added Q 140, and code to convert json to file format

Author: moe18

json_to_files.py

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+Write a Python class …

questions/140_bernoulli-naive-bayes-classifier/description.md

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+{
+  "input": "…",
+  "output": "[1]",
+  "reasoning": "…"
+}

questions/140_bernoulli-naive-bayes-classifier/example.json

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+# Learn section …

questions/140_bernoulli-naive-bayes-classifier/learn.md

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+{
+  "id": "140",
+  "title": "Bernoulli Naive Bayes Classifier",
+  "difficulty": "medium",
+  "category": "Machine Learning",
+  "video": "",
+  "likes": "0",
+  "dislikes": "0",
+  "contributor": [
+    {
+      "name": "Coder1010ayush",
+      "profile_link": "https://github.com/Coder1010ayush"
+    }
+  ]
+}

questions/140_bernoulli-naive-bayes-classifier/meta.json

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+import numpy as np
+
+class NaiveBayes():
+    pass

questions/140_bernoulli-naive-bayes-classifier/solution.py

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+import numpy as np
+
+class NaiveBayes():
+    pass

questions/140_bernoulli-naive-bayes-classifier/starter_code.py

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+[
+  {
+    "test": "print(1+1)",
+    "expected_output": "2"
+  }
+]

questions/140_bernoulli-naive-bayes-classifier/tests.json

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+#!/usr/bin/env python
+"""
+convert_single_question.py
+──────────────────────────
+Paste ONE question dict into QUESTION_DICT and run:
+
+    python utils/convert_single_question.py
+
+The script splits it into:
+
+questions/<id>_<slug>/
+    ├─ meta.json
+    ├─ description.md
+    ├─ learn.md
+    ├─ starter_code.py
+    ├─ solution.py
+    ├─ example.json
+    ├─ tests.json
+    ├─ tinygrad/   (optional)
+    └─ pytorch/    (optional)
+"""
+
+import base64
+import json
+import pathlib
+import re
+from typing import Any, Dict
+
+# ── 1️⃣  EDIT YOUR QUESTION HERE ────────────────────────────────────────────
+QUESTION_DICT: Dict[str, Any] = {
+    "id": "140",
+    "title": "Bernoulli Naive Bayes Classifier",
+    "difficulty": "medium",
+    "category": "Machine Learning",
+    "description": "Write a Python class …",
+    "learn_section": "# Learn section …",
+    "starter_code": "import numpy as np\n\nclass NaiveBayes():\n    pass",
+    "solution": "import numpy as np\n\nclass NaiveBayes():\n    pass",
+    "example": {
+        "input": "…",
+        "output": "[1]",
+        "reasoning": "…"
+    },
+    "test_cases": [
+        {"test": "print(1+1)", "expected_output": "2"}
+    ],
+    "video": "",
+    "likes": "0",
+    "dislikes": "0",
+    "contributor": [
+        {"name": "Moe Chabot", "profile_link": "https://github.com/moe18"}
+    ]
+    # Optional extras:
+    # "marimo_link": "https://…",
+    # "tinygrad_difficulty": "medium",
+    # "tinygrad_starter_code": "BASE64…",
+    # "tinygrad_solution":     "BASE64…",
+    # "tinygrad_test_cases":   [],
+    # "pytorch_difficulty": "medium",
+    # "pytorch_starter_code": "BASE64…",
+    # "pytorch_solution":     "BASE64…",
+    # "pytorch_test_cases":   []
+}
+# ────────────────────────────────────────────────────────────────────────────
+
+
+# ---------- helpers ---------------------------------------------------------
+def slugify(text: str) -> str:
+    text = re.sub(r"[^0-9A-Za-z]+", "-", text.lower())
+    return re.sub(r"-{2,}", "-", text).strip("-")[:50]
+
+
+def maybe_b64(s: str) -> str:
+    try:
+        if len(s) % 4 == 0 and re.fullmatch(r"[0-9A-Za-z+/=\n\r]+", s):
+            return base64.b64decode(s).decode("utf-8")
+    except Exception:
+        pass
+    return s
+
+
+def write_text(path: pathlib.Path, content: str) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(content.rstrip("\n") + "\n", encoding="utf-8")
+
+
+def write_json(path: pathlib.Path, obj: Any) -> None:
+    write_text(path, json.dumps(obj, indent=2, ensure_ascii=False))
+
+
+# ---------- converter -------------------------------------------------------
+def convert_one(q: Dict[str, Any]) -> None:
+    folder = pathlib.Path("questions") / f"{q['id']}_{slugify(q['title'])}"
+    folder.mkdir(parents=True, exist_ok=True)
+
+    # meta.json
+    meta = {
+        "id": q["id"],
+        "title": q["title"],
+        "difficulty": q["difficulty"],
+        "category": q["category"],
+        "video": q.get("video", ""),
+        "likes": q.get("likes", "0"),
+        "dislikes": q.get("dislikes", "0"),
+        "contributor": q.get("contributor", []),
+    }
+    for opt in ("tinygrad_difficulty", "pytorch_difficulty", "marimo_link"):
+        if opt in q:
+            meta[opt] = q[opt]
+    write_json(folder / "meta.json", meta)
+
+    # core files
+    write_text(folder / "description.md", q["description"])
+    write_text(folder / "learn.md", q["learn_section"])
+    write_text(folder / "starter_code.py", q["starter_code"])
+    write_text(folder / "solution.py", q["solution"])
+    write_json(folder / "example.json", q["example"])
+    write_json(folder / "tests.json", q["test_cases"])
+
+    # optional language-specific extras
+    for lang in ("tinygrad", "pytorch"):
+        sc, so, tc = (f"{lang}_starter_code", f"{lang}_solution", f"{lang}_test_cases")
+        if any(k in q for k in (sc, so, tc)):
+            sub = folder / lang
+            if sc in q:
+                write_text(sub / "starter_code.py", maybe_b64(q[sc]))
+            if so in q:
+                write_text(sub / "solution.py", maybe_b64(q[so]))
+            if tc in q:
+                write_json(sub / "tests.json", q[tc])
+
+    # success message (relative if possible)
+    try:
+        rel = folder.relative_to(pathlib.Path.cwd())
+    except ValueError:
+        rel = folder
+    print(f"✓  Created {rel}")
+
+
+# ---------- main ------------------------------------------------------------
+def main() -> None:
+    convert_one(QUESTION_DICT)
+
+
+if __name__ == "__main__":
+    main()