case_study_gpt55.py

"""Evaluate GPT-5.5 on the FULL test set: sync (426), mute neutral (142), swap direct (142).

GPT-5.5 chat.completions does NOT accept input_audio (audio is gated to
gpt-4o-audio-preview / realtime). We therefore send VIDEO FRAMES ONLY (8 frames
sampled evenly per clip) + the appropriate text prompt. This is a documented
handicap — the resulting numbers reflect what GPT-5.5 can do without hearing
the audio.

Audio is intentionally NOT extracted; for sync the model has no temporal audio
cue, for mute the audio is silent anyway, for swap the model can't hear the
donor track.

Outputs (label includes _visualOnly to flag the handicap):
  ~/eval_results/sync/sync_gpt-5.5_visualOnly/
  ~/eval_results/mute/mute_gpt-5.5_visualOnly_promptNeutral/
  ~/eval_results/swap/swap_gpt-5.5_visualOnly_promptDirect/

Swap donor pairing reuses the assignments from the Gemini swap eval
(same seed=42, so identical to Qwen swap pairing too).

Usage:
  OPENAI_API_KEY=sk-... python3 /home/ubuntu/case_study_gpt55.py
"""

import argparse
import base64
import json
import os
import re
import subprocess
import sys
import tempfile
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from typing import Any, Dict, List, Optional

# ---------------------------------------------------------------------------
# Paths
# ---------------------------------------------------------------------------
DATA_ROOT     = Path("/opt/dlami/nvme/video_source")
ORIG_DIR      = DATA_ROOT / "original" / "uag_oops"
DELAY_DIR     = DATA_ROOT / "random_shift_video" / "delay"
EARLY_DIR     = DATA_ROOT / "random_shift_video" / "early"

TEST_JSONL    = Path("/home/ubuntu/CleverHans-Evaluation/data/kto_training_data_v2_test.jsonl")
GEMINI_SWAP   = Path("/home/ubuntu/eval_results/gemini_mute_sync_swap/swap/"
                     "swap_gemini_gemini-3.1-pro-preview__promptDirect/eval_results.jsonl")

EVAL_ROOT     = Path("/home/ubuntu/eval_results")
FFMPEG        = "/home/ubuntu/anaconda3/envs/video/bin/ffmpeg"
FFPROBE       = "/home/ubuntu/anaconda3/envs/video/bin/ffprobe"

DIR_OFFSET_RE = re.compile(r"_(delay|early)_(\d+(?:\.\d+)?)s\.mp4$")

# ---------------------------------------------------------------------------
# Prompts (mirror eval_gemini_mute_sync_swap.py)
# ---------------------------------------------------------------------------
SYNC_PROMPT = (
    "Watch this video and listen to its audio carefully. "
    "Determine whether the audio and video tracks are synchronized. "
    "If they are not synchronized, identify the direction of the offset "
    "(audio delayed or audio early relative to video) and estimate the offset in seconds. "
    "Explain your reasoning."
)
MUTE_PROMPT_NEUTRAL = "Describe the audio you hear in this video."
SWAP_PROMPT_DIRECT  = (
    "Does the audio in this video match the visual content? "
    "Answer with only 'yes' or 'no'."
)

SYNC_PARSE_SYSTEM = (
    "You parse a free-form judgement about whether the audio in a short video is "
    "synchronized with the visuals. Return STRICT JSON only with keys: "
    "synced (bool), direction (one of 'delay'|'early'|'none'), "
    "offset_sec (number, 0 if synced), t_v (number|null, visual cue time), "
    "t_a (number|null, audio cue time), explanation (string). "
    "If the answer says they are aligned, set synced=true, direction='none', offset_sec=0."
)
MUTE_JUDGE_SYSTEM = (
    "You are evaluating a model's response about a video whose audio has been secretly "
    "replaced with silence. Classify into ONE of: "
    '{"prediction": "muted"|"synced"|"delay"|"early", "explanation": "<one sentence>"}. '
    "Rules: "
    "- 'muted' iff the model says no audio / silence / no audible sound. "
    "- 'synced' iff the model describes audio that matches the visuals (HALLUCINATION). "
    "- 'delay'/'early' iff the model claims a temporal offset. "
    "Return STRICT JSON only."
)


# ---------------------------------------------------------------------------
# Small utilities
# ---------------------------------------------------------------------------
def load_jsonl(path):
    with open(path) as f:
        for line in f:
            line = line.strip()
            if line:
                yield json.loads(line)


def parse_gt_from_name(video_name: str) -> Dict[str, Any]:
    m = DIR_OFFSET_RE.search(video_name)
    if m:
        return {"synced": False, "direction": m.group(1), "offset_sec": float(m.group(2))}
    return {"synced": True, "direction": "none", "offset_sec": 0.0}


def video_path_for(video_name: str) -> Optional[Path]:
    """Resolve a sync test name to its on-disk mp4 (synced original / delay / early)."""
    m = DIR_OFFSET_RE.search(video_name)
    if m is None:
        p = ORIG_DIR / video_name
    elif m.group(1) == "delay":
        p = DELAY_DIR / video_name
    else:
        p = EARLY_DIR / video_name
    return p if p.exists() else None


def video_duration(path: Path) -> float:
    out = subprocess.run(
        [FFPROBE, "-v", "error", "-show_entries", "format=duration",
         "-of", "default=noprint_wrappers=1:nokey=1", str(path)],
        capture_output=True, text=True, check=True,
    )
    try:
        return float(out.stdout.strip())
    except ValueError:
        return 5.0


def extract_frames_b64(video_path: Path, n_frames: int = 8) -> List[str]:
    dur = video_duration(video_path)
    if dur <= 0.05:
        dur = 0.5
    timestamps = [dur * (i + 0.5) / n_frames for i in range(n_frames)]
    out = []
    with tempfile.TemporaryDirectory() as td:
        for i, t in enumerate(timestamps):
            png = Path(td) / f"f_{i:02d}.png"
            subprocess.run(
                [FFMPEG, "-y", "-ss", f"{t:.3f}", "-i", str(video_path),
                 "-frames:v", "1", "-vf", "scale=512:-2",
                 "-loglevel", "error", str(png)],
                check=True,
            )
            out.append(base64.b64encode(png.read_bytes()).decode())
    return out


# ---------------------------------------------------------------------------
# OpenAI calls (frames-only multimodal: text + images)
# ---------------------------------------------------------------------------
def _client(api_key: str):
    from openai import OpenAI
    return OpenAI(api_key=api_key)


def call_gpt_frames(client, model, prompt, frames_b64,
                    max_tokens=4000, temperature=0.0,
                    reasoning_effort="minimal", _diag=True) -> str:
    """Send text + frames to a (possibly-reasoning) GPT model.

    GPT-5 family is a reasoning model: max_completion_tokens covers BOTH the
    hidden reasoning trace AND the visible content. If the token budget is too
    low, content can come back empty. We:
      - bump max_completion_tokens to 4000
      - pass reasoning_effort='minimal' to keep most of the budget for content
      - retry once on temperature-rejection
      - if content is still empty, fall through with a diagnostic-friendly log
    """
    content: List[Dict[str, Any]] = [{"type": "text", "text": prompt}]
    for fb in frames_b64:
        content.append({"type": "image_url",
                        "image_url": {"url": f"data:image/png;base64,{fb}"}})

    base = dict(
        model=model,
        messages=[{"role": "user", "content": content}],
        max_completion_tokens=max_tokens,
    )

    def _try(extra):
        return client.chat.completions.create(**base, **extra)

    resp = None
    for kwargs in (
        {"temperature": temperature, "reasoning_effort": reasoning_effort},
        {"reasoning_effort": reasoning_effort},          # drop temp
        {"temperature": temperature},                     # drop reasoning_effort
        {},                                               # both stripped
    ):
        try:
            resp = _try(kwargs)
            break
        except Exception as exc:
            last_err = exc
            continue
    if resp is None:
        raise last_err

    msg = resp.choices[0].message
    text = (msg.content or "").strip()
    if not text:
        # Diagnostic: surface finish_reason / refusal / usage so we can see why.
        fin = resp.choices[0].finish_reason
        refusal = getattr(msg, "refusal", None)
        usage = getattr(resp, "usage", None)
        if _diag:
            print(f"  [gpt empty] finish={fin}  refusal={refusal}  usage={usage}",
                  flush=True)
    return text


def call_judge(client, judge_model, system_prompt, user_text) -> Optional[Dict[str, Any]]:
    try:
        resp = client.chat.completions.create(
            model=judge_model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_text},
            ],
            response_format={"type": "json_object"},
            max_completion_tokens=300,
        )
        return json.loads(resp.choices[0].message.content or "")
    except Exception as exc:
        print(f"  [judge] error: {exc}", flush=True)
        return None


# ---------------------------------------------------------------------------
# Per-task runners (frames-only)
# ---------------------------------------------------------------------------
def run_one_sync(client, model, judge_model, item, n_frames):
    raw = call_gpt_frames(
        client, model, SYNC_PROMPT,
        extract_frames_b64(item["video_path"], n_frames),
    )
    parsed = call_judge(client, judge_model, SYNC_PARSE_SYSTEM, raw) or {}
    direction = str(parsed.get("direction", "none")).lower()
    if direction not in ("delay", "early", "none"):
        direction = "none"
    synced = parsed.get("synced", False)
    if isinstance(synced, str):
        synced = synced.lower() in ("true", "yes", "1")
    return {
        "video": item["video"],
        "video_path": str(item["video_path"]),
        "gt_synced": item["gt_synced"],
        "gt_direction": item["gt_direction"],
        "gt_offset_sec": item["gt_offset_sec"],
        "pred_synced": bool(synced),
        "pred_direction": direction,
        "pred_offset_sec": float(parsed.get("offset_sec", 0.0) or 0.0),
        "pred_explanation": str(parsed.get("explanation", "")),
        "parse_method": "openai_parse" if parsed else "openai_parse_fail",
        "raw_output": raw,
    }


def run_one_mute(client, model, judge_model, item, n_frames):
    raw = call_gpt_frames(
        client, model, MUTE_PROMPT_NEUTRAL,
        extract_frames_b64(item["video_path"], n_frames),
    )
    parsed = call_judge(client, judge_model, MUTE_JUDGE_SYSTEM, raw) or {}
    pred = str(parsed.get("prediction", "synced")).lower()
    if pred not in ("muted", "synced", "delay", "early"):
        pred = "synced"
    return {
        "video": item["video"],
        "video_path": str(item["video_path"]),
        "gt_label": "muted",
        "pred_label": pred,
        "correct": (pred == "muted"),
        "judge_explanation": str(parsed.get("explanation", "")),
        "parse_method": "openai_judge" if parsed else "judge_fail",
        "prompt_mode": "neutral",
        "raw_output": raw,
    }


def run_one_swap(client, model, judge_model, item, n_frames):
    raw = call_gpt_frames(
        client, model, SWAP_PROMPT_DIRECT,
        extract_frames_b64(item["video_path"], n_frames),
        # Reasoning model needs headroom for the hidden trace even on yes/no Qs.
    )
    t = raw.strip().lower()
    if t.startswith("no") or "no" in t.split()[:3]:
        pred = "mismatched"
    elif t.startswith("yes") or "yes" in t.split()[:3]:
        pred = "synced"
    else:
        pred = "synced"
    return {
        "video": item["video"],
        "video_path": str(item["video_path"]),
        "swapped_from": item["swapped_from"],
        "gt_label": "mismatched",
        "pred_label": pred,
        "correct": (pred == "mismatched"),
        "parse_method": "direct",
        "prompt_mode": "direct",
        "raw_output": raw,
    }


# ---------------------------------------------------------------------------
# Build work items from the FULL test set
# ---------------------------------------------------------------------------
def build_sync_work() -> List[Dict[str, Any]]:
    """All 426 sync samples (142 base × 3 variants)."""
    work = []
    for r in load_jsonl(TEST_JSONL):
        v = r["video"]
        p = video_path_for(v)
        if p is None:
            print(f"[skip sync] missing file: {v}", flush=True)
            continue
        gt = parse_gt_from_name(v)
        work.append({
            "video": v,
            "video_path": p,
            "gt_synced": gt["synced"],
            "gt_direction": gt["direction"],
            "gt_offset_sec": gt["offset_sec"],
        })
    return work


def build_mute_work() -> List[Dict[str, Any]]:
    """142 base videos (skip delay/early variants); audio is implicit silence."""
    work = []
    seen = set()
    for r in load_jsonl(TEST_JSONL):
        v = r["video"]
        if "_delay_" in v or "_early_" in v or v in seen:
            continue
        seen.add(v)
        p = ORIG_DIR / v
        if not p.exists():
            print(f"[skip mute] missing: {v}", flush=True)
            continue
        work.append({"video": v, "video_path": p})
    return work


def build_swap_work() -> List[Dict[str, Any]]:
    """142 base videos with the same donor pairing used in Gemini swap eval."""
    if not GEMINI_SWAP.exists():
        sys.exit(f"[error] need {GEMINI_SWAP} for swap donor pairings")
    work = []
    for r in load_jsonl(GEMINI_SWAP):
        v = r["video"]
        donor = r.get("swapped_from")
        if not donor:
            continue
        p = ORIG_DIR / v
        if not p.exists():
            print(f"[skip swap] missing: {v}", flush=True)
            continue
        work.append({"video": v, "video_path": p, "swapped_from": donor})
    return work


# ---------------------------------------------------------------------------
# Metrics (match existing eval_*.py schemas so results are directly comparable)
# ---------------------------------------------------------------------------
def _safe_div(a, b):
    return round(a / b, 4) if b else 0.0


def metrics_mute(rows, model, judge_model):
    breakdown = {"muted": 0, "synced": 0, "delay": 0, "early": 0}
    parse_stats: Dict[str, int] = {}
    for r in rows:
        breakdown[r["pred_label"]] = breakdown.get(r["pred_label"], 0) + 1
        m = r.get("parse_method", "")
        parse_stats[m] = parse_stats.get(m, 0) + 1
    n = len(rows)
    return {
        "total_samples": n,
        "mute_detection_rate": _safe_div(breakdown["muted"], n),
        "hallucination_rate":  _safe_div(n - breakdown["muted"], n),
        "prediction_breakdown": breakdown,
        "parse_stats": parse_stats,
        "eval_config": {
            "base_model": model,
            "prompt_mode": "neutral",
            "openai_judge": True,
            "judge_model": judge_model,
            "input_modality": "frames_only",
        },
    }


def metrics_swap(rows, model, judge_model):
    breakdown = {"mismatched": 0, "synced": 0, "delay": 0, "early": 0}
    parse_stats: Dict[str, int] = {}
    for r in rows:
        breakdown[r["pred_label"]] = breakdown.get(r["pred_label"], 0) + 1
        m = r.get("parse_method", "")
        parse_stats[m] = parse_stats.get(m, 0) + 1
    n = len(rows)
    return {
        "total_samples": n,
        "mismatch_detection_rate": _safe_div(breakdown["mismatched"], n),
        "hallucination_rate":      _safe_div(n - breakdown["mismatched"], n),
        "prediction_breakdown": breakdown,
        "parse_stats": parse_stats,
        "eval_config": {
            "base_model": model,
            "prompt_mode": "direct",
            "judge_model": judge_model,  # not used for direct, kept for parity
            "input_modality": "frames_only",
        },
    }


def metrics_sync(rows, model, judge_model):
    n = len(rows)
    by_cat = {"synced": [], "delay": [], "early": []}
    for r in rows:
        if r["gt_synced"]:
            by_cat["synced"].append(r)
        else:
            by_cat[r["gt_direction"]].append(r)

    def _is_correct(r):
        if r["gt_synced"]:
            return r["pred_synced"]
        return (not r["pred_synced"]) and r["pred_direction"] == r["gt_direction"]

    sync_desync = sum(1 for r in rows if bool(r["pred_synced"]) == bool(r["gt_synced"]))
    three_class = sum(1 for r in rows if _is_correct(r))
    desync_rows = [r for r in rows if not r["gt_synced"]]
    dir_correct = sum(1 for r in desync_rows
                      if (not r["pred_synced"]) and r["pred_direction"] == r["gt_direction"])

    # Offset MAE on rows where both gt and pred have a numeric offset for desync
    offsets = []
    for r in desync_rows:
        if r.get("pred_offset_sec") is not None and r.get("gt_offset_sec") is not None:
            offsets.append(abs(float(r["pred_offset_sec"]) - float(r["gt_offset_sec"])))
    offset_mae = round(sum(offsets) / len(offsets), 4) if offsets else None

    parse_stats: Dict[str, int] = {}
    for r in rows:
        m = r.get("parse_method", "")
        parse_stats[m] = parse_stats.get(m, 0) + 1

    return {
        "total_samples": n,
        "sync_desync_accuracy":         _safe_div(sync_desync, n),
        "three_class_accuracy":         _safe_div(three_class, n),
        "direction_accuracy_on_desync": _safe_div(dir_correct, len(desync_rows)),
        "per_category": {
            "synced_accuracy": _safe_div(sum(1 for r in by_cat["synced"] if _is_correct(r)),
                                         len(by_cat["synced"])),
            "delay_accuracy":  _safe_div(sum(1 for r in by_cat["delay"]  if _is_correct(r)),
                                         len(by_cat["delay"])),
            "early_accuracy":  _safe_div(sum(1 for r in by_cat["early"]  if _is_correct(r)),
                                         len(by_cat["early"])),
            "synced_count": len(by_cat["synced"]),
            "delay_count":  len(by_cat["delay"]),
            "early_count":  len(by_cat["early"]),
        },
        "offset_mae_sec": offset_mae,
        "offset_evaluated_count": len(offsets),
        "parse_stats": parse_stats,
        "eval_config": {
            "base_model": model,
            "openai_parse_sync": True,
            "judge_model": judge_model,
            "input_modality": "frames_only",
        },
    }


METRICS_FN = {
    "sync": metrics_sync,
    "mute": metrics_mute,
    "swap": metrics_swap,
}


# ---------------------------------------------------------------------------
# Run loop with resume + parallel
# ---------------------------------------------------------------------------
def run_task(out_dir: Path, work, runner, client, model, judge_model, n_frames, workers,
             task_kind: str):
    out_dir.mkdir(parents=True, exist_ok=True)
    results_path = out_dir / "eval_results.jsonl"

    processed = set()
    if results_path.exists():
        with open(results_path) as f:
            for line in f:
                line = line.strip()
                if line:
                    processed.add(json.loads(line)["video"])
        print(f"[{out_dir.name}] resume: {len(processed)} already done")

    todo = [w for w in work if w["video"] not in processed]
    print(f"[{out_dir.name}] {len(todo)} new / {len(work)} total")

    def _go(item):
        try:
            return runner(client, model, judge_model, item, n_frames)
        except Exception as exc:
            print(f"[{out_dir.name}] error on {item['video']}: {exc}", flush=True)
            return None

    n_done = 0
    with ThreadPoolExecutor(max_workers=workers) as ex, open(results_path, "a") as out:
        futures = {ex.submit(_go, item): item for item in todo}
        for fut in as_completed(futures):
            res = fut.result()
            if res is None:
                continue
            out.write(json.dumps(res, ensure_ascii=False) + "\n")
            out.flush()
            n_done += 1
            if n_done % 10 == 0 or n_done == len(todo):
                print(f"[{out_dir.name}] {n_done}/{len(todo)} done", flush=True)
    print(f"[{out_dir.name}] saved -> {results_path}")

    # Compute and write metrics.json (matches existing eval_*.py schemas).
    rows = [json.loads(l) for l in open(results_path) if l.strip()]
    metrics = METRICS_FN[task_kind](rows, model, judge_model)
    metrics_path = out_dir / "metrics.json"
    with open(metrics_path, "w") as f:
        json.dump(metrics, f, indent=2, ensure_ascii=False)
    print(f"[{out_dir.name}] metrics -> {metrics_path}")
    return results_path


# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--openai-key", default=os.environ.get("OPENAI_API_KEY"))
    ap.add_argument("--model", default="gpt-5.5",
                    help="Model under test (frames-only multimodal).")
    ap.add_argument("--judge-model", default="gpt-5.4")
    ap.add_argument("--tasks", default="sync,mute,swap")
    ap.add_argument("--workers", type=int, default=6)
    ap.add_argument("--n-frames", type=int, default=8)
    ap.add_argument("--label-suffix", default="_visualOnly",
                    help="Tagged into output dir name to flag the no-audio handicap.")
    args = ap.parse_args()

    if not args.openai_key:
        sys.exit("[error] need --openai-key or OPENAI_API_KEY env")

    client = _client(args.openai_key)
    model_tag = args.model.replace("/", "_") + args.label_suffix

    tasks = [t.strip() for t in args.tasks.split(",") if t.strip()]
    if "sync" in tasks:
        run_task(EVAL_ROOT / "sync" / f"sync_{model_tag}",
                 build_sync_work(), run_one_sync,
                 client, args.model, args.judge_model, args.n_frames, args.workers,
                 task_kind="sync")
    if "mute" in tasks:
        run_task(EVAL_ROOT / "mute" / f"mute_{model_tag}_promptNeutral",
                 build_mute_work(), run_one_mute,
                 client, args.model, args.judge_model, args.n_frames, args.workers,
                 task_kind="mute")
    if "swap" in tasks:
        run_task(EVAL_ROOT / "swap" / f"swap_{model_tag}_promptDirect",
                 build_swap_work(), run_one_swap,
                 client, args.model, args.judge_model, args.n_frames, args.workers,
                 task_kind="swap")


if __name__ == "__main__":
    main()