{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthetic Sensitive Data in Source Code (N=300) — Starter\n",
    "\n",
    "Quick exploration of the dataset: load samples, inspect categories, and preview labeled secrets.\n",
    "\n",
    "**Intended use:** secret/PII detection, local masking evaluation, OWASP LLM02–style leakage tests.\n",
    "\n",
    "> All values are **synthetic**. Do not treat them as real credentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import json\n",
    "import pandas as pd\n",
    "\n",
    "# Works on Kaggle and locally\n",
    "CANDIDATES = [\n",
    "    Path(\"/kaggle/input\"),\n",
    "    Path(\".\"),\n",
    "]\n",
    "\n",
    "csv_path = None\n",
    "json_path = None\n",
    "for root in CANDIDATES:\n",
    "    hits = list(root.rglob(\"synthetic_sensitive_data_in_source_code_n300.csv\"))\n",
    "    if hits:\n",
    "        csv_path = hits[0]\n",
    "        json_path = csv_path.with_suffix(\".json\")\n",
    "        break\n",
    "\n",
    "assert csv_path is not None, \"Dataset CSV not found\"\n",
    "print(\"CSV :\", csv_path)\n",
    "print(\"JSON:\", json_path if json_path.exists() else \"(optional)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(csv_path)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Category & language distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Categories\")\n",
    "display(df[\"category\"].value_counts().rename(\"count\").to_frame())\n",
    "\n",
    "print(\"\\nLanguages\")\n",
    "display(df[\"language\"].value_counts().rename(\"count\").to_frame())\n",
    "\n",
    "print(\"\\nSecrets per sample\")\n",
    "display(df[\"sensitive_count\"].describe().to_frame(\"sensitive_count\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = df[\"category\"].value_counts().plot(kind=\"bar\", figsize=(8, 3), title=\"Samples by category\")\n",
    "ax.set_xlabel(\"category\")\n",
    "ax.set_ylabel(\"count\")\n",
    "ax.tick_params(axis=\"x\", rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Preview one sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = df.sample(1, random_state=42).iloc[0]\n",
    "print(\"id      :\", row[\"id\"])\n",
    "print(\"category:\", row[\"category\"])\n",
    "print(\"language:\", row[\"language\"])\n",
    "print(\"findings:\", row[\"finding_types\"])\n",
    "print(\"count   :\", row[\"sensitive_count\"])\n",
    "print(\"\\n--- code_text ---\\n\")\n",
    "# CSV may store newlines as the two characters \\\\n\n",
    "code = str(row[\"code_text\"]).replace(\"\\\\n\", \"\\n\")\n",
    "print(code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Ground truth from JSON (recommended for evaluation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if json_path is not None and json_path.exists():\n",
    "    payload = json.loads(json_path.read_text(encoding=\"utf-8\"))\n",
    "    records = payload[\"records\"]\n",
    "    print(\"name :\", payload.get(\"name\"))\n",
    "    print(\"size :\", payload.get(\"size\"))\n",
    "    print(\"owasp:\", payload.get(\"owasp_primary\"))\n",
    "\n",
    "    sample = records[0]\n",
    "    print(\"\\nExample ground truth:\")\n",
    "    print(\"id      :\", sample[\"id\"])\n",
    "    print(\"findings:\")\n",
    "    for f in sample[\"sensitive_findings\"]:\n",
    "        print(\" -\", f[\"type\"], \"=\", f[\"value\"][:48] + (\"...\" if len(f[\"value\"]) > 48 else \"\"))\n",
    "else:\n",
    "    print(\"JSON not found in this runtime; use the CSV columns for a quick look.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Tiny detection baseline (substring match)\n",
    "\n",
    "Toy baseline: if a labeled secret string appears in `code_text`, count it as detected.  \n",
    "Replace this with your masking / detector pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if json_path is not None and json_path.exists():\n",
    "    records = json.loads(json_path.read_text(encoding=\"utf-8\"))[\"records\"]\n",
    "    total = hit = 0\n",
    "    for r in records:\n",
    "        code = r[\"code_text\"]\n",
    "        for f in r[\"sensitive_findings\"]:\n",
    "            total += 1\n",
    "            if f[\"value\"] in code:\n",
    "                hit += 1\n",
    "    print(f\"Labeled secrets present in code_text: {hit}/{total} ({100 * hit / total:.1f}%)\")\n",
    "    print(\"(Expected ~100% — sanity check that labels match the snippets.)\")\n",
    "else:\n",
    "    print(\"Skip: JSON required for this cell.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "1. Build a detector (regex / ML) over `code_text`\n",
    "2. Compare predictions to `sensitive_findings`\n",
    "3. Measure precision, recall, and latency for your masking layer"
   ]
  }
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