import numpy as np
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
import librosa
import librosa.display
import gradio as gr
import soundfile as sf
import os

# Function for creating a spectrogram image with text
def text_to_spectrogram_image(text, base_width=512, height=256, max_font_size=80, margin=10, letter_spacing=5):
    font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf"
    if os.path.exists(font_path):
        font = ImageFont.truetype(font_path, max_font_size)
    else:
        font = ImageFont.load_default()

    image = Image.new('L', (base_width, height), 'black')
    draw = ImageDraw.Draw(image)
    text_width = 0
    for char in text:
        text_bbox = draw.textbbox((0, 0), char, font=font)
        text_width += text_bbox[2] - text_bbox[0] + letter_spacing
    text_width -= letter_spacing

    if text_width + margin * 2 > base_width:
        width = text_width + margin * 2
    else:
        width = base_width

    image = Image.new('L', (width, height), 'black')
    draw = ImageDraw.Draw(image)
    
    text_x = (width - text_width) // 2
    text_y = (height - (text_bbox[3] - text_bbox[1])) // 2
    for char in text:
        draw.text((text_x, text_y), char, font=font, fill='white')
        char_bbox = draw.textbbox((0, 0), char, font=font)
        text_x += char_bbox[2] - char_bbox[0] + letter_spacing
    
    image = np.array(image)
    image = np.where(image > 0, 255, image)
    return image

# Converting an image to audio
def spectrogram_image_to_audio(image, sr=22050):
    flipped_image = np.flipud(image)
    S = flipped_image.astype(np.float32) / 255.0 * 100.0
    y = librosa.griffinlim(S)
    return y

# Function for creating an audio file and spectrogram from text
def create_audio_with_spectrogram(text, base_width, height, max_font_size, margin, letter_spacing):
    spec_image = text_to_spectrogram_image(text, base_width, height, max_font_size, margin, letter_spacing)
    y = spectrogram_image_to_audio(spec_image)
    audio_path = 'output.wav'
    sf.write(audio_path, y, 22050)
    image_path = 'spectrogram.png'
    plt.imsave(image_path, spec_image, cmap='gray')
    return audio_path, image_path

# Function for displaying the spectrogram of an audio file
def display_audio_spectrogram(audio_path):
    y, sr = librosa.load(audio_path)
    S = librosa.feature.melspectrogram(y=y, sr=sr)
    S_dB = librosa.power_to_db(S, ref=np.max)

    plt.figure(figsize=(10, 4))
    librosa.display.specshow(S_dB)
    plt.tight_layout()

    spectrogram_path = 'uploaded_spectrogram.png'
    plt.savefig(spectrogram_path)
    plt.close()
    return spectrogram_path

# Converting a downloaded image to an audio spectrogram
def image_to_spectrogram_audio(image_path, sr=22050):
    image = Image.open(image_path).convert('L')
    image = np.array(image)
    y = spectrogram_image_to_audio(image, sr)
    audio_path = 'image_to_audio_output.wav'
    sf.write(audio_path, y, sr)
    return audio_path

# Gradio interface
with gr.Blocks(title='Audio Steganography', theme=gr.themes.Soft(primary_hue="green", secondary_hue="green", spacing_size="sm", radius_size="lg")) as iface:
    
    with gr.Group():
        with gr.Row(variant='panel'):
            with gr.Column():
                gr.HTML("<center><h2><a href='https://t.me/pol1trees'>Telegram Channel</a></h2></center>")
            with gr.Column():
                gr.HTML("<center><h2><a href='https://t.me/+GMTP7hZqY0E4OGRi'>Telegram Chat</a></h2></center>")
            with gr.Column():
                gr.HTML("<center><h2><a href='https://www.youtube.com/channel/UCHb3fZEVxUisnqLqCrEM8ZA'>YouTube</a></h2></center>")
            with gr.Column():
                gr.HTML("<center><h2><a href='https://github.com/Bebra777228/Audio-Steganography'>GitHub</a></h2></center>")

    with gr.Tab("Text to Spectrogram"):
        with gr.Group():
            text = gr.Textbox(lines=2, placeholder="Enter your text:", label="Text")
            with gr.Row(variant='panel'):
                base_width = gr.Slider(value=512, label="Image Width", visible=False)
                height = gr.Slider(value=256, label="Image Height", visible=False)
                max_font_size = gr.Slider(minimum=10, maximum=130, step=5, value=80, label="Font size")
                margin = gr.Slider(minimum=0, maximum=50, step=1, value=10, label="Indent")
                letter_spacing = gr.Slider(minimum=0, maximum=50, step=1, value=5, label="Letter spacing")
            generate_button = gr.Button("Generate")
        
        with gr.Column(variant='panel'):
            with gr.Group():
                output_audio = gr.Audio(type="filepath", label="Generated audio")
                output_image = gr.Image(type="filepath", label="Spectrogram")

        def gradio_interface_fn(text, base_width, height, max_font_size, margin, letter_spacing):
            return create_audio_with_spectrogram(text, base_width, height, max_font_size, margin, letter_spacing)
        
        generate_button.click(
            gradio_interface_fn,
            inputs=[text, base_width, height, max_font_size, margin, letter_spacing],
            outputs=[output_audio, output_image]
        )

    with gr.Tab("Image to Spectrogram"):
        with gr.Group():
            with gr.Row(variant='panel'):
                upload_image = gr.Image(type="filepath", label="Upload image")
                convert_button = gr.Button("Convert to audio")
            
            with gr.Column(variant='panel'):
                output_audio_from_image = gr.Audio(type="filepath", label="Generated audio")

            def gradio_image_to_audio_fn(upload_image):
                return image_to_spectrogram_audio(upload_image)
            
            convert_button.click(
                gradio_image_to_audio_fn,
                inputs=[upload_image],
                outputs=[output_audio_from_image]
            )

    with gr.Tab("Audio Spectrogram"):
        with gr.Group():
            with gr.Row(variant='panel'):
                upload_audio = gr.Audio(type="filepath", label="Upload audio", scale=3)
                decode_button = gr.Button("Show spectrogram", scale=2)
            
            with gr.Column(variant='panel'):
                decoded_image = gr.Image(type="filepath", label="Audio Spectrogram")

            def gradio_decode_fn(upload_audio):
                return display_audio_spectrogram(upload_audio)
            
            decode_button.click(
                gradio_decode_fn,
                inputs=[upload_audio],
                outputs=[decoded_image]
            )

iface.launch(share=True)