Create app.py

app.py

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+import gradio as gr
+import librosa
+import librosa.display
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+from diffusers import StableDiffusionPipeline
+
+# Load AI Art Model (Stable Diffusion)
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+
+def process_audio(audio_file):
+    # Load audio file
+    y, sr = librosa.load(audio_file, sr=22050)
+
+    # Convert to Mel spectrogram
+    mel_spectrogram = librosa.feature.melspectrogram(y=y, sr=sr)
+    mel_spectrogram = librosa.power_to_db(mel_spectrogram, ref=np.max)
+
+    # Save spectrogram image
+    plt.figure(figsize=(6, 4))
+    librosa.display.specshow(mel_spectrogram, sr=sr, x_axis='time', y_axis='mel')
+    plt.colorbar(format='%+2.0f dB')
+    plt.title("Mel Spectrogram")
+    spectrogram_path = "spectrogram.png"
+    plt.savefig(spectrogram_path)
+    plt.close()
+
+    # Generate AI Art (Based on Audio)
+    prompt = "An abstract painting inspired by music and sound waves"
+    image = pipe(prompt).images[0]
+    image_path = "generated_art.png"
+    image.save(image_path)
+
+    return spectrogram_path, image_path
+
+# Create Web Interface
+interface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs=[gr.Image(type="filepath", label="Spectrogram"), 
+             gr.Image(type="filepath", label="AI-Generated Art")],
+    title="Audio2Art 🎨",
+    description="Upload an audio file, and this AI will generate an artwork inspired by the sound."
+)
+
+interface.launch()