python
#!/usr/bin/env python3
# fast_deepfake_check.py
# Requires: ffmpeg, requests, Pillow, openai
# pip install requests Pillow openai

import os, sys, json, subprocess, base64, requests, openai
from pathlib import Path
from PIL import Image

# ---------- CONFIG ----------
SENSITY_API_KEY = os.getenv("SENSITY_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
MAX_FRAMES = 10               # How many frames to sample
TMP_DIR = Path("/tmp/deepfake_check")
TMP_DIR.mkdir(parents=True, exist_ok=True)
# ---------------------------

def download_video(url: str) -> Path:
    out = TMP_DIR / "source.mp4"
    subprocess.run(
        ["ffmpeg", "-y", "-i", url, "-c", "copy", str(out)],
        stdout=subprocess.DEVNULL,
        stderr=subprocess.DEVNULL,
    )
    return out

def extract_frames(video_path: Path) -> list[Path]:
    """Grab MAX_FRAMES evenly spaced frames."""
    out_pattern = TMP_DIR / "frame_%03d.jpg"
    subprocess.run(
        [
            "ffmpeg",
            "-i",
            str(video_path),
            "-vf",
            f"select='not(mod(n, {int(30/MAX_FRAMES)}))',scale=640:-1",
            "-vframes",
            str(MAX_FRAMES),
            str(out_pattern),
        ],
        stdout=subprocess.DEVNULL,
        stderr=subprocess.DEVNULL,
    )
    return sorted(TMP_DIR.glob("frame_*.jpg"))

def sensity_score(frames: list[Path]) -> float:
    """Call Sensity’s /video/scan endpoint (batch of base64 images)."""
    payload = {
        "api_key": SENSITY_API_KEY,
        "frames": [base64.b64encode(p.read_bytes()).decode() for p in frames],
    }
    r = requests.post("https://api.sensity.ai/v1/video/scan", json=payload, timeout=30)
    r.raise_for_status()
    # API returns avg confidence 0‑1
    return r.json().get("deepfake_confidence", 0.0)

def openai_score(frames: list[Path]) -> float:
    """Ask GPT‑4‑Vision to judge the batch."""
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Analyze these frames and give a deepfake confidence (0‑100)."},
                *[{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64.b64encode(p.read_bytes()).decode()}"}} for p in frames],
            ],
        }
    ]
    response = openai.ChatCompletion.create(
        model="gpt-4-vision-preview",
        messages=messages,
        temperature=0,
    )
    answer = response.choices[0].message.content
    # Extract number from text, fallback to 0
    try:
        return float(''.join(filter(str.isdigit, answer.split("%")[0]))) / 100
    except Exception:
        return 0.0

def main(video_url: str):
    print(f"🔎 Analyzing: {video_url}")
    video = download_video(video_url)
    frames = extract_frames(video)
    if not frames:
        print("❌ No frames extracted – check the URL.")
        sys.exit(1)

    s_score = sensity_score(frames)
    o_score = openai_score(frames)
    final = (s_score + o_score) / 2