DJ32", "B0002JHI1I", "B0006O10P4", "B0009RF9DW", "B000FI4S1E", "B000FOI48G", "B000FTYALG", "B000GLRREU", "B000LIBUBY", "B000NKJIXM", "B000PKKAGO", "B000URXP6E", "B000V5Z4J6", "B000VUXCGI", "B000VV1YOY", "B000W0C07Y", "B000WR2HB6", "B000WYJTZG", "B000X2FPXC", "B000X7ST9Y", "B000YB70PS", "B0010ZBORW", "B00112DRHY", "B0011FYB5I", "B00120VWTK", "B00126LYJM", "B0012XPRO8", "B0012Y0ZG2", "B0013NB7DW", "B0014SQQ3M", "B00155Z6V2", "B00157OBRU", "B0017TZD7S", "B001E5PLCM", "B001E96LUO", "B001ET7FZE", "B001F51RAG", "B001LNODUS", "B001OHV1H4", "B001QY8QXM", "B002GP80EU", "B002RZZXYE", "B004CALFE4", "B004KEJ65C", "B006IB5T4W", "B006WYJM8Y", "B007R6UXNY", "B007V6JNE0", "B008YQM4A6", "B0091OCA86", "B00AKP21KM", "B00B7V273E", "B00B9V9ASM", "B00BSE3III", "B00CQ0LN80", "B00CZH3K1C", "B00DY59MB6", "B00EF1QRMU", "B00EYZY6LQ", "B00GHJOM2U", "B00HLXEXDO", "B00IJHY54S", "B00JF2GVWK", "B00L1I1VMG", "B00MGK9Z8U", "B00MTR49IG", "B00N2WQ2IW", "B00NT0AR7E", "B00QXW95Q4", "B00RZYW4RG", "B00UWB35UY", "B00VARTPKS", "B00VG1AV5Q", "B00W259T7G", "B016V8YWBC", "B019809F9Y", "B019FWRG3C", "B019LAI4HU", "B019V2KYZS", "B01BNEYGQU", "B01DKQAXC0", "B01DLR9IDI", "B01E7UKR38"]