"""
YouTube Video Analysis Tool - Extract transcripts or analyze frames from YouTube videos
Author: @mangubee
Date: 2026-01-13

Provides two modes for YouTube video analysis:
- Transcript Mode: youtube-transcript-api (instant, 1-3 seconds) or Whisper fallback
- Frame Mode: Extract video frames and analyze with vision models

Transcript Mode Workflow:
    YouTube URL
        ├─ Has transcript? ✅ → Use youtube-transcript-api (instant)
        └─ No transcript? ❌ → Download audio + Whisper (slower, but works)

Frame Mode Workflow:
    YouTube URL
        ├─ Download video with yt-dlp
        ├─ Extract N frames at regular intervals
        └─ Analyze frames with vision models (summarize findings)

Requirements:
- youtube-transcript-api: pip install youtube-transcript-api
- yt-dlp: pip install yt-dlp
- openai: pip install openai (via src.tools.audio)
- opencv-python: pip install opencv-python (for frame extraction)
- PIL: pip install Pillow (for image handling)
"""

import logging
import os
import re
import tempfile
from typing import Dict, Any, Optional
from pathlib import Path

# ============================================================================
# CONFIG
# ============================================================================
# YouTube URL patterns
YOUTUBE_PATTERNS = [
    r'(?:youtube\.com\/watch\?v=|youtu\.be\/|youtube\.com\/shorts\/)([a-zA-Z0-9_-]{11})',
]

# Audio download settings
AUDIO_FORMAT = "mp3"
AUDIO_QUALITY = "128"  # 128 kbps (sufficient for speech)

# Frame extraction settings
FRAME_COUNT = 6  # Number of frames to extract
FRAME_QUALITY = "worst"  # YouTube-dl format quality for frame extraction (worst = faster download)

# Temporary file cleanup
CLEANUP_TEMP_FILES = True

# ============================================================================
# Logging Setup
# ============================================================================
logger = logging.getLogger(__name__)


# ============================================================================
# Transcript Cache
# ============================================================================

def save_transcript_to_cache(video_id: str, text: str, source: str) -> None:
    """
    Save transcript to _log/ folder for debugging.

    Args:
        video_id: YouTube video ID
        text: Transcript text
        source: "api" or "whisper"
    """
    try:
        log_dir = Path("_log")
        log_dir.mkdir(exist_ok=True)

        cache_file = log_dir / f"{video_id}_transcript.md"
        with open(cache_file, "w", encoding="utf-8") as f:
            f.write(f"# YouTube Transcript\n\n")
            f.write(f"**Video ID:** {video_id}\n")
            f.write(f"**Source:** {source}\n")
            f.write(f"**Length:** {len(text)} characters\n")
            f.write(f"**Generated:** {__import__('datetime').datetime.now().isoformat()}\n\n")
            f.write(f"## Transcript\n\n")
            f.write(f"{text}\n")

        logger.info(f"Transcript saved: {cache_file}")
    except Exception as e:
        logger.warning(f"Failed to save transcript: {e}")


# ============================================================================
# YouTube URL Parser
# =============================================================================

def extract_video_id(url: str) -> Optional[str]:
    """
    Extract video ID from various YouTube URL formats.

    Supports:
    - youtube.com/watch?v=VIDEO_ID
    - youtu.be/VIDEO_ID
    - youtube.com/shorts/VIDEO_ID

    Args:
        url: YouTube URL

    Returns:
        Video ID (11 characters) or None if not found

    Examples:
        >>> extract_video_id("https://youtube.com/watch?v=dQw4w9WgXcQ")
        "dQw4w9WgXcQ"

        >>> extract_video_id("https://youtu.be/dQw4w9WgXcQ")
        "dQw4w9WgXcQ"
    """
    if not url:
        return None

    for pattern in YOUTUBE_PATTERNS:
        match = re.search(pattern, url)
        if match:
            return match.group(1)

    return None


# ============================================================================
# Transcript Extraction (Primary Method)
# =============================================================================

def get_youtube_transcript(video_id: str) -> Dict[str, Any]:
    """
    Get transcript using youtube-transcript-api.

    Args:
        video_id: YouTube video ID (11 characters)

    Returns:
        Dict with structure: {
            "text": str,           # Transcript text
            "video_id": str,       # Video ID
            "source": str,         # "api" or "whisper"
            "success": bool,       # True if transcription succeeded
            "error": str or None   # Error message if failed
        }
    """
    try:
        from youtube_transcript_api import YouTubeTranscriptApi

        logger.info(f"Fetching transcript for video: {video_id}")

        # Get transcript (auto-detect language, prefer English)
        # Note: fetch() is an instance method in newer versions
        api = YouTubeTranscriptApi()
        transcript_list = api.fetch(
            video_id,
            languages=['en', 'en-US', 'en-GB']
        )

        # Clean transcript: remove timestamps, combine segments
        text_parts = []
        for entry in transcript_list:
            text = entry.get('text', '').strip()
            if text:
                text_parts.append(text)

        text = ' '.join(text_parts)

        logger.info(f"Transcript fetched: {len(text)} characters")

        # Save to cache for debugging
        save_transcript_to_cache(video_id, text, "api")

        return {
            "text": text,
            "video_id": video_id,
            "source": "api",
            "success": True,
            "error": None
        }

    except Exception as e:
        error_msg = str(e)
        logger.error(f"YouTube transcript API failed: {error_msg}")

        # Check if error is "No transcript found" (expected for videos without captions)
        if "No transcript found" in error_msg or "Could not retrieve a transcript" in error_msg:
            return {
                "text": "",
                "video_id": video_id,
                "source": "api",
                "success": False,
                "error": "No transcript available (video may not have captions)"
            }

        return {
            "text": "",
            "video_id": video_id,
            "source": "api",
            "success": False,
            "error": f"Transcript API error: {error_msg}"
        }


# ============================================================================
# Audio Fallback (Secondary Method)
# =============================================================================

def download_audio(video_url: str) -> Optional[str]:
    """
    Download audio from YouTube using yt-dlp.

    Args:
        video_url: Full YouTube URL

    Returns:
        Path to downloaded audio file or None if failed
    """
    try:
        import yt_dlp

        logger.info(f"Downloading audio from: {video_url}")

        # Create temp file for audio
        temp_dir = tempfile.gettempdir()
        output_path = os.path.join(temp_dir, f"youtube_audio_{os.getpid()}.{AUDIO_FORMAT}")

        # yt-dlp options: audio only, best quality
        ydl_opts = {
            'format': 'bestaudio/best',
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': AUDIO_FORMAT,
                'preferredquality': AUDIO_QUALITY,
            }],
            'outtmpl': output_path.replace(f'.{AUDIO_FORMAT}', ''),
            'quiet': True,
            'no_warnings': True,
        }

        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.download([video_url])

        # yt-dlp adds .mp3 extension, adjust path
        actual_path = output_path if os.path.exists(output_path) else output_path

        if os.path.exists(actual_path):
            logger.info(f"Audio downloaded: {actual_path} ({os.path.getsize(actual_path)} bytes)")
            return actual_path
        else:
            # Find the file with the correct extension
            for file in os.listdir(temp_dir):
                if file.startswith(f"youtube_audio_{os.getpid()}"):
                    actual_path = os.path.join(temp_dir, file)
                    logger.info(f"Audio downloaded: {actual_path}")
                    return actual_path

        logger.error("Audio file not found after download")
        return None

    except ImportError:
        logger.error("yt-dlp not installed. Run: pip install yt-dlp")
        return None
    except Exception as e:
        logger.error(f"Audio download failed: {e}")
        return None


def transcribe_from_audio(video_url: str) -> Dict[str, Any]:
    """
    Fallback: Download audio and transcribe with Whisper.

    Args:
        video_url: Full YouTube URL

    Returns:
        Dict with structure: {
            "text": str,           # Transcript text
            "video_id": str,       # Video ID
            "source": str,         # "whisper"
            "success": bool,       # True if transcription succeeded
            "error": str or None   # Error message if failed
        }
    """
    video_id = extract_video_id(video_url)

    if not video_id:
        return {
            "text": "",
            "video_id": "",
            "source": "whisper",
            "success": False,
            "error": "Invalid YouTube URL"
        }

    # Download audio
    audio_file = download_audio(video_url)

    if not audio_file:
        return {
            "text": "",
            "video_id": video_id,
            "source": "whisper",
            "success": False,
            "error": "Failed to download audio"
        }

    try:
        # Import transcribe_audio (avoid circular import)
        from src.tools.audio import transcribe_audio

        # Transcribe with Whisper
        result = transcribe_audio(audio_file)

        # Cleanup temp file
        if CLEANUP_TEMP_FILES:
            try:
                os.remove(audio_file)
                logger.info(f"Cleaned up temp file: {audio_file}")
            except Exception as e:
                logger.warning(f"Failed to cleanup temp file: {e}")

        if result["success"]:
            # Save to cache for debugging
            save_transcript_to_cache(video_id, result["text"], "whisper")

            return {
                "text": result["text"],
                "video_id": video_id,
                "source": "whisper",
                "success": True,
                "error": None
            }
        else:
            return {
                "text": "",
                "video_id": video_id,
                "source": "whisper",
                "success": False,
                "error": result.get("error", "Transcription failed")
            }

    except Exception as e:
        logger.error(f"Whisper transcription failed: {e}")
        return {
            "text": "",
            "video_id": video_id,
            "source": "whisper",
            "success": False,
            "error": f"Whisper transcription failed: {str(e)}"
        }


# ============================================================================
# Frame Processing (Video Analysis Mode)
# =============================================================================

def download_video(url: str) -> Optional[str]:
    """
    Download video from YouTube using yt-dlp for frame extraction.

    Args:
        url: Full YouTube URL

    Returns:
        Path to downloaded video file or None if failed
    """
    try:
        import yt_dlp

        logger.info(f"Downloading video from: {url}")

        # Create temp file for video
        temp_dir = tempfile.gettempdir()
        output_path = os.path.join(temp_dir, f"youtube_video_{os.getpid()}")

        # yt-dlp options: video only, lowest quality (faster for frame extraction)
        ydl_opts = {
            'format': f'best[ext=mp4]/best',
            'outtmpl': output_path,
            'quiet': True,
            'no_warnings': True,
        }

        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.download([url])

        # Find the downloaded file (yt-dlp adds extension)
        for file in os.listdir(temp_dir):
            if file.startswith(f"youtube_video_{os.getpid()}"):
                actual_path = os.path.join(temp_dir, file)
                size_mb = os.path.getsize(actual_path) / (1024 * 1024)
                logger.info(f"Video downloaded: {actual_path} ({size_mb:.2f}MB)")
                return actual_path

        logger.error("Video file not found after download")
        return None

    except ImportError:
        logger.error("yt-dlp not installed. Run: pip install yt-dlp")
        return None
    except Exception as e:
        logger.error(f"Video download failed: {e}")
        return None


def extract_frames(video_path: str, count: int = FRAME_COUNT) -> list:
    """
    Extract frames from video at regular intervals.

    Args:
        video_path: Path to video file
        count: Number of frames to extract (default: FRAME_COUNT)

    Returns:
        List of (frame_path, timestamp) tuples
    """
    try:
        import cv2

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

        logger.info(f"Video: {total_frames} frames, {fps:.2f} FPS, {duration:.2f}s duration")

        # Calculate frame indices at regular intervals
        if total_frames <= count:
            frame_indices = list(range(total_frames))
        else:
            interval = total_frames / count
            frame_indices = [int(i * interval) for i in range(count)]

        logger.info(f"Extracting {len(frame_indices)} frames at indices: {frame_indices[:3]}...")

        frames = []
        temp_dir = tempfile.gettempdir()

        for idx, frame_idx in enumerate(frame_indices):
            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
            ret, frame = cap.read()

            if ret:
                timestamp = frame_idx / fps if fps > 0 else 0
                frame_path = os.path.join(temp_dir, f"frame_{os.getpid()}_{idx}.jpg")
                cv2.imwrite(frame_path, frame)
                frames.append((frame_path, timestamp))
                logger.debug(f"Frame {idx}: {timestamp:.2f}s -> {frame_path}")
            else:
                logger.warning(f"Failed to extract frame at index {frame_idx}")

        cap.release()
        logger.info(f"Extracted {len(frames)} frames")
        return frames

    except ImportError:
        logger.error("opencv-python not installed. Run: pip install opencv-python")
        return []
    except Exception as e:
        logger.error(f"Frame extraction failed: {e}")
        return []


def analyze_frames(frames: list, question: str = None) -> Dict[str, Any]:
    """
    Analyze video frames using vision models.

    Args:
        frames: List of (frame_path, timestamp) tuples
        question: Optional question to ask about frames

    Returns:
        Dict with structure: {
            "text": str,           # Summarized analysis
            "video_id": str,       # Video ID (placeholder)
            "source": str,         # "frames"
            "success": bool,       # True if analysis succeeded
            "error": str or None   # Error message if failed
            "frame_count": int,    # Number of frames analyzed
        }
    """
    from src.tools.vision import analyze_image

    if not frames:
        return {
            "text": "",
            "video_id": "",
            "source": "frames",
            "success": False,
            "error": "No frames to analyze",
            "frame_count": 0,
        }

    # Default question for frame analysis
    if not question:
        question = "Describe what you see in this frame. Include any visible text, objects, people, or actions."

    try:
        logger.info(f"Analyzing {len(frames)} frames with vision model...")

        frame_analyses = []

        for idx, (frame_path, timestamp) in enumerate(frames):
            logger.info(f"Analyzing frame {idx + 1}/{len(frames)} at {timestamp:.2f}s...")

            # Customize question with timestamp context
            frame_question = f"This is frame {idx + 1} of {len(frames)} from a video at timestamp {timestamp:.2f} seconds. {question}"

            try:
                result = analyze_image(frame_path, frame_question)
                answer = result.get("answer", "")

                # Add timestamp context
                frame_analyses.append(f"[Frame {idx + 1} @ {timestamp:.2f}s]\n{answer}")

                logger.info(f"Frame {idx + 1} analyzed: {len(answer)} chars")

            except Exception as e:
                logger.warning(f"Frame {idx + 1} analysis failed: {e}")
                frame_analyses.append(f"[Frame {idx + 1} @ {timestamp:.2f}s]\nAnalysis failed: {str(e)}")

        # Cleanup frame files
        if CLEANUP_TEMP_FILES:
            for frame_path, _ in frames:
                try:
                    os.remove(frame_path)
                except Exception as e:
                    logger.warning(f"Failed to cleanup frame {frame_path}: {e}")

        # Combine all frame analyses
        combined_text = "\n\n".join(frame_analyses)

        logger.info(f"Frame analysis complete: {len(combined_text)} chars total")

        return {
            "text": combined_text,
            "video_id": "",
            "source": "frames",
            "success": True,
            "error": None,
            "frame_count": len(frames),
        }

    except Exception as e:
        logger.error(f"Frame analysis failed: {e}")
        return {
            "text": "",
            "video_id": "",
            "source": "frames",
            "success": False,
            "error": f"Frame analysis failed: {str(e)}",
            "frame_count": len(frames),
        }


def process_video_frames(url: str, question: str = None, frame_count: int = FRAME_COUNT) -> Dict[str, Any]:
    """
    Download video, extract frames, and analyze with vision models.

    Args:
        url: Full YouTube URL
        question: Optional question to ask about frames
        frame_count: Number of frames to extract

    Returns:
        Dict with structure: {
            "text": str,           # Combined frame analyses
            "video_id": str,       # Video ID
            "source": str,         # "frames"
            "success": bool,       # True if processing succeeded
            "error": str or None   # Error message if failed
            "frame_count": int     # Number of frames analyzed
        }
    """
    video_id = extract_video_id(url)

    if not video_id:
        return {
            "text": "",
            "video_id": "",
            "source": "frames",
            "success": False,
            "error": "Invalid YouTube URL",
            "frame_count": 0,
        }

    # Download video
    video_file = download_video(url)

    if not video_file:
        return {
            "text": "",
            "video_id": video_id,
            "source": "frames",
            "success": False,
            "error": "Failed to download video",
            "frame_count": 0,
        }

    try:
        # Extract frames
        frames = extract_frames(video_file, frame_count)

        if not frames:
            return {
                "text": "",
                "video_id": video_id,
                "source": "frames",
                "success": False,
                "error": "Failed to extract frames",
                "frame_count": 0,
            }

        # Analyze frames
        result = analyze_frames(frames, question)

        # Cleanup temp video file
        if CLEANUP_TEMP_FILES:
            try:
                os.remove(video_file)
                logger.info(f"Cleaned up temp video: {video_file}")
            except Exception as e:
                logger.warning(f"Failed to cleanup temp video: {e}")

        # Add video_id to result
        result["video_id"] = video_id

        return result

    except Exception as e:
        logger.error(f"Video frame processing failed: {e}")
        return {
            "text": "",
            "video_id": video_id,
            "source": "frames",
            "success": False,
            "error": f"Video processing failed: {str(e)}",
            "frame_count": 0,
        }


# ============================================================================
# Main API Function
# =============================================================================

def youtube_analyze(url: str, mode: str = "transcript") -> Dict[str, Any]:
    """
    Analyze YouTube video using transcript or frame processing mode.

    Transcript Mode: Extract transcript (youtube-transcript-api or Whisper)
    Frame Mode: Extract frames and analyze with vision models

    Args:
        url: YouTube video URL (youtube.com, youtu.be, shorts)
        mode: Analysis mode - "transcript" (default) or "frames"

    Returns:
        Dict with structure: {
            "text": str,           # Transcript or frame analyses
            "video_id": str,       # Video ID
            "source": str,         # "api", "whisper", or "frames"
            "success": bool,       # True if analysis succeeded
            "error": str or None   # Error message if failed
            "frame_count": int     # Number of frames (frame mode only)
        }

    Raises:
        ValueError: If URL is not valid or mode is invalid

    Examples:
        >>> youtube_analyze("https://youtube.com/watch?v=dQw4w9WgXcQ", mode="transcript")
        {"text": "Never gonna give you up...", "video_id": "dQw4w9WgXcQ", "source": "api", "success": True, "error": None}

        >>> youtube_analyze("https://youtube.com/watch?v=dQw4w9WgXcQ", mode="frames")
        {"text": "[Frame 1 @ 0.00s]\nA man...", "video_id": "dQw4w9WgXcQ", "source": "frames", "success": True, "frame_count": 6, "error": None}
    """
    # Validate URL and extract video ID
    video_id = extract_video_id(url)

    if not video_id:
        logger.error(f"Invalid YouTube URL: {url}")
        return {
            "text": "",
            "video_id": "",
            "source": "none",
            "success": False,
            "error": f"Invalid YouTube URL: {url}"
        }

    # Validate mode
    mode = mode.lower()
    if mode not in ("transcript", "frames"):
        logger.error(f"Invalid mode: {mode}")
        return {
            "text": "",
            "video_id": video_id,
            "source": "none",
            "success": False,
            "error": f"Invalid mode: {mode}. Valid: transcript, frames"
        }

    logger.info(f"Processing YouTube video: {video_id} (mode: {mode})")

    # Route to appropriate processing mode
    if mode == "frames":
        # Frame processing mode
        result = process_video_frames(url)
        if result["success"]:
            logger.info(f"Frame analysis complete: {result.get('frame_count', 0)} frames, {len(result['text'])} chars")
        return result

    else:  # mode == "transcript"
        # Transcript mode: Try API first, fallback to Whisper
        result = get_youtube_transcript(video_id)

        if result["success"]:
            logger.info(f"Transcript retrieved via API: {len(result['text'])} characters")
            logger.info(f"Transcript content: {result['text'][:200]}...")
            return result

        # Fallback to audio transcription (slow but works)
        logger.info(f"Transcript API failed, trying audio transcription...")
        result = transcribe_from_audio(url)

        if result["success"]:
            logger.info(f"Transcript retrieved via Whisper: {len(result['text'])} characters")
            logger.info(f"Full transcript: {result['text']}")
        else:
            logger.error(f"All transcript methods failed for video: {video_id}")

        return result


# Backward compatibility wrapper that respects YOUTUBE_MODE environment variable
def youtube_transcript(url: str) -> Dict[str, Any]:
    """
    Wrapper for youtube_analyze that respects YOUTUBE_MODE environment variable.

    This allows the agent to switch between transcript and frame modes
    without changing the function signature used in the graph.

    Mode selection:
    - YOUTUBE_MODE env variable (set by UI): "transcript" or "frames"
    - Default: "transcript" (backward compatible)

    Args:
        url: YouTube video URL

    Returns:
        Dict with structure from youtube_analyze()
    """
    # Read mode from environment variable (set by app.py UI)
    mode = os.getenv("YOUTUBE_MODE", "transcript").lower()

    logger.info(f"youtube_transcript called with YOUTUBE_MODE={mode}")

    return youtube_analyze(url, mode=mode)