import os
import numpy as np
from moviepy import VideoFileClip, TextClip, CompositeVideoClip
from faster_whisper import WhisperModel

def detect_loud_moments(video_path, chunk_duration=5, threshold_db=10):
    print("🔍 Analyzing audio...")
    clip = VideoFileClip(video_path)
    audio = clip.audio.to_soundarray(fps=44100)
    volume = np.linalg.norm(audio, axis=1)
    chunk_size = int(chunk_duration * 44100)

    loud_chunks = []
    max_db = -float('inf')
    for i in range(0, len(volume), chunk_size):
        chunk = volume[i:i+chunk_size]
        db = 20 * np.log10(np.mean(chunk) + 1e-10)
        max_db = max(max_db, db)
        if db > threshold_db:
            start = i / 44100
            loud_chunks.append((start, min(start + chunk_duration, clip.duration)))

    print(f"🔊 Max volume found: {max_db:.2f} dB, threshold: {threshold_db} dB")
    print(f"📈 Found {len(loud_chunks)} loud moments")
    clip.close()
    return loud_chunks

def transcribe_and_extract_subtitles(video_path, start, end):
    print(f"🗣️ Transcribing audio from {start:.2f}s to {end:.2f}s...")
    model = WhisperModel("base", device="cpu", compute_type="int8")
    segments, _ = model.transcribe(video_path, beam_size=5, language="en", vad_filter=True)

    subtitles = []
    for segment in segments:
        if start <= segment.start <= end:
            subtitles.append((segment.start - start, segment.end - start, segment.text))
    return subtitles

def create_short_clip(video_path, start, end, subtitles, output_path):
    print(f"🎬 Creating short: {output_path}")
    clip = VideoFileClip(video_path).subclipped(start, end)
    video_duration = clip.duration
    print(f"📏 Video clip duration: {video_duration:.2f}s")

    vertical_clip = clip.resized(height=1920).cropped(width=1080, x_center=clip.w / 2)
    clips = [vertical_clip]

    subtitle_y_px = 1550  # Fixed Y position for subtitles

    for (s, e, text) in subtitles:
        try:
            subtitle_start = max(0, s)
            subtitle_end = min(e, video_duration)

            if subtitle_start >= video_duration or subtitle_end <= subtitle_start:
                print(f"⚠️ Skipping subtitle outside video duration: {text[:30]}...")
                continue

            words = text.strip().split()
            if not words:
                continue

            # Split into small readable chunks (max ~3-4 words)
            chunks = []
            current_chunk = []
            for word in words:
                current_chunk.append(word)
                if len(current_chunk) >= 2 or len(' '.join(current_chunk)) > 25:
                    chunks.append(' '.join(current_chunk))
                    current_chunk = []
            if current_chunk:
                chunks.append(' '.join(current_chunk))

            chunk_duration = (subtitle_end - subtitle_start) / len(chunks)

            for chunk_idx, chunk_text in enumerate(chunks):
                chunk_start = subtitle_start + (chunk_idx * chunk_duration)
                chunk_end = min(chunk_start + chunk_duration, subtitle_end)

                chunk_words = chunk_text.split()

                # Base subtitle
                base_subtitle = TextClip(
                    text=chunk_text.upper(),
                    font='C:/Windows/Fonts/LatoWeb-Bold.ttf',
                    font_size=65,
                    color='white',
                    stroke_color='black',
                    stroke_width=5
                )
                text_width, _ = base_subtitle.size
                base_subtitle = base_subtitle.with_start(chunk_start).with_end(chunk_end).with_position(('center', subtitle_y_px))
                clips.append(base_subtitle)

                # Highlighted words (perfectly aligned)
                word_duration = chunk_duration / len(chunk_words)
                current_x = 540 - (text_width / 2)  # 540 is center X of 1080px width

                for i, word in enumerate(chunk_words):
                    word_start = chunk_start + (i * word_duration)
                    word_end = min(word_start + word_duration * 0.8, chunk_end)

                    highlighted_word = TextClip(
                        text=word.upper(),
                        font='C:/Windows/Fonts/LatoWeb-Bold.ttf',
                        font_size=68,
                        color='#FFD700',
                        stroke_color='#FF6B35',
                        stroke_width=5
                    )
                    word_width, _ = highlighted_word.size

                    word_x = current_x + (word_width / 2)
                    highlighted_word = highlighted_word.with_start(word_start).with_end(word_end).with_position((word_x -8, subtitle_y_px))
                    clips.append(highlighted_word)

                    current_x += word_width + 20  # Add spacing between words

            print(f"✅ Added Opus-style subtitle ({subtitle_start:.1f}s-{subtitle_end:.1f}s): {text[:30]}...")
        except Exception as e:
            print(f"⚠️ Subtitle error: {e}, skipping subtitle: {text[:50]}...")
            continue

    final = CompositeVideoClip(clips, size=(1080, 1920))
    final.write_videofile(output_path, codec="libx264", audio_codec="aac", threads=1)

    clip.reader.close()
    if clip.audio:
        clip.audio.reader.close()
    final.close()

def generate_shorts(video_path, max_clips=3, output_folder="shorts"):
    os.makedirs(output_folder, exist_ok=True)
    best_moments = detect_loud_moments(video_path, threshold_db=-30)
    selected = best_moments[:max_clips]
    for i, (start, end) in enumerate(selected):
        subtitles = transcribe_and_extract_subtitles(video_path, start, end)
        out_path = os.path.join(output_folder, f"short_{i+1}.mp4")
        create_short_clip(video_path, start, end, subtitles, out_path)

if __name__ == "__main__":
    import sys
    if len(sys.argv) < 2:
        print("Usage: python shorts_generator.py your_video.mp4")
    else:
        generate_shorts(sys.argv[1])