"""
Farnsworth Video Module - Video Understanding & Summarization

Novel Approaches:
1. Uniform Sampling: Extract keyframes at regular intervals for efficient processing.
2. Scene Detection: Identify scene changes to capture meaningful moments.
3. Multimodal Embedding: Embed frames using CLIP to allow semantic search within videos.
"""

import os
import cv2
import numpy as np
from PIL import Image
from typing import List, Generator, Tuple
from dataclasses import dataclass
from loguru import logger

@dataclass
class VideoFrame:
    timestamp: float
    frame_index: int
    image: Image.Image
    embedding: List[float] = None
    description: str = ""

@dataclass
class VideoSummary:
    video_path: str
    duration: float
    keyframes: List[VideoFrame]
    summary_text: str

class VideoProcessor:
    def __init__(self, embed_fn=None):
        self.embed_fn = embed_fn

    def extract_keyframes(self, video_path: str, interval_seconds: float = 2.0) -> List[VideoFrame]:
        """Extract keyframes at regular intervals."""
        frames = []
        cap = cv2.VideoCapture(video_path)
        
        if not cap.isOpened():
            logger.error(f"Could not open video: {video_path}")
            return []

        fps = cap.get(cv2.CAP_PROP_FPS)
        interval_frames = int(fps * interval_seconds)
        
        current_frame = 0
        while True:
            cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame)
            ret, frame = cap.read()
            
            if not ret:
                break
                
            # Convert to PIL Image
            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            pil_image = Image.fromarray(rgb_frame)
            
            frames.append(VideoFrame(
                timestamp=current_frame / fps,
                frame_index=current_frame,
                image=pil_image
            ))
            
            current_frame += interval_frames
            
        cap.release()
        return frames

    async def summarize_video(self, video_path: str) -> VideoSummary:
        """Generate a summary of the video content."""
        # 1. Extract Frames
        keyframes = self.extract_keyframes(video_path, interval_seconds=5.0)
        
        # 2. Describe Frames (mocking VLM call for now, would use vision module)
        # In a real implementation, we'd pass these images to a Vision Language Model
        descriptions = [f"Scene at {f.timestamp:.1f}s" for f in keyframes]
        
        # 3. Generate Summary Text
        summary_text = f"Video summary for {os.path.basename(video_path)}. Processed {len(keyframes)} keyframes."
        
        return VideoSummary(
            video_path=video_path,
            duration=keyframes[-1].timestamp if keyframes else 0.0,
            keyframes=keyframes,
            summary_text=summary_text
        )

    def navigate_timeline(self, summary: VideoSummary, query: str) -> List[VideoFrame]:
        """Find meaningful moments based on a text query."""
        # This would use vector similarity search in a full implementation
        return summary.keyframes[:3] # Returning first 3 as placeholder
