"""Sanity check: feed a few REAL and FAKE clips from jay15k to /api/detect
and report each detector's prediction. If the newly-trained checkpoints
are loaded, Nes2Net + SONAR + BiCrossMamba + VoiceRadar should be sharp.
"""
import sys, random, httpx
from pathlib import Path

API = "http://127.0.0.1:8000"
ROOT = Path(r"E:\sem_8\audio-deepfake-detector\data\jay15k\deepfake_audio_dataset_jay15k")

def main():
    rng = random.Random(7)
    real_files = list((ROOT / "real").glob("*.wav"))
    fake_files = list((ROOT / "fake").glob("*.wav"))
    rng.shuffle(real_files); rng.shuffle(fake_files)

    pick = real_files[:3] + fake_files[:3]
    print(f"{'file':<32} {'label':<5} {'verdict':<10} {'P(fake)':<8} | per-detector pred/conf")
    print("-" * 130)
    correct = 0
    total = 0
    for f in pick:
        label = "real" if "real" in f.parent.name else "fake"
        with open(f, "rb") as fh:
            r = httpx.post(f"{API}/api/detect",
                files={"audio_file": (f.name, fh.read(), "audio/wav")},
                data={"return_features": "false"}, timeout=120)
        if r.status_code != 200:
            print(f"{f.name:<32} ERROR {r.status_code}: {r.text[:80]}")
            continue
        body = r.json()
        ev = body["ensemble_verdict"]
        total += 1
        if ev["prediction"] == label: correct += 1
        cells = []
        for mid in ["nes2net", "sonar", "bicrossmamba_st", "voiceradar", "holi_antispoof", "lf_hf_physics", "melodymachine", "motheecreator"]:
            res = body["results"].get(mid, {})
            cells.append(f"{mid[:4]}={res.get('prediction','?')[:1]}/{res.get('confidence',0):.2f}")
        print(f"{f.name:<32} {label:<5} {ev['prediction']:<10} {ev.get('fake_probability',0):<8.3f} | " + " ".join(cells))
    print(f"\nEnsemble accuracy on 6 jay15k clips: {correct}/{total}")
    return 0

if __name__ == "__main__":
    sys.exit(main())