code/test_code/test_gpt41.py

import os
import json
import argparse
import base64
import cv2
import tempfile
import subprocess
import shutil
import io
import time
import glob
from openai import OpenAI
from tqdm import tqdm
from PIL import Image

def get_media_type(file_path: str) -> str:
    ext = os.path.splitext(file_path)[1].lower()
    if ext in ['.mp4', '.avi', '.mov', '.mkv', '.webm']:
        return 'video'
    elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.gif', '.webp']:
        return 'image'
    else:
        raise ValueError(f"Unsupported file format: {ext}")

def encode_media_to_base64(media_path: str) -> str:
    try:
        with open(media_path, "rb") as media_file:
            return base64.b64encode(media_file.read()).decode('utf-8')
    except Exception as e:
        raise IOError(f"Could not read or encode file {media_path}: {e}")

def extract_keyframes(video_path: str, max_frames: int = 20) -> list:
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        raise ValueError(f"Cannot open video file: {video_path}")

    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    video_fps = cap.get(cv2.CAP_PROP_FPS)
    duration = total_frames / video_fps if video_fps > 0 else 0

    if duration <= 20:
        target_frames = min(max_frames, int(duration))
        frame_interval = max(1, int(video_fps))
    else:
        target_frames = max_frames
        frame_interval = max(1, int(total_frames / max_frames))

    keyframes = []
    frame_count = 0
    sampled_count = 0
    while sampled_count < target_frames:
        ret, frame = cap.read()
        if not ret:
            break
        
        if frame_count % frame_interval == 0:
            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            pil_image = Image.fromarray(frame_rgb)
            keyframes.append(pil_image)
            sampled_count += 1
        
        frame_count += 1
    
    cap.release()
    print(f" Extracted {len(keyframes)} keyframes.")
    return keyframes

def extract_audio_to_text(video_path: str, client: OpenAI) -> str:
    temp_audio_path = ""
    try:
        with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_audio:
            temp_audio_path = temp_audio.name

        command = [
            'ffmpeg', '-i', video_path, '-vn', '-acodec', 'mp3', 
            '-ar', '16000', '-ac', '1', '-y', temp_audio_path
        ]
        
        result = subprocess.run(command, capture_output=True, text=True, check=False)
        if result.returncode != 0:
            print(f"  [WARN] Ffmpeg audio extraction failed: {result.stderr}")
            return "Audio extraction failed."

        with open(temp_audio_path, 'rb') as audio_file:
            transcription = client.audio.transcriptions.create(
                model="whisper-1",
                file=audio_file,
                response_format="text"
            )
        return transcription

    except Exception as e:
        print(f" Audio processing failed: {e}")
        return "Audio processing failed."
    finally:
        if temp_audio_path and os.path.exists(temp_audio_path):
            os.unlink(temp_audio_path)

def process_dataset(dataset_path: str, client: OpenAI, model_name: str, result_suffix: str):

    result_path = f"{os.path.splitext(dataset_path)[0]}{result_suffix}"
    
    if os.path.exists(result_path):
        print(f"Result file '{os.path.basename(result_path)}' already exists. Skipping.")
        return

    try:
        with open(dataset_path, 'r', encoding='utf-8') as f:
            data = json.load(f)
    except (json.JSONDecodeError, FileNotFoundError) as e:
        print(f" Could not read or parse JSON file {dataset_path}: {e}")
        return

    all_results = []
    base_dir = os.path.dirname(dataset_path)

    for item in tqdm(data, desc=f"Processing items from {os.path.basename(dataset_path)}"):
        start_time = time.time()
        model_output = ""
        try:
            prompt = item['conversations'][0]['value']
            ground_truth = item['conversations'][1]['value']
            media_key = 'image' if 'image' in item else 'video'
            media_relative_path = item.get(media_key)

            if not media_relative_path:
                raise ValueError("JSON item is missing 'image' or 'video' key.")
            
            media_full_path = os.path.join(base_dir, media_relative_path)
            if not os.path.exists(media_full_path):
                raise FileNotFoundError(f"Media file not found: {media_full_path}")

            media_type = get_media_type(media_full_path)
            clean_prompt = prompt.replace("<image>", "").replace("<video>", "").strip()
            
            messages = []
            if media_type == 'image':
                base64_media = encode_media_to_base64(media_full_path)
                messages = [{
                    "role": "user",
                    "content": [
                        {"type": "text", "text": clean_prompt},
                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_media}"}}
                    ]
                }]
            else: 
                print(f"\n      [INFO] Processing video: {os.path.basename(media_full_path)}")
                keyframes = extract_keyframes(media_full_path)
                audio_text = extract_audio_to_text(media_full_path, client)
                
                enhanced_prompt = f"""Please analyze the following video content:

Original question: {clean_prompt}

Transcribed audio from the video:
"{audio_text}"

Analyze the keyframes and the audio transcription to provide a comprehensive answer."""

                content = [{"type": "text", "text": enhanced_prompt}]
                for frame in keyframes:
                    buffer = io.BytesIO()
                    frame.save(buffer, format='JPEG', quality=85)
                    frame_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
                    content.append({
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{frame_base64}"}
                    })
                
                messages = [{"role": "user", "content": content}]

            response = client.chat.completions.create(
                model=model_name,
                messages=messages,
                max_tokens=1024,
                temperature=0.0
            )
            model_output = response.choices[0].message.content

        except Exception as e:
            model_output = f"ERROR: {str(e)}"
        
        end_time = time.time()
        all_results.append({
            "id": item.get('id', 'N/A'),
            "prompt": prompt,
            "model_output": model_output,
            "ground_truth": ground_truth,
            "processing_time_seconds": round(end_time - start_time, 2)
        })

    # Save results
    with open(result_path, 'w', encoding='utf-8') as f:
        json.dump(all_results, f, indent=4, ensure_ascii=False)
    print(f" Processing complete. Results saved to: {result_path}")

def main():
    parser = argparse.ArgumentParser(
        description="Run inference on a dataset using a specified GPT model."
    )
    parser.add_argument("--api-key", required=True, help="OpenAI API key.")
    parser.add_argument("--model-name", default="gpt-4o", help="The model name to use (e.g., 'gpt-4o').")
    parser.add_argument("--result-suffix", default="_result.json", help="Suffix for generated result files.")
    
    args = parser.parse_args()

    try:
        client = OpenAI(api_key=args.api_key)
    except Exception as e:
        print(f"Failed to initialize OpenAI client: {e}")
        return

    if shutil.which('ffmpeg') is None:
        print("[ffmpeg not found. Video audio extraction will be unavailable.")
    source_files = [
        f for f in glob.glob('*.json') 
        if not f.endswith(args.result_suffix)
    ]

    if not source_files:
        print("\n No source JSON files.")
    else:
        print(f"\nFound {len(source_files)} dataset to process.")

    for dataset_file in sorted(source_files):
        process_dataset(dataset_file, client, args.model_name, args.result_suffix)


if __name__ == "__main__":
    main()