{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc834ae9-7876-4b96-85bc-b6f86fb7c5de",
   "metadata": {},
   "source": [
    "# Olfactory Intelligence Benchmark csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34340a73-ea3b-454d-b063-0b8e2e20b3f0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary\n",
      "-------\n",
      "Included files: 8\n",
      "Total combined rows: 1010\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "import uuid\n",
    "from pathlib import Path\n",
    "from typing import List, Tuple\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "ROOT = Path(\".\").resolve()              \n",
    "OUT_CSV = Path(\"OP_Benchmark.csv\")  \n",
    "if OUT_CSV.exists():\n",
    "    OUT_CSV.unlink()\n",
    "DEDUPE = False                         \n",
    "\n",
    "REQUIRED_COLS = [\n",
    "    \"question_ID\",\n",
    "    \"compound.name_1\",\n",
    "    \"SMILES_1\",\n",
    "    \"compound.name_2\",\n",
    "    \"SMILES_2\",\n",
    "    \"OPTIONS\",\n",
    "    \"question_category\",\n",
    "    \"prompt.1\",\n",
    "    \"prompt.2\",\n",
    "    \"answer\",\n",
    "    \"other_info\",\n",
    "]\n",
    "\n",
    "\n",
    "def find_csvs_one_level(root: Path) -> List[Path]:\n",
    "    \"\"\"\n",
    "    Return CSV files located in immediate subfolders.\n",
    "    Does not include CSV files in `root` itself.\n",
    "    \"\"\"\n",
    "    results: List[Path] = []\n",
    "    for entry in root.iterdir():\n",
    "        if entry.is_dir():\n",
    "            results.extend(entry.glob(\"*.csv\"))\n",
    "    return results\n",
    "\n",
    "def validate_columns(df: pd.DataFrame) -> Tuple[bool, List[str]]:\n",
    "    cols = list(df.columns.astype(str))\n",
    "    missing = [c for c in REQUIRED_COLS if c not in cols]\n",
    "    return (len(missing) == 0), missing\n",
    "\n",
    "candidates = find_csvs_one_level(ROOT)\n",
    "\n",
    "good_frames: List[pd.DataFrame] = []\n",
    "included_paths: List[Path] = []\n",
    "excluded_missing: List[Tuple[Path, List[str]]] = []\n",
    "excluded_errors: List[Tuple[Path, str]] = []\n",
    "\n",
    "for csv_path in candidates:\n",
    "    try:\n",
    "        df = pd.read_csv(csv_path, dtype=str, keep_default_na=False, na_values=[])\n",
    "    except Exception as e:\n",
    "        excluded_errors.append((csv_path, f\"read error: {e}\"))\n",
    "        print(f\"{csv_path} | read error: {e}\")\n",
    "        continue\n",
    "\n",
    "    ok, missing = validate_columns(df)\n",
    "    if not ok:\n",
    "        excluded_missing.append((csv_path, missing))\n",
    "        print(f\"{csv_path} | missing columns: {', '.join(missing)}\")\n",
    "        continue\n",
    "\n",
    "    # keep only required columns, in exact order\n",
    "    df = df[REQUIRED_COLS].copy()\n",
    "    good_frames.append(df)\n",
    "    included_paths.append(csv_path)\n",
    "    \n",
    "\n",
    "if not good_frames:\n",
    "    print(\"\\nNo valid CSVs matched the required headers. Nothing to combine.\")\n",
    "else:\n",
    "    combined = pd.concat(good_frames, axis=0, ignore_index=True)\n",
    "\n",
    "    if DEDUPE:\n",
    "        before = len(combined)\n",
    "        combined = combined.drop_duplicates()\n",
    "        print(f\"\\nDe-duplicated rows: {before} → {len(combined)}\")\n",
    "\n",
    "    # ensure exact column order\n",
    "    combined = combined[REQUIRED_COLS]\n",
    "\n",
    "    # Replace question_ID values with fresh UUID4 strings (universal identifiers)\n",
    "    combined[\"question_ID\"] = [str(uuid.uuid4()) for _ in range(len(combined))]\n",
    "\n",
    "    # write output\n",
    "    OUT_CSV.parent.mkdir(parents=True, exist_ok=True)\n",
    "    combined.to_csv(OUT_CSV, index=False)\n",
    "\n",
    "    # summary\n",
    "    print(\"\\nSummary\")\n",
    "    print(\"-------\")\n",
    "    print(f\"Included files: {len(included_paths)}\")\n",
    "    print(f\"Total combined rows: {len(combined)}\")\n",
    "    print(f\"Done\")\n",
    "\n",
    "    if excluded_missing:\n",
    "        print(\"\\nFiles excluded due to missing columns:\")\n",
    "        for p, missing in excluded_missing:\n",
    "            print(f\"  - {p}  (missing: {', '.join(missing)})\")\n",
    "\n",
    "    if excluded_errors:\n",
    "        print(\"\\nFiles excluded due to read errors:\")\n",
    "        for p, msg in excluded_errors:\n",
    "            print(f\"  - {p}  ({msg})\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}