#!/usr/bin/env python3
"""Automated readiness checks for judge demo cases."""

from __future__ import annotations

import json
import os
import sys

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from plane_mode_scholar.gradio_ui.layout import (
    STATE,
    case_01_recall_restart,
    grade_quiz_intentionally_wrong,
    load_readiness_cases,
    start_study_session,
)
from plane_mode_scholar.ingestion.pack_builder import PackBuilder
from plane_mode_scholar.memory.scheduler import get_due_reviews, schedule_review
from plane_mode_scholar.storage.sqlite_store import SQLiteStore
from plane_mode_scholar.storage.trace_export import export_session_trace


def load_cases() -> list[dict]:
    return load_readiness_cases()


def check_srs_due() -> bool:
    store = SQLiteStore()
    pb = PackBuilder(store)
    demo = os.path.normpath("plane_mode_scholar/data/demo_notes.txt")
    if not os.path.exists(demo):
        print("SKIP srs: demo notes missing")
        return True
    result = pb.create_pack("RC", "ML", file_paths=[demo], user_id="rc_user")
    pack_id = result["pack"]["pack_id"]
    schedule_review(store, pack_id, "overfitting", "sess_rc", user_id="rc_user", interval_days=0.0)
    due = get_due_reviews(store, pack_id, "rc_user")
    ok = len(due) >= 1
    print(f"{'PASS' if ok else 'FAIL'} case_03_srs: due_count={len(due)}")
    return ok


def check_trace_shape() -> bool:
    store = SQLiteStore()
    trace = export_session_trace(store, "sess_missing", "pack_missing", "user")
    ok = "pipeline_steps" in trace and "memory_operations" in trace
    print(f"{'PASS' if ok else 'FAIL'} case_04_trace: keys present")
    return ok


def check_case_01_recall_flow() -> bool:
    store = SQLiteStore()
    pb = PackBuilder(store)
    demo = os.path.normpath("plane_mode_scholar/data/demo_notes.txt")
    if not os.path.exists(demo):
        print("SKIP case_01: demo notes missing")
        return True
    result = pb.create_pack("C01", "ML", file_paths=[demo], user_id="c01_user")
    pack_id = result["pack"]["pack_id"]
    goals = "Review ML concepts"
    start_study_session(pack_id, goals, 45, "c01_user")
    sid1 = STATE.session_id
    STATE.quiz_data = [
        {
            "question": "What is gradient descent?",
            "options": ["Optimization", "Overfitting", "Clustering", "Sampling"],
            "correct_index": 0,
            "concept": "gradient descent",
            "explanation": "Gradient descent optimizes loss.",
        }
    ]
    STATE.quiz_index = 0
    grade_quiz_intentionally_wrong(pack_id, sid1, "c01_user")
    out = case_01_recall_restart(
        "case_01_recall", sid1, pack_id, "", [], goals, 45, "c01_user"
    )
    recall_html = out[1]
    ok = "recall" in recall_html.lower() or "remember" in recall_html.lower() or "due" in recall_html.lower()
    print(f"{'PASS' if ok else 'FAIL'} case_01_recall: restart recall banner present")
    return ok


def main() -> int:
    cases = load_cases()
    print(f"Loaded {len(cases)} readiness cases")
    results = [check_srs_due(), check_trace_shape(), check_case_01_recall_flow()]
    for case in cases:
        print(f"  - {case['id']}: {case['title']}")
    if all(results):
        print("All automated checks passed.")
        return 0
    print("Some checks failed.")
    return 1


if __name__ == "__main__":
    raise SystemExit(main())