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

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/SmolVLM2-2.2B-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,
            _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) -> List[Dict]:
        try:
            # Create temp directory for segments
            temp_dir = tempfile.mkdtemp()
            segments_info = []
            
            # Get video duration
            duration = get_video_duration_seconds(video_path)
            segments_processed = 0
            total_segments = int(duration / 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):
                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
                segment_path = os.path.join(temp_dir, f"segment_{start_time}.mp4")
                cmd = [
                    "ffmpeg",
                    "-y",
                    "-i", video_path,
                    "-ss", str(start_time),
                    "-t", str(end_time - start_time),  # Duration of this segment
                    "-c:v", "libx264",
                    "-preset", "ultrafast",
                    "-pix_fmt", "yuv420p",
                    segment_path
                ]
                subprocess.run(cmd, check=True)
                
                # Analyze segment
                description = self.analyze_segment(segment_path, start_time)
                
                # Add segment info with timestamp
                segments_info.append({
                    "timestamp": format_duration(start_time),
                    "description": description
                })
                
                # Clean up segment file
                os.remove(segment_path)
                
                logger.info(f"Processed segment {segment_idx + 1}/{total_segments} ({start_time}-{end_time}s)")
            
            # Clean up temp directory
            os.rmdir(temp_dir)
            
            return segments_info
            
        except Exception as e:
            logger.error(f"Error processing video: {str(e)}", exc_info=True)
            raise