Update steganography.py

steganography.py

@@ -1,8 +1,5 @@
 import logging
 import tempfile
-import os
-from io import BytesIO
-
 import gradio as gr
 import librosa
 import librosa.display
@@ -11,18 +8,12 @@ import numpy as np
 import soundfile as sf
 from PIL import Image, ImageDraw, ImageFont
 
-# Constants
 DEFAULT_FONT_PATH = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf"
 DEFAULT_SAMPLE_RATE = 22050
 
-# Setup logging
 logging.basicConfig(level=logging.INFO)
 
-
-# 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
-):
+def text_to_spectrogram_image(text, base_width=512, height=256, max_font_size=80, margin=10, letter_spacing=5):
     try:
         font = ImageFont.truetype(DEFAULT_FONT_PATH, max_font_size)
     except IOError:
@@ -44,7 +35,6 @@ def text_to_spectrogram_image(
         - draw.textbbox((0, 0), text[0], font=font)[1]
     )
 
-    # Adjust width and height based on text size
     width = max(base_width, text_width + margin * 2)
     height = max(height, text_height + margin * 2)
 
@@ -63,29 +53,20 @@ def text_to_spectrogram_image(
     image = np.where(image > 0, 255, image)
     return image
 
-
-# Converting an image to audio
 def spectrogram_image_to_audio(image, sr=DEFAULT_SAMPLE_RATE):
     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
-    )
+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)
 
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
         audio_path = temp_audio.name
         sf.write(audio_path, y, DEFAULT_SAMPLE_RATE)
 
-    # Create spectrogram from audio
     S = librosa.feature.melspectrogram(y=y, sr=DEFAULT_SAMPLE_RATE)
     S_dB = librosa.power_to_db(S, ref=np.max)
     plt.figure(figsize=(10, 4))
@@ -95,15 +76,11 @@ def create_audio_with_spectrogram(
 
     with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_spectrogram:
         spectrogram_path = temp_spectrogram.name
-        plt.savefig(
-            spectrogram_path, bbox_inches="tight", pad_inches=0, transparent=True
-        )
+        plt.savefig(spectrogram_path, bbox_inches="tight", pad_inches=0, transparent=True)
     plt.close()
 
     return audio_path, spectrogram_path
 
-
-# Function for displaying the spectrogram of an audio file
 def display_audio_spectrogram(audio_path):
     y, sr = librosa.load(audio_path, sr=None)
     S = librosa.feature.melspectrogram(y=y, sr=sr)
@@ -114,14 +91,12 @@ def display_audio_spectrogram(audio_path):
     plt.axis("off")
     plt.tight_layout(pad=0)
 
-    buf = BytesIO()
-    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0, transparent=True)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_spectrogram:
+        spectrogram_path = temp_spectrogram.name
+        plt.savefig(spectrogram_path, bbox_inches="tight", pad_inches=0, transparent=True)
     plt.close()
-    buf.seek(0)
-    return buf
-
+    return spectrogram_path
 
-# Converting a downloaded image to an audio spectrogram
 def image_to_spectrogram_audio(image_path, sr=DEFAULT_SAMPLE_RATE):
     image = Image.open(image_path).convert("L")
     image = np.array(image)
@@ -132,46 +107,26 @@ def image_to_spectrogram_audio(image_path, sr=DEFAULT_SAMPLE_RATE):
         sf.write(img2audio_path, y, sr)
     return img2audio_path
 
-
-# Gradio interface
 def gradio_interface_fn(text, base_width, height, max_font_size, margin, letter_spacing):
-    logging.info(f"Generating audio and spectrogram for text:\n{text}\n")
     audio_path, spectrogram_path = create_audio_with_spectrogram(text, base_width, height, max_font_size, margin, letter_spacing)
     return audio_path, spectrogram_path
 
-
 def gradio_image_to_audio_fn(upload_image):
-    logging.info(f"Converting image to audio:\n{upload_image}\n")
     return image_to_spectrogram_audio(upload_image)
 
-
 def gradio_decode_fn(upload_audio):
-    logging.info(f"Generating spectrogram for audio:\n{upload_audio}\n")
     return display_audio_spectrogram(upload_audio)
 
-
-with gr.Blocks(
-    title="Audio Steganography",
-    css="footer{display:none !important}",
-    theme=gr.themes.Soft(
-        primary_hue="green", secondary_hue="green", spacing_size="sm", radius_size="lg"
-    ),
-) as txt2spec:
+with gr.Blocks(title="Audio Steganography", css="footer{display:none !important}", theme=gr.themes.Soft(primary_hue="green", secondary_hue="green", spacing_size="sm", radius_size="lg")) as txt2spec:
     with gr.Tab("Text to Spectrogram"):
         with gr.Group():
             text = gr.Textbox(lines=2, placeholder="Enter your text:", label="Text", info="Enter the text you want to convert to audio.")
             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"
-                )
+                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", variant="primary", size="lg")
 
         with gr.Column(variant="panel"):
@@ -179,42 +134,28 @@ with gr.Blocks(
                 output_audio = gr.Audio(type="filepath", label="Generated audio")
                 output_spectrogram = gr.Image(type="filepath", label="Spectrogram")
 
-        generate_button.click(
-            gradio_interface_fn,
-            inputs=[text, base_width, height, max_font_size, margin, letter_spacing],
-            outputs=[output_audio, output_spectrogram],
-        )
+        generate_button.click(gradio_interface_fn, inputs=[text, base_width, height, max_font_size, margin, letter_spacing], outputs=[output_audio, output_spectrogram])
 
     with gr.Tab("Image to Spectrogram"):
         with gr.Group():
             with gr.Column():
                 upload_image = gr.Image(type="filepath", label="Upload image")
-                convert_button = gr.Button(
-                    "Convert to audio", variant="primary", size="lg"
-                )
+                convert_button = gr.Button("Convert to audio", variant="primary", size="lg")
 
         with gr.Column(variant="panel"):
             output_audio_from_image = gr.Audio(type="filepath", label="Generated audio")
 
-        convert_button.click(
-            gradio_image_to_audio_fn,
-            inputs=[upload_image],
-            outputs=[output_audio_from_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.Column():
                 upload_audio = gr.Audio(type="filepath", label="Upload audio", scale=3)
-                decode_button = gr.Button(
-                    "Show spectrogram", variant="primary", size="lg"
-                )
+                decode_button = gr.Button("Show spectrogram", variant="primary", size="lg")
 
         with gr.Column(variant="panel"):
             decoded_image = gr.Image(type="filepath", label="Audio Spectrogram")
 
-        decode_button.click(
-            gradio_decode_fn, inputs=[upload_audio], outputs=[decoded_image]
-        )
+        decode_button.click(gradio_decode_fn, inputs=[upload_audio], outputs=[decoded_image])
 
 txt2spec.launch(share=True)