app.py

import os
import torch
import librosa
import numpy as np
import gradio as gr
from sonics import HFAudioClassifier

# Model configurations
MODEL_IDS = {
    "SpecTTTra-α (5s)": "awsaf49/sonics-spectttra-alpha-5s",
    "SpecTTTra-β (5s)": "awsaf49/sonics-spectttra-beta-5s",
    "SpecTTTra-γ (5s)": "awsaf49/sonics-spectttra-gamma-5s",
    "SpecTTTra-α (120s)": "awsaf49/sonics-spectttra-alpha-120s",
    "SpecTTTra-β (120s)": "awsaf49/sonics-spectttra-beta-120s",
    "SpecTTTra-γ (120s)": "awsaf49/sonics-spectttra-gamma-120s",
}

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_cache = {}


def load_model(model_name):
    """Load model if not already cached"""
    if model_name not in model_cache:
        model_id = MODEL_IDS[model_name]
        model = HFAudioClassifier.from_pretrained(model_id)
        model = model.to(device)
        model.eval()
        model_cache[model_name] = model
    return model_cache[model_name]


def process_audio(audio_path, model_name):
    """Process audio file and return prediction"""
    try:
        model = load_model(model_name)
        max_time = model.config.audio.max_time

        # Load and process audio
        audio, sr = librosa.load(audio_path, sr=16000)
        chunk_samples = int(max_time * sr)
        total_chunks = len(audio) // chunk_samples
        middle_chunk_idx = total_chunks // 2

        # Extract middle chunk
        start = middle_chunk_idx * chunk_samples
        end = start + chunk_samples
        chunk = audio[start:end]

        if len(chunk) < chunk_samples:
            chunk = np.pad(chunk, (0, chunk_samples - len(chunk)))

        # Get prediction
        with torch.no_grad():
            chunk = torch.from_numpy(chunk).float().to(device)
            pred = model(chunk.unsqueeze(0))
            prob = torch.sigmoid(pred).cpu().numpy()[0]

        real_prob = 1 - prob
        fake_prob = prob
        
        # Return formatted results
        return {
            "Real": float(real_prob),
            "Fake": float(fake_prob)
        }

    except Exception as e:
        return {"Error": str(e)}


def predict(audio_file, model_name):
    """Gradio interface function"""
    if audio_file is None:
        return {"Message": "Please upload an audio file"}
    return process_audio(audio_file, model_name)


# Updated CSS with better color scheme for resource links
css = """
/* Custom CSS that works with Ocean theme */
.sonics-header {
    text-align: center;
    padding: 20px;
    margin-bottom: 20px;
    border-radius: 10px;
}

.sonics-logo {
    max-width: 150px;
    border-radius: 10px;
    box-shadow: 0 4px 8px rgba(0,0,0,0.3);
}

.sonics-title {
    font-size: 28px;
    margin-bottom: 10px;
}

.sonics-subtitle {
    margin-bottom: 15px;
}

.sonics-description {
    font-size: 16px;
    margin: 0;
}

/* Resource links styling */
.resource-links {
    display: flex;
    justify-content: center;
    flex-wrap: wrap;
    gap: 8px;
    margin-bottom: 25px;
}

.resource-link {
    background-color: #222222;
    color: #4aedd6;
    border: 1px solid #333333;
    padding: 8px 16px;
    border-radius: 20px;
    margin: 5px;
    text-decoration: none;
    display: inline-block;
    font-weight: 500;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.3);
    transition: all 0.2s ease;
}

.resource-link:hover {
    background-color: #333333;
    transform: translateY(-2px);
    box-shadow: 0 3px 6px rgba(0, 0, 0, 0.4);
    transition: all 0.2s ease;
}

.resource-link-icon {
    margin-right: 5px;
}

/* Footer styling */
.sonics-footer {
    text-align: center;
    margin-top: 30px;
    padding: 15px;
}
"""

# Create Gradio interface
with gr.Blocks(css=css, theme=gr.themes.Ocean()) as demo:
    # Title and Logo
    gr.HTML(
        """
        <div class="sonics-header">
            <div style="display: flex; justify-content: center; margin-bottom: 20px;">
                <img src="https://i.postimg.cc/3Jx3yZ5b/real-vs-fake-sonics-w-logo.jpg" class="sonics-logo">
            </div>
            <h1 class="sonics-title">SONICS: Synthetic Or Not - Identifying Counterfeit Songs</h1>
            <h3 class="sonics-subtitle">ICLR 2025 [Poster]</h3>
            <p class="sonics-description">
                Detect if a song is real or AI-generated with our state-of-the-art models.
                Simply upload an audio file to verify its authenticity!
            </p>
        </div>
        """
    )

    # Resource Links - Updated with custom styling to match screenshot
    gr.HTML(
        """
        <div class="resource-links">
            <a href="https://openreview.net/forum?id=PY7KSh29Z8" target="_blank" class="resource-link">
                <span class="resource-link-icon">📄</span>Paper
            </a>
            <a href="https://huggingface.co/datasets/awsaf49/sonics" target="_blank" class="resource-link">
                <span class="resource-link-icon">🎵</span>Dataset
            </a>
            <a href="https://huggingface.co/collections/awsaf49/sonics-spectttra-67bb6517b3920fd18e409013" target="_blank" class="resource-link">
                <span class="resource-link-icon">🤖</span>Models
            </a>
            <a href="https://arxiv.org/abs/2408.14080" target="_blank" class="resource-link">
                <span class="resource-link-icon">🔬</span>ArXiv
            </a>
            <a href="https://github.com/awsaf49/sonics" target="_blank" class="resource-link">
                <span class="resource-link-icon">💻</span>GitHub
            </a>
        </div>
        """
    )

    # Main Interface
    with gr.Row(equal_height=True):
        with gr.Column():
            audio_input = gr.Audio(
                label="Upload Audio File",
                type="filepath",
                elem_id="audio_input"
            )
            
            model_dropdown = gr.Dropdown(
                choices=list(MODEL_IDS.keys()),
                value="SpecTTTra-γ (5s)",
                label="Select Model",
                elem_id="model_dropdown"
            )
            
            submit_btn = gr.Button(
                "✨ Analyze Audio",
                elem_id="submit_btn"
            )

        with gr.Column():
            # Define output before using it in Examples
            output = gr.Label(
                label="Analysis Result",
                num_top_classes=2,
                elem_id="output"
            )
            
            with gr.Accordion("How It Works", open=False):
                gr.Markdown("""
                    ## The SONICS classifier
                    
                    The SONICS classifier analyzes your audio to determine if it's an authentic song (human created) or generated by AI. Our models are trained on a diverse dataset of real and AI-generated songs from Suno and Udio.
                    
                    ### Models available:
                    - **SpecTTTra-α**: Optimized for speed
                    - **SpecTTTra-β**: Balanced performance 
                    - **SpecTTTra-γ**: Highest accuracy
                    
                    ### Duration variants:
                    - **5s**: Analyzes a 5-second clip (faster)
                    - **120s**: Analyzes up to 2 minutes (more accurate)
                """)

    # Add Examples section after output is defined
    with gr.Accordion("Example Audio Files", open=True):
        gr.Examples(
            examples=[
                ["example/real_song.mp3", "SpecTTTra-γ (5s)"],
                ["example/fake_song.mp3", "SpecTTTra-γ (5s)"],
            ],
            inputs=[audio_input, model_dropdown],
            outputs=[output],
            fn=predict,
            cache_examples=True,
        )

    # Footer
    gr.HTML(
        """
        <div class="sonics-footer">
            <p>SONICS: Synthetic Or Not - Identifying Counterfeit Songs | ICLR 2025</p>
            <p style="font-size: 12px;">For research purposes only</p>
        </div>
        """
    )

    # Prediction handling
    submit_btn.click(fn=predict, inputs=[audio_input, model_dropdown], outputs=[output])

if __name__ == "__main__":
    demo.launch()