#!/usr/bin/env python3
import os
import json
import package.utils as utils
import tempfile
from pathlib import Path
from moviepy.video.VideoClip import TextClip

from pathlib import Path

def get_video_info(input_path: Path) -> tuple:
    """Uses ffprobe to instantly read input video dimensions and frame rate."""
    cmd = f"ffprobe -v error -select_streams v:0 -show_entries stream=width,height,r_frame_rate -of json {input_path}"
    # Run command and capture output (assumes linux.run_command prints or you use subprocess)
    import subprocess
    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
    #linux.run_command(cmd)
    try:
        data = json.loads(result.stdout)
        stream = data['streams'][0]
        w = int(stream['width'])
        h = int(stream['height'])
        # Convert fractional FPS string (e.g. "60/1" or "30000/1001") to float
        fps_parts = stream['r_frame_rate'].split('/')
        fps = float(fps_parts[0]) / float(fps_parts[1]) if len(fps_parts) > 1 else float(fps_parts[0])
        return w, h, fps
    except Exception:
        return 1920, 1080, 60.0  # Safe defaults if probe fails

def fit_to_9_16_letterbox(input_path: str, top_text: str = "TOP TEXT", bottom_text: str = "BOTTOM TEXT", use_blur: bool = True, force: bool = False):
    threads = "8"
    input_file = Path(input_path)
    output_suffix = "gaussian_9_16" if use_blur else "black_9_16"
    output_path = input_file.parent / f"{input_file.stem}_{output_suffix}{input_file.suffix}"

    if os.path.exists(output_path):
        if not force:
            print(f"❌ Error: Short video already exists: {output_path}")
            return
        else:
            os.remove(output_path)

    # 1. Probe input metadata instantly
    orig_w, orig_h, fps = get_video_info(input_file)
    canvas_w = 1080
    canvas_h = 1920

    print("✍️ Generating text overlay graphics via MoviePy...")
    # Render static images for text instead of running a video context
    title_clip = TextClip(
        text=top_text, font_size=55, color="white", font="DejaVuSans-Bold",
        text_align="center", size=(canvas_w - 100, 300), method="caption"
    )
    bottom_clip = TextClip(
        text=bottom_text, font_size=55, color="white", font="DejaVuSans-Bold",
        text_align="center", size=(canvas_w - 100, 300), method="caption"
    )

    # Save text layers to temporary PNGs
    top_png = tempfile.NamedTemporaryFile(suffix=".png", delete=False).name
    bottom_png = tempfile.NamedTemporaryFile(suffix=".png", delete=False).name
    title_clip.save_frame(top_png)
    bottom_clip.save_frame(bottom_png)
    
    title_clip.close()
    bottom_clip.close()

    print("🎬 Dispatching compilation workload to FFmpeg filtergraph...")
    
    # 2. Build the complex FFmpeg filtergraph
    # [0:v] is the raw input video stream
    filter_complex = []

    if use_blur:
        # Scale height to 1920, crop center 1080x1920, apply fast boxblur (power of 3 approximates Gaussian)
        filter_complex.append(
            f"[0:v]scale=-1:{canvas_h},crop={canvas_w}:{canvas_h}:(iw-{canvas_w})/2:0,boxblur=luma_radius=35:luma_power=3[bg];"
        )
    else:
        # Generate a pure black background canvas matching video frame specs
        filter_complex.append(
            f"color=c=black:s={canvas_w}x{canvas_h}:r={fps}[bg];"
        )

    # Scale the foreground video to a clean 1080 width, keeping aspect ratio
    filter_complex.append(
        f"[0:v]scale={canvas_w}:-1[fg];"
    )

    # Layer composition chain:
    # Overlay 1: Put scaled foreground onto background (centered vertically)
    filter_complex.append(
        f"[bg][fg]overlay=0:(H-h)/2[tmp1];"
    )
    # Overlay 2: Drop top text asset onto position Y=180
    filter_complex.append(
        f"[tmp1][1:v]overlay=(W-w)/2:180[tmp2];"
    )
    # Overlay 3: Drop bottom text asset onto position Y=1430
    filter_complex.append(
        f"[tmp2][2:v]overlay=(W-w)/2:1430[finalv]"
    )

    filter_graph = "".join(filter_complex)

    # 3. Execute the native assembly command
    # -map_chapters -1 -sn: Strips unnecessary metadata chunks instantly
    # -c:a copy: Safely pulls original digital audio directly without decompression cycles
    # -threads 0: Forces FFmpeg to auto-consume all available processing cores
    ffmpeg_cmd = (
        f'ffmpeg -y -v error -i "{input_file}" -i "{top_png}" -i "{bottom_png}" '
        f'-filter_complex "{filter_graph}" '
        f'-map "[finalv]" -map 0:a? -c:v libx264 -crf 18 -preset slow -pix_fmt yuv420p '
        f'-c:a copy -map_chapters -1 -sn -threads {threads} "{output_path}"'
    )

    try:
        utils.run_command(ffmpeg_cmd)
        print(f"🎉 High-speed processing complete! Video saved to: {output_path}")
    finally:
        # Clean up temporary PNG picture files safely
        for path in (top_png, bottom_png):
            if os.path.exists(path):
                os.unlink(path)

if __name__ == "__main__":
    import time

    start_time = time.perf_counter()

    creator_name = "greenskiesbluegrass"
    top_txt = "TeamPGP Live on Twitch...\n Every Friday and Sunday @7:30 ET.\n twitch.tv/teampgp"
    bottom_txt = f"Clipped By: {creator_name}."
    fit_to_9_16_letterbox("download/clips/AbnegateAgitatedGrassPJSalt/AbnegateAgitatedGrassPJSalt.mp4", top_txt, bottom_txt, True, True)

    end_time = time.perf_counter()
    execution_time = end_time - start_time

    print(f"The function took {execution_time:.6f} seconds to complete.")