app.py

import gradio as gr
import librosa
import numpy as np
import re
import os
import time
import struct
import subprocess
import soundfile as sf
import matplotlib.font_manager as fm
from PIL import ImageFont
from typing import Tuple, List, Dict
from mutagen.flac import FLAC
from moviepy import CompositeVideoClip, TextClip, VideoClip, AudioFileClip, ImageClip

# --- Font Scanning and Management ---
def get_font_display_name(font_path: str) -> Tuple[str, str]:
    """
    A robust TTF/TTC parser based on the user's final design.
    It reads the 'name' table to find the localized "Full Font Name" (nameID=4).
    Returns a tuple of (display_name, language_tag {'zh'/'ja'/'ko'/'en'/'other'}).
    """
    def decode_name_string(name_bytes: bytes, platform_id: int, encoding_id: int) -> str:
        """Decodes the name string based on platform and encoding IDs."""
        try:
            if platform_id == 3 and encoding_id in [1, 10]: # Windows, Unicode
                return name_bytes.decode('utf_16_be').strip('\x00')
            elif platform_id == 1 and encoding_id == 0: # Macintosh, Roman
                return name_bytes.decode('mac_roman').strip('\x00')
            elif platform_id == 0: # Unicode
                 return name_bytes.decode('utf_16_be').strip('\x00')
            else: # Fallback
                return name_bytes.decode('utf_8', errors='ignore').strip('\x00')
        except Exception:
            return None

    try:
        with open(font_path, 'rb') as f: data = f.read()
        def read_ushort(offset): return struct.unpack('>H', data[offset:offset+2])[0]
        def read_ulong(offset): return struct.unpack('>I', data[offset:offset+4])[0]
        
        font_offsets = [0]
        # Check for TTC (TrueType Collection) header
        if data[:4] == b'ttcf':
            num_fonts = read_ulong(8)
            font_offsets = [read_ulong(12 + i * 4) for i in range(num_fonts)]
        
        # For simplicity, we only parse the first font in a TTC
        font_offset = font_offsets[0]
        
        num_tables = read_ushort(font_offset + 4)
        name_table_offset = -1
        # Locate the 'name' table
        for i in range(num_tables):
            entry_offset = font_offset + 12 + i * 16
            tag = data[entry_offset:entry_offset+4]
            if tag == b'name':
                name_table_offset = read_ulong(entry_offset + 8); break
        
        if name_table_offset == -1: return None, None
        
        count, string_offset = read_ushort(name_table_offset + 2), read_ushort(name_table_offset + 4)
        name_candidates = {}
        # Iterate through all name records
        for i in range(count):
            rec_offset = name_table_offset + 6 + i * 12
            platform_id, encoding_id, language_id, name_id, length, offset = struct.unpack('>HHHHHH', data[rec_offset:rec_offset+12])

            if name_id == 4:  # We only care about the "Full Font Name"
                string_pos = name_table_offset + string_offset + offset
                value = decode_name_string(data[string_pos : string_pos + length], platform_id, encoding_id)
                
                if value:
                    # Store candidates based on language ID
                    if language_id in [1028, 2052, 3076, 4100, 5124]: name_candidates["zh"] = value # Chinese
                    elif language_id == 1041: name_candidates["ja"] = value # Japanese
                    elif language_id == 1042: name_candidates["ko"] = value # Korean
                    elif language_id in [1033, 0]: name_candidates["en"] = value # English
                    else:
                        if "other" not in name_candidates: name_candidates["other"] = value
        
        # Return the best candidate based on language priority
        if name_candidates.get("zh"): return name_candidates.get("zh"), "zh"
        if name_candidates.get("ja"): return name_candidates.get("ja"), "ja"
        if name_candidates.get("ko"): return name_candidates.get("ko"), "ko"
        if name_candidates.get("other"): return name_candidates.get("other"), "other"
        if name_candidates.get("en"): return name_candidates.get("en"), "en"
        return None, None
        
    except Exception:
        return None, None

def get_font_data() -> Tuple[Dict[str, str], List[str]]:
    """
    Scans system fonts, parses their display names, and returns a sorted list
    with a corresponding name-to-path map.
    """
    font_map = {}
    found_names = [] # Stores (display_name, is_fallback, lang_tag)
    
    # Scan for both .ttf and .ttc files
    ttf_files = fm.findSystemFonts(fontpaths=None, fontext='ttf')
    ttc_files = fm.findSystemFonts(fontpaths=None, fontext='ttc')
    all_font_files = list(set(ttf_files + ttc_files))
    
    for path in all_font_files:
        display_name, lang_tag = get_font_display_name(path)
        is_fallback = display_name is None
        
        if is_fallback:
            # Create a fallback name from the filename
            display_name = os.path.splitext(os.path.basename(path))[0].replace('-', ' ').replace('_', ' ').title()
            lang_tag = 'fallback'
            
        if display_name and display_name not in font_map:
            font_map[display_name] = path
            found_names.append((display_name, is_fallback, lang_tag))
            
    # Define sort priority for languages
    sort_order = {'zh': 0, 'ja': 1, 'ko': 2, 'en': 3, 'other': 4, 'fallback': 5}
    
    # Sort by priority, then alphabetically
    found_names.sort(key=lambda x: (sort_order.get(x[2], 99), x[0]))
    
    sorted_display_names = [name for name, _, _ in found_names]
    return font_map, sorted_display_names

print("Scanning system fonts and parsing names...")
SYSTEM_FONTS_MAP, FONT_DISPLAY_NAMES = get_font_data()
print(f"Scan complete. Found {len(FONT_DISPLAY_NAMES)} available fonts.")


# --- CUE Sheet Parsing Logic ---
def cue_time_to_seconds(time_str: str) -> float:
    try:
        minutes, seconds, frames = map(int, time_str.split(':'))
        return minutes * 60 + seconds + frames / 75.0
    except ValueError:
        return 0.0

def parse_cue_sheet_manually(cue_data: str) -> List[Dict[str, any]]:
    tracks = []
    current_track_info = None
    for line in cue_data.splitlines():
        line = line.strip()
        if line.upper().startswith('TRACK'):
            if current_track_info and 'title' in current_track_info and 'start_time' in current_track_info:
                tracks.append(current_track_info)
            current_track_info = {}
            continue
        if current_track_info is not None:
            title_match = re.search(r'TITLE\s+"(.*?)"', line, re.IGNORECASE)
            if title_match:
                current_track_info['title'] = title_match.group(1)
                continue
            index_match = re.search(r'INDEX\s+01\s+(\d{2}:\d{2}:\d{2})', line, re.IGNORECASE)
            if index_match:
                current_track_info['start_time'] = cue_time_to_seconds(index_match.group(1))
                continue
    if current_track_info and 'title' in current_track_info and 'start_time' in current_track_info:
        tracks.append(current_track_info)
    return tracks


# --- FFmpeg Framerate Conversion ---
def increase_video_framerate(input_path: str, output_path: str, target_fps: int = 24):
    """
    Uses FFmpeg to increase the video's framerate without re-encoding.
    This is extremely fast as it only copies streams and changes metadata.
    
    Args:
        input_path (str): Path to the low-framerate video file.
        output_path (str): Path for the final, high-framerate video file.
        target_fps (int): The desired output framerate.
    """
    print(f"Increasing framerate of '{input_path}' to {target_fps} FPS...")
    
    # Construct the FFmpeg command based on the user's specification
    command = [
        'ffmpeg',
        '-y',  # Overwrite output file if exists
        '-i', input_path,
        '-map', '0',                # Map all streams (video, audio, subtitles)
        '-vf', f'fps={target_fps}', # Use fps filter to convert framerate to 24
        '-c:v', 'libx264',          # Re-encode video with H.264 codec
        '-preset', 'fast',          # Encoding speed/quality tradeoff
        '-crf', '18',               # Quality (lower is better)
        '-c:a', 'copy',             # Copy audio without re-encoding
        output_path
    ]
    
    try:
        # Execute the command
        # Using capture_output to hide ffmpeg logs from the main console unless an error occurs
        result = subprocess.run(command, check=True, capture_output=True, text=True)
        print("Framerate increase successful.")
    except FileNotFoundError:
        # This error occurs if FFmpeg is not installed or not in the system's PATH
        raise gr.Error("FFmpeg not found. Please ensure FFmpeg is installed and accessible in your system's PATH.")
    except subprocess.CalledProcessError as e:
        # This error occurs if FFmpeg returns a non-zero exit code
        print("FFmpeg error output:\n", e.stderr)
        raise gr.Error(f"FFmpeg failed to increase the framerate. See console for details. Error: {e.stderr}")


# --- Main Processing Function ---
def process_audio_to_video(
    audio_files: List[str], image_paths: List[str],
    format_double_digits: bool,
    video_width: int, video_height: int,
    spec_fg_color: str, spec_bg_color: str, 
    font_name: str, font_size: int, font_color: str,
    font_bg_color: str, font_bg_alpha: float,
    pos_h: str, pos_v: str,
    progress=gr.Progress(track_tqdm=True)
) -> str:
    if not audio_files:
        raise gr.Error("Please upload at least one audio file.")
    if not font_name:
        raise gr.Error("Please select a font from the list.")
    
    progress(0, desc="Initializing...")

    # Define paths for temporary and final files
    timestamp = int(time.time())
    temp_fps1_path = f"temp_{timestamp}_fps1.mp4"
    temp_audio_path = f"temp_combined_audio_{timestamp}.wav"
    final_output_path = f"final_video_{timestamp}_fps24.mp4"

    WIDTH, HEIGHT = int(video_width), int(video_height)
    RENDER_FPS = 1 # Render at 1 FPS
    PLAYBACK_FPS = 24 # Final playback framerate
    
    # --- A robust color parser for hex and rgb() strings ---
    def parse_color_to_rgb(color_str: str) -> Tuple[int, int, int]:
        """
        Parses a color string which can be in hex format (#RRGGBB) or
        rgb format (e.g., "rgb(255, 128, 0)").
        Returns a tuple of (R, G, B).
        """
        color_str = color_str.strip()
        if color_str.startswith('#'):
            # Handle hex format
            hex_val = color_str.lstrip('#')
            if len(hex_val) == 3: # Handle shorthand hex like #FFF
                hex_val = "".join([c*2 for c in hex_val])
            return tuple(int(hex_val[i:i+2], 16) for i in (0, 2, 4))
        elif color_str.startswith('rgb'):
            # Handle rgb format
            try:
                numbers = re.findall(r'\d+', color_str)
                return tuple(int(n) for n in numbers[:3])
            except (ValueError, IndexError):
                raise ValueError(f"Could not parse rgb color string: {color_str}")
        else:
            raise ValueError(f"Unknown color format: {color_str}")

    # Use the new robust parser for all color inputs
    fg_rgb, bg_rgb = parse_color_to_rgb(spec_fg_color), parse_color_to_rgb(spec_bg_color)
    grid_rgb = tuple(min(c + 40, 255) for c in bg_rgb)
    
    # Wrap the entire process in a try...finally block to ensure cleanup
    try:
        # --- Define total steps for the progress bar ---
        TOTAL_STEPS = 5
        
        # --- 1. Audio Processing & Track Info Aggregation ---
        all_tracks_info = []
        total_duration = 0.0
        y_accumulator = []
        current_sr = None
        
        # --- Use `progress.tqdm` to create a progress bar for this loop ---
        for file_idx, audio_path in enumerate(progress.tqdm(audio_files, desc=f"Stage 1/{TOTAL_STEPS}: Analyzing Audio Files")):
            # --- Load audio as stereo (or its original channel count) ---
            y, sr = librosa.load(audio_path, sr=None, mono=False)
            # If loaded audio is mono (1D array), convert it to a 2D stereo array
            # by duplicating the channel. This ensures all arrays can be concatenated.
            if y.ndim == 1:
                print(f"  - Converting mono file to stereo: {os.path.basename(audio_path)}")
                y = np.stack([y, y])
            
            if current_sr is None:
                current_sr = sr
            if current_sr != sr:
                print(f"Warning: Sample rate mismatch for {os.path.basename(audio_path)}. Expected {current_sr}Hz, found {sr}Hz.")
                print(f"Resampling from {sr}Hz to {current_sr}Hz...")
                y = librosa.resample(y, orig_sr=sr, target_sr=current_sr)
            
            y_accumulator.append(y)
            # Use the first channel (y[0]) for duration calculation, which is standard practice
            file_duration = librosa.get_duration(y=y[0], sr=current_sr)
            
            # First, try to parse the CUE sheet from the audio file.
            cue_tracks = []
            if audio_path.lower().endswith('.flac'):
                try:
                    audio_meta = FLAC(audio_path)
                    if 'cuesheet' in audio_meta.tags:
                        cue_tracks = parse_cue_sheet_manually(audio_meta.tags['cuesheet'][0])
                        
                        print(f"Successfully parsed {len(cue_tracks)} tracks from CUE sheet.")
                except Exception as e:
                    print(f"Warning: Could not read or parse CUE sheet for {os.path.basename(audio_path)}: {e}")
                    
            # --- Apply New Numbering Logic ---
            file_num = file_idx + 1 # File numbering starts from 1
            if len(audio_files) > 1:
                if cue_tracks: # Scenario 3: Multiple files, this one has CUE
                    for track_idx, track in enumerate(cue_tracks):
                        track_num = track_idx + 1 # Track numbering starts from 1
                        number_str = f"{file_num:02d}-{track_num:02d}" if format_double_digits else f"{file_num}-{track_num}"
                        all_tracks_info.append({
                            "title": track.get('title', 'Unknown Track'),
                            "start_time": total_duration + track.get('start_time', 0),
                            "end_time": total_duration + (cue_tracks[track_idx+1].get('start_time', file_duration) if track_idx + 1 < len(cue_tracks) else file_duration),
                            "number_str": number_str
                        })
                else: # Scenario 2: Multiple files, this one has NO CUE
                    number_str = f"{file_num:02d}" if format_double_digits else str(file_num)
                    all_tracks_info.append({
                        "title": os.path.splitext(os.path.basename(audio_path))[0],
                        "start_time": total_duration, "end_time": total_duration + file_duration,
                        "number_str": number_str
                    })
            else: # Scenario 1: Single file upload
                if cue_tracks: # With CUE
                    for track_idx, track in enumerate(cue_tracks):
                        track_num = track_idx + 1
                        number_str = f"{track_num:02d}" if format_double_digits else str(track_num)
                        all_tracks_info.append({
                            "title": track.get('title', 'Unknown Track'),
                            "start_time": total_duration + track.get('start_time', 0),
                            "end_time": total_duration + (cue_tracks[track_idx+1].get('start_time', file_duration) if track_idx + 1 < len(cue_tracks) else file_duration),
                            "number_str": f"{number_str}." # Add a dot for single file CUE tracks
                        })
                else: # No CUE
                    all_tracks_info.append({
                        "title": os.path.splitext(os.path.basename(audio_path))[0],
                        "start_time": total_duration, "end_time": total_duration + file_duration,
                        "number_str": None # Signal to not show any number
                    })

            total_duration += file_duration

        # --- Concatenate along the time axis (axis=1) for stereo arrays ---
        y_combined = np.concatenate(y_accumulator, axis=1)
        duration = total_duration
        
        # --- Transpose the array for soundfile to write stereo correctly ---
        sf.write(temp_audio_path, y_combined.T, current_sr)
        print(f"Combined all audio files into one. Total duration: {duration:.2f}s")

        # --- Update progress to the next stage, use fractional progress (current/total) ---
        progress(1 / TOTAL_STEPS, desc=f"Stage 2/{TOTAL_STEPS}: Generating Text Overlays")
        
        # --- 2. Text Overlay Logic using the aggregated track info
        text_clips = []
        if all_tracks_info:
            font_path = SYSTEM_FONTS_MAP.get(font_name)
            if not font_path: raise gr.Error(f"Font path for '{font_name}' not found!")
            
            # Use the robust parser for text colors as well
            font_bg_rgb = parse_color_to_rgb(font_bg_color)

            position = (pos_h.lower(), pos_v.lower())

            print(f"Using font: {font_name}, Size: {font_size}, Position: {position}")

            # Create the RGBA tuple for the background color.
            # The alpha value is converted from a 0.0-1.0 float to a 0-255 integer.
            bg_color_tuple = (font_bg_rgb[0], font_bg_rgb[1], font_bg_rgb[2], int(font_bg_alpha * 255))
            
            # 1. Define a maximum width for the caption. 90% of the video width is a good choice.
            caption_width = int(WIDTH * 0.9)
            
            # --- Get font metrics to calculate dynamic padding ---
            try:
                # Load the font with Pillow to access its metrics
                pil_font = ImageFont.truetype(font_path, size=font_size)
                _, descent = pil_font.getmetrics()
                # Calculate a bottom margin to compensate for the font's descent.
                # A small constant is added as a safety buffer.
                # This prevents clipping on fonts with large descenders (like 'g', 'p').
                bottom_margin = int(descent * 0.5) + 2
                print(f"Font '{font_name}' descent: {descent}. Applying dynamic bottom margin of {bottom_margin}px.")
            except Exception as e:
                # Fallback in case of any font loading error
                print(f"Warning: Could not get font metrics for '{font_name}'. Using fixed margin. Error: {e}")
                bottom_margin = int(WIDTH * 0.01) # A small fixed fallback
            
            for track in all_tracks_info:
                text_duration = track['end_time'] - track['start_time']
                if text_duration <= 0:
                    continue
                
                # Construct display text based on pre-formatted number string
                display_text = f"{track['number_str']} {track['title']}" if track['number_str'] else track['title']
                

                # 1. Create the TextClip first without positioning to get its size
                txt_clip = TextClip(
                    text=display_text.strip(),
                    font_size=font_size,
                    color=font_color,
                    font=font_path,
                    bg_color=bg_color_tuple,
                    method='caption', # <-- Set method to caption
                    size=(caption_width, None), # <-- Provide size for wrapping
                    margin=(0, 0, 0, bottom_margin)
                ).with_position(position).with_duration(text_duration).with_start(track['start_time'])

                text_clips.append(txt_clip)
        
        # --- Update progress to the next stage, use fractional progress (current/total) ---
        progress(2 / TOTAL_STEPS, desc=f"Stage 3/{TOTAL_STEPS}: Generating Visual Layers")

        # --- 3. Image and Spectrogram Logic ---
        image_clips = []
        if image_paths and len(image_paths) > 0:
            print(f"Found {len(image_paths)} images to process.")
            
            # Simplified logic: calculate time per image, max 3 mins, and loop.
            img_duration = duration / len(image_paths)
            for i, img_path in enumerate(image_paths):

                # --- HELPER FUNCTION FOR ROBUST IMAGE CLIPS ---
                def create_image_layer(img_path, start, dur):
                    """
                    Creates an image layer that fits entirely within the video frame.
                    It scales the image down to fit and centers it on a transparent background.
                    """
                    # This function implements a "cover" scaling mode to ensure the image
                    # fills the entire video frame without leaving black bars.
                    try:
                        img_clip_raw = ImageClip(img_path)
                    
                        # 1. Calculate scaling factor to "contain" the image (fit inside).
                        #    We use min() to find the ratio that requires the most shrinkage,
                        #    ensuring the whole image fits without being cropped.
                        scale_factor = min(WIDTH / img_clip_raw.w, HEIGHT / img_clip_raw.h)
                        
                        # 2. Resize the image so it fits perfectly within the video dimensions.
                        resized_clip = img_clip_raw.resized(scale_factor)
                        
                        # 3. Create a composite clip to position the resized image on a
                        #    correctly-sized transparent canvas. This is the key to preventing overflow.
                        final_layer = CompositeVideoClip(
                            [resized_clip.with_position("center")],
                            size=(WIDTH, HEIGHT)
                        )

                        # 4. Set the timing on the final composite layer.
                        return final_layer.with_duration(dur).with_start(start)
                    except Exception as e:
                        print(f"Warning: Failed to process image '{img_path}'. Skipping. Error: {e}")
                        return None
                    
                # Create an ImageClip for the duration of the track.
                clip = create_image_layer(img_path, i * img_duration, img_duration)
                if clip:
                    image_clips.append(clip)
        
        N_FFT, HOP_LENGTH, N_BANDS = 2048, 512, 32
        MIN_DB, MAX_DB = -80.0, 0.0

        # Spectrogram calculation on combined audio
        # --- Create a mono version of audio specifically for the spectrogram ---
        # This resolves the TypeError while keeping the final audio in stereo.
        y_mono_for_spec = librosa.to_mono(y_combined)
        S_mel = librosa.feature.melspectrogram(y=y_mono_for_spec, sr=current_sr, n_fft=N_FFT, hop_length=HOP_LENGTH, n_mels=N_BANDS, fmax=current_sr/2)
        S_mel_db = librosa.power_to_db(S_mel, ref=np.max)
        
        # Frame generation logic for the spectrogram
        def frame_generator(t):
            # If images are used as background, the spectrogram's own background should be transparent.
            # Otherwise, use the selected background color.
            # Here, we will use a simple opacity setting on the final clip, so we always generate the frame.
            frame_bg = bg_rgb if not image_clips else (0,0,0) # Use black if it will be made transparent later
            frame = np.full((HEIGHT, WIDTH, 3), frame_bg, dtype=np.uint8)

            # Draw the grid lines only if no images are being used.
            if not image_clips:
                for i in range(1, 9):
                    y_pos = int(i * (HEIGHT / 9)); frame[y_pos-1:y_pos, :] = grid_rgb

            time_idx = min(int((t / duration) * S_mel_db.shape[1]), S_mel_db.shape[1] - 1)
            bar_width = WIDTH / N_BANDS
            for i in range(N_BANDS):
                energy_db = S_mel_db[i, time_idx]
                
                # The denominator should be the range of DB values (MAX_DB - MIN_DB).
                # Since MAX_DB is 0, this simplifies to -MIN_DB, which is a positive 80.0.
                # This prevents the division by zero warning.
                norm_height = np.clip((energy_db - MIN_DB) / (MAX_DB - MIN_DB), 0, 1)
                bar_height = int(np.nan_to_num(norm_height) * HEIGHT)
                if bar_height < 1:
                    continue
                x_start, x_end = int(i * bar_width), int((i + 1) * bar_width - 2)
                y_start = HEIGHT - bar_height
                for k in range(bar_height):
                    y_pos, ratio = y_start + k, k / bar_height
                    r, g, b = (int(c1 * (1-ratio) + c2 * ratio) for c1, c2 in zip(fg_rgb, bg_rgb))
                    frame[y_pos, x_start:x_end] = (r, g, b)
            return frame
            
        video_clip = VideoClip(frame_function=frame_generator, duration=duration)
        
        # --- NEW: Set Spectrogram Opacity ---
        # If image clips were created, make the spectrogram layer 50% transparent.
        if image_clips:
            print("Applying 50% opacity to spectrogram layer.")
            video_clip = video_clip.with_opacity(0.5)

        # --- Use fractional progress (current/total) ---
        progress(3 / TOTAL_STEPS, desc=f"Stage 4/{TOTAL_STEPS}: Rendering Base Video (this may take time)")
        
        # --- 4. Composition and Rendering ---
        audio_clip = AudioFileClip(temp_audio_path)
        
        # --- Clip Composition ---
        # The final composition order is important: images at the bottom, then spectrogram, then text.
        # The base layer is now the list of image clips.
        final_layers = image_clips + [video_clip] + text_clips
        final_clip = CompositeVideoClip(final_layers, size=(WIDTH, HEIGHT)).with_audio(audio_clip)
        
        # Step 1: Render the slow, 1 FPS intermediate file
        print(f"Step 1/2: Rendering base video at {RENDER_FPS} FPS...")
        try:
            # Attempt to copy audio stream directly
            print("Attempting to copy audio stream directly...")
            final_clip.write_videofile(
                temp_fps1_path, codec="libx264", audio_codec="copy", fps=RENDER_FPS,
                logger='bar', threads=os.cpu_count(), preset='ultrafast'
            )
            print("Audio stream successfully copied!")
        except Exception:
            # Fallback to AAC encoding if copy fails
            print("Direct audio copy failed, falling back to high-quality AAC encoding...")
            final_clip.write_videofile(
                temp_fps1_path, codec="libx264", audio_codec="aac",
                audio_bitrate="320k", fps=RENDER_FPS,
                logger='bar', threads=os.cpu_count(), preset='ultrafast')
            print("High-quality AAC audio encoding complete.")
        
        final_clip.close()
        
        # Step 2: Use FFmpeg to quickly increase the framerate to 24 FPS
        print(f"\nStep 2/2: Remuxing video to {PLAYBACK_FPS} FPS...")

        # --- Use fractional progress (current/total) ---
        progress(4 / TOTAL_STEPS, desc=f"Stage 5/{TOTAL_STEPS}: Finalizing Video")

        # --- 5. Finalizing ---
        increase_video_framerate(temp_fps1_path, final_output_path, target_fps=PLAYBACK_FPS)
        
        return final_output_path
        
    except Exception as e:
        # Re-raise the exception to be caught and displayed by Gradio
        raise e
    finally:
        # Step 3: Clean up the temporary file regardless of success or failure
        for f in [temp_fps1_path, temp_audio_path]:
            if os.path.exists(f):
                print(f"Cleaning up temporary file: {f}")
                os.remove(f)

# --- Gradio UI ---
with gr.Blocks(title="Spectrogram Video Generator") as iface:
    gr.Markdown("# Spectrogram Video Generator")
    with gr.Row():
        with gr.Column(scale=1):
            # --- Changed to gr.Files for multi-upload ---
            audio_inputs = gr.Files(
                label="Upload Audio File(s)", 
                file_count="multiple",
                file_types=["audio"]
            )
            
            # --- Image Upload Component ---
            gr.Markdown("### Background Image Options (Optional)")
            gr.Markdown(
                """
                When background images are uploaded, they will be displayed in a looping sequence.
                - The display duration for each image is calculated by dividing the total video length by the number of images, with a maximum duration of **3 minutes** per image.
                - The sequence loops until the video ends.
                """
            )
            image_uploads = gr.File(
                label="Upload Background Images", 
                file_count="multiple", # Allow multiple files
                # Replace the generic "image" category with a specific list of extensions.
                # Note that the dot (.) before each extension is required.
                file_types=[".png", ".jpg", ".jpeg", ".bmp", ".gif", ".webp", ".avif"]

            )
            
            with gr.Accordion("Visualizer Options", open=True):
                with gr.Row():
                    width_input = gr.Number(value=1920, label="Video Width (px)", precision=0)
                    height_input = gr.Number(value=1080, label="Video Height (px)", precision=0)
                fg_color = gr.ColorPicker(value="#71808c", label="Spectrogram Bar Top Color")
                bg_color = gr.ColorPicker(value="#2C3E50", label="Background Color (if no images)")
            
            with gr.Accordion("Text Overlay Options", open=True):
                gr.Markdown(
                    "**Note:** The title overlay feature automatically detects if a file has an embedded CUE sheet. If not, the filename will be used as the title."
                )
                gr.Markdown("---")
                # --- Checkbox for number formatting ---
                format_double_digits_checkbox = gr.Checkbox(label="Format track numbers as double digits (e.g., 01, 05-09)", value=True)
                gr.Markdown("If the CUE sheet or filenames contain non-English characters, please select a compatible font.")
                
                # Define a priority list for default fonts, starting with common Japanese ones.
                # This list can include multiple names for the same font to improve matching.
                preferred_fonts = [
                    "Meiryo", "メイリオ",
                    "Yu Gothic", "游ゴシック",
                    "MS Gothic", "ＭＳ ゴシック",
                    "Hiragino Kaku Gothic ProN", # Common on macOS
                    "Microsoft JhengHei", # Fallback to Traditional Chinese
                    "Arial" # Generic fallback
                ]
                default_font = None
                # Find the first available font from the preferred list
                for font in preferred_fonts:
                    if font in FONT_DISPLAY_NAMES:
                        default_font = font
                        break
                    
                # If none of the preferred fonts are found, use the first available font as a last resort
                if not default_font and FONT_DISPLAY_NAMES:
                    default_font = FONT_DISPLAY_NAMES[0]

                font_name_dd = gr.Dropdown(choices=FONT_DISPLAY_NAMES, value=default_font, label="Font Family")
                
                with gr.Row():
                    font_size_slider = gr.Slider(minimum=12, maximum=256, value=80, step=1, label="Font Size")
                    font_color_picker = gr.ColorPicker(value="#FFFFFF", label="Font Color")
                
                with gr.Row():
                    font_bg_color_picker = gr.ColorPicker(value="#000000", label="Text BG Color")
                    font_bg_alpha_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.6, step=0.05, label="Text BG Opacity")
                
                gr.Markdown("Text Position")
                with gr.Row():
                    pos_h_radio = gr.Radio(["left", "center", "right"], value="center", label="Horizontal Align")
                    pos_v_radio = gr.Radio(["top", "center", "bottom"], value="bottom", label="Vertical Align")
            
            submit_btn = gr.Button("Generate Video", variant="primary")
            
        with gr.Column(scale=2):
            video_output = gr.Video(label="Generated Video")
    
    # --- Update inputs for the click event ---
    submit_btn.click(
        fn=process_audio_to_video,
        inputs=[
            audio_inputs, image_uploads,
            format_double_digits_checkbox,
            width_input, height_input,
            fg_color, bg_color, 
            font_name_dd, font_size_slider, font_color_picker,
            font_bg_color_picker, font_bg_alpha_slider,
            pos_h_radio, pos_v_radio
        ],
        outputs=video_output
    )

if __name__ == "__main__":
    iface.launch(inbrowser=True)