src/video_processor/processor.py

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from typing import List, Dict, Generator
import logging
import os
import subprocess
import json
import tempfile
import time

logger = logging.getLogger(__name__)

def _grab_best_device(use_gpu=True):
    if torch.cuda.device_count() > 0 and use_gpu:
        device = "cuda"
    else:
        device = "cpu"
    return device

def get_video_duration_seconds(video_path: str) -> float:
    """Use ffprobe to get video duration in seconds."""
    cmd = [
        "ffprobe",
        "-v", "quiet",
        "-print_format", "json",
        "-show_format",
        video_path
    ]
    result = subprocess.run(cmd, capture_output=True, text=True)
    info = json.loads(result.stdout)
    return float(info["format"]["duration"])

def format_duration(seconds: int) -> str:
    minutes = seconds // 60
    secs = seconds % 60
    return f"{minutes:02d}:{secs:02d}"

DEVICE = _grab_best_device()

logger.info(f"Using device: {DEVICE}")

class VideoAnalyzer:
    def __init__(self):
        if not torch.cuda.is_available():
            raise RuntimeError("CUDA is required but not available!")
            
        logger.info("Initializing VideoAnalyzer")
        self.model_path = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"
        logger.info(f"Loading model from {self.model_path} - Using device: {DEVICE}")
        
        # Load processor and model
        self.processor = AutoProcessor.from_pretrained(self.model_path)

        self.model = AutoModelForImageTextToText.from_pretrained(
            self.model_path,
            torch_dtype=torch.bfloat16,
            device_map=DEVICE,
            _attn_implementation="flash_attention_2"
        ).to(DEVICE)
        logger.info(f"Model loaded on device: {self.model.device}")
        
    def analyze_segment(self, video_path: str, start_time: float) -> str:
        """Analyze a single video segment."""
        messages = [
            {
                "role": "system",
                "content": [{"type": "text", "text": """You are a detailed video analysis assistant with expertise in scene description. Your task is to:
1. Describe the visual content with precise details
2. Note any significant actions or movements
3. Describe important objects, people, or elements in the scene
4. Capture the mood, atmosphere, or emotional content if present
5. Mention any scene transitions or camera movements
Be specific and thorough, but focus only on what is visually present in this segment."""}]
            },
            {
                "role": "user",
                "content": [
                    {"type": "video", "path": video_path},
                    {"type": "text", "text": """Describe this video segment in detail. Focus on:
- What objects, people, or elements are visible?
- What actions or movements are occurring?
- What is the setting or environment?
- Are there any notable visual effects or transitions?
- What is the overall mood or atmosphere?
Be specific about visual details but stay concise."""}
                ]
            }
        ]
        
        inputs = self.processor.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            return_tensors="pt"
        ).to(DEVICE, dtype=torch.bfloat16)
        
        outputs = self.model.generate(
            **inputs,
            do_sample=True,
            temperature=0.7,
            max_new_tokens=256
        )
        return self.processor.batch_decode(outputs, skip_special_tokens=True)[0].split("Assistant: ")[-1]

    def process_video(self, video_path: str, segment_length: int = 10) -> Generator[Dict, None, None]:
        try:
            # Create temp directory for segments
            temp_dir = tempfile.mkdtemp()
            
            # Get video duration
            duration = get_video_duration_seconds(video_path)
            total_segments = (int(duration) + segment_length - 1) // segment_length
            logger.info(f"Processing {total_segments} segments for video of length {duration:.2f} seconds")
            
            # Process video in segments
            for segment_idx in range(total_segments):
                segment_start_time = time.time()
                start_time = segment_idx * segment_length
                end_time = min(start_time + segment_length, duration)
                
                # Skip if we've reached the end
                if start_time >= duration:
                    break
                
                # Create segment - Optimized ffmpeg settings
                segment_path = os.path.join(temp_dir, f"segment_{start_time}.mp4")
                cmd = [
                    "ffmpeg",
                    "-y",  # Overwrite output files
                    "-hwaccel", "cuda",  # Use CUDA hardware acceleration
                    "-hwaccel_output_format", "cuda",  # Keep frames in GPU memory
                    "-threads", "0",  # Use all available CPU threads
                    "-thread_type", "frame",  # Frame-level multi-threading
                    "-i", video_path,
                    "-ss", str(start_time),  # Seek position
                    "-t", str(end_time - start_time),  # Duration
                    "-c:v", "h264_nvenc",  # Use NVIDIA hardware encoder
                    "-preset", "p1",  # Lowest latency preset for NVENC
                    "-tune", "ll",  # Low latency tuning
                    "-rc", "vbr",  # Variable bitrate mode
                    "-cq", "28",  # Quality-based VBR
                    "-b:v", "0",  # Let VBR control bitrate
                    "-vf", "scale_cuda=640:-2",  # GPU-accelerated scaling
                    "-an",  # Remove audio
                    segment_path
                ]
                
                ffmpeg_start = time.time()
                try:
                    result = subprocess.run(cmd, check=True, capture_output=True, text=True)
                    logger.debug(f"FFmpeg output: {result.stderr}")
                except subprocess.CalledProcessError as e:
                    logger.error(f"FFmpeg error: {e.stderr}")
                    # Fallback to CPU if GPU encoding fails
                    logger.warning("Falling back to CPU encoding")
                    cmd = [
                        "ffmpeg",
                        "-y",
                        "-threads", "0",
                        "-i", video_path,
                        "-ss", str(start_time),
                        "-t", str(end_time - start_time),
                        "-c:v", "libx264",
                        "-preset", "ultrafast",
                        "-tune", "fastdecode",
                        "-crf", "28",
                        "-vf", "scale=640:-2",
                        "-an",
                        "-pix_fmt", "yuv420p",
                        segment_path
                    ]
                    subprocess.run(cmd, check=True, capture_output=True)
                ffmpeg_time = time.time() - ffmpeg_start
                
                # Analyze segment
                inference_start = time.time()
                description = self.analyze_segment(segment_path, start_time)
                inference_time = time.time() - inference_start
                
                # Add segment info with timestamp
                yield {
                    "timestamp": format_duration(int(start_time)),
                    "description": description,
                    "processing_times": {
                        "ffmpeg": ffmpeg_time,
                        "inference": inference_time,
                        "total": time.time() - segment_start_time
                    }
                }
                
                # Clean up segment file
                os.remove(segment_path)
                
                logger.info(
                    f"Segment {segment_idx + 1}/{total_segments} ({start_time}-{end_time}s) - "
                    f"FFmpeg: {ffmpeg_time:.2f}s, Inference: {inference_time:.2f}s"
                )
            
            # Clean up temp directory
            os.rmdir(temp_dir)
            
        except Exception as e:
            logger.error(f"Error processing video: {str(e)}", exc_info=True)
            raise