data_gen/generate_activitynet_chosen.py

#!/usr/bin/env python3
"""
Generate chosen responses for ActivityNet DPO data using GPT-5.4.
Usage:
    export OPENAI_API_KEY="sk-..."
    python generate_activitynet_chosen.py
"""

import json
import os
import sys
import base64
import time
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor, as_completed

import av
import numpy as np
from openai import OpenAI

# === Config ===
VIDEO_DIR = "/opt/dlami/nvme/sharegpt_activitynet_raw/activitynet/videos"
DPO_SOURCE = "/opt/dlami/nvme/sharegpt_instructions/video_instruction/train/dpo/sft_dpo_17k.jsonl"
OUTPUT_FILE = "/opt/dlami/nvme/activitynet_gpt_chosen.jsonl"
NUM_FRAMES = 10
MAX_WORKERS = 8  # parallel API calls
MODEL = "gpt-5.4-2026-03-05"

client = OpenAI()


def extract_frames(video_path: str, num_frames: int = 10) -> list[bytes]:
    """Extract evenly-spaced frames from video, return as JPEG bytes."""
    container = av.open(video_path)
    stream = next(s for s in container.streams if s.type == "video")
    total_frames = stream.frames
    if total_frames == 0:
        total_frames = 300  # fallback

    indices = np.linspace(0, total_frames - 1, num_frames).astype(int)
    frames = []
    for i, frame in enumerate(container.decode(stream)):
        if i in indices:
            img = frame.to_image()
            import io
            buf = io.BytesIO()
            img.save(buf, format="JPEG", quality=85)
            frames.append(buf.getvalue())
        if len(frames) >= num_frames:
            break

    container.close()
    return frames


def generate_chosen(video_id: str, prompt: str) -> dict:
    """Call GPT-5.4 with video frames + question, return chosen response."""
    video_path = os.path.join(VIDEO_DIR, f"{video_id}.mp4")
    if not os.path.exists(video_path):
        return {"video": video_id, "error": f"video not found: {video_path}"}

    try:
        frames = extract_frames(video_path, NUM_FRAMES)
    except Exception as e:
        return {"video": video_id, "error": f"frame extraction failed: {e}"}

    image_messages = []
    for i, frame_bytes in enumerate(frames):
        b64 = base64.b64encode(frame_bytes).decode("utf-8")
        image_messages.append({
            "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{b64}", "detail": "low"}
        })

    system_prompt = (
        "You are watching a video. The following images are evenly-spaced frames extracted from the video. "
        "Answer the question based on what you observe in these frames. "
        "Be specific, accurate, and detailed. Describe visual details you actually see. "
        "Do not hallucinate or guess about things not visible in the frames."
    )

    user_content = [
        {"type": "text", "text": f"These are {len(frames)} frames from a video:"},
        *image_messages,
        {"type": "text", "text": f"\nQuestion: {prompt}\n\nPlease provide a detailed and accurate answer."},
    ]

    for attempt in range(3):
        try:
            response = client.chat.completions.create(
                model=MODEL,
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": user_content},
                ],
                max_completion_tokens=512,
                temperature=0.3,
            )
            answer = response.choices[0].message.content.strip()
            return {
                "video": video_id,
                "prompt": prompt,
                "chosen_gpt": answer,
                "tokens_used": response.usage.total_tokens if response.usage else 0,
            }
        except Exception as e:
            if attempt < 2:
                time.sleep(2 ** (attempt + 1))
            else:
                return {"video": video_id, "error": f"API failed after 3 retries: {e}"}


def main():
    # Load ActivityNet DPO entries
    activitynet = []
    with open(DPO_SOURCE) as f:
        for line in f:
            d = json.loads(line)
            if d.get("video", "").startswith("v_"):
                activitynet.append(d)

    print(f"ActivityNet DPO entries: {len(activitynet)}")

    # Resume from existing output
    done_ids = set()
    if os.path.exists(OUTPUT_FILE):
        with open(OUTPUT_FILE) as f:
            for line in f:
                d = json.loads(line)
                done_ids.add(f"{d['video']}_{d.get('prompt', '')[:50]}")
        print(f"Resuming: {len(done_ids)} already done")

    todo = []
    for d in activitynet:
        key = f"{d['video']}_{d['prompt'][:50]}"
        if key not in done_ids:
            todo.append(d)

    print(f"To process: {len(todo)}")
    if not todo:
        print("All done!")
        return

    # Process with thread pool
    total_tokens = 0
    success = 0
    errors = 0

    with open(OUTPUT_FILE, "a") as out_f:
        with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
            futures = {
                executor.submit(generate_chosen, d["video"], d["prompt"]): d
                for d in todo
            }

            for i, future in enumerate(as_completed(futures), 1):
                result = future.result()

                if "error" in result:
                    errors += 1
                    print(f"  [{i}/{len(todo)}] ERROR {result['video']}: {result['error']}")
                else:
                    success += 1
                    total_tokens += result.get("tokens_used", 0)
                    out_f.write(json.dumps(result, ensure_ascii=False) + "\n")
                    out_f.flush()

                if i % 100 == 0:
                    print(f"  [{i}/{len(todo)}] success={success}, errors={errors}, tokens={total_tokens:,}")

    print(f"\nDone! success={success}, errors={errors}, total_tokens={total_tokens:,}")
    print(f"Output: {OUTPUT_FILE}")


if __name__ == "__main__":
    main()