app.py

import gradio as gr
import os
from predict import predict_healing_music
import train_model
import logging
import tempfile
import time
import shutil
import socket
import joblib

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def find_free_port(start_port=7860, max_port=7960):
    """Find a free port in the given range."""
    for port in range(start_port, max_port + 1):
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
            try:
                s.bind(('', port))
                return port
            except OSError:
                continue
    return None

# Ensure model directory exists
model_dir = os.path.join(os.path.dirname(__file__), "models")
os.makedirs(model_dir, exist_ok=True)

# Model file paths
model_path = os.path.join(model_dir, "model.joblib")
scaler_path = os.path.join(model_dir, "scaler.joblib")

# Check if model exists
if not os.path.exists(model_path) or not os.path.exists(scaler_path):
    print('First run: Training the model...')
    try:
        train_model.train_and_evaluate_model()
        print('Model training completed!')
    except Exception as e:
        print(f'Model training failed: {str(e)}')
        raise e

def process_audio(audio_path):
    """
    Process and analyze the audio file
    """
    if audio_path is None:
        return None, None, None, "Please upload an audio file"
    
    model_dir = os.path.join(os.path.dirname(__file__), "models")
    model_path = os.path.join(model_dir, "model.joblib")
    scaler_path = os.path.join(model_dir, "scaler.joblib")

    try:
        # Load model and scaler
        model = joblib.load(model_path)
        scaler = joblib.load(scaler_path)
        
        tmp_file = None
        try:
            # Create temporary file
            suffix = os.path.splitext(audio_path)[1]
            tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
            shutil.copy2(audio_path, tmp_file.name)
            
            # Make prediction
            healing_probability = predict_healing_music(tmp_file.name)
            
            if healing_probability is not None:
                # Calculate percentage
                healing_percentage = healing_probability * 100
                
                # Generate description
                if healing_percentage >= 75:
                    description = "This music has strong healing properties! ✨"
                    color = "#15803d"  # Dark green
                elif healing_percentage >= 50:
                    description = "This music has moderate healing effects. 🌟"
                    color = "#0369a1"  # Dark blue
                else:
                    description = "This music has limited healing potential. 🎵"
                    color = "#b91c1c"  # Dark red
                
                return f"{healing_percentage:.1f}%", f'<div style="background-color: {color}; color: white; padding: 1rem; border-radius: 8px; text-align: center;">{description}</div>', None, None
            else:
                return "Error", "Error analyzing file. Please ensure it's a valid MP3 or WAV file.", None, None
                
        except Exception as e:
            logger.error(f"Error during analysis: {str(e)}")
            return "Error", f"An unexpected error occurred: {str(e)}", None, None
        
        finally:
            # Clean up temporary file
            if tmp_file is not None:
                try:
                    tmp_file.close()
                    os.unlink(tmp_file.name)
                except Exception as e:
                    logger.error(f"Failed to clean up temporary file: {str(e)}")

    except Exception as e:
        logger.error(f"Error during model loading: {str(e)}")
        return "Error", f"An unexpected error occurred: {str(e)}", None, None

def analyze_audio(audio):
    """Analyze the audio file"""
    try:
        if audio is None:
            return [
                gr.update(visible=False),  # results
                gr.update(visible=False),  # analyzing
                "",                        # healing_index
                ""                         # result_text
            ]

        # Show analyzing status first
        yield [
            gr.update(visible=False),      # results
            gr.update(visible=True),       # analyzing
            "",                            # healing_index
            ""                            # result_text
        ]

        # Process audio and get results
        index, desc, _, _ = process_audio(audio)
        desc_with_hint = f'{desc}<div style="margin-top: 1rem; color: #9ca3af; font-size: 0.9rem;">To analyze another file, please refresh the page</div>'

        # Return final results
        yield [
            gr.update(visible=True),       # results
            gr.update(visible=False),      # analyzing
            index,                         # healing_index
            desc_with_hint                # result_text
        ]

    except Exception as e:
        logger.error(f"Error in analyze_audio: {str(e)}")
        yield [
            gr.update(visible=True),       # results
            gr.update(visible=False),      # analyzing
            "Error",                       # healing_index
            f"An error occurred: {str(e)}" # result_text
        ]

# Custom CSS styles
custom_css = """
.gradio-container {
    font-family: 'Inter', -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
    max-width: 800px !important;
    margin: auto;
    padding: 0 1rem;
    background-color: #0f1117;
}
.container {
    max-width: 700px;
    margin: 0 auto;
    padding-top: 2rem;
}
.header {
    text-align: center;
    margin-bottom: 1.5rem;
    width: 100%;
    display: flex;
    justify-content: center;
    align-items: center;
}
.title {
    font-size: 2.8rem !important;
    font-weight: 800 !important;
    color: #ffffff !important;
    margin: 0 !important;
    line-height: 1.2 !important;
    text-align: center !important;
    letter-spacing: 0.05em !important;
}
.subtitle {
    font-size: 1.4rem !important;
    text-align: center;
    color: #ffffff !important;
    margin-top: 1rem !important;
    max-width: 800px;
    margin-left: auto;
    margin-right: auto;
    white-space: nowrap !important;
    font-weight: 500 !important;
    letter-spacing: 0.02em !important;
}
.upload-box {
    background-color: #1f2937;
    border-radius: 12px;
    padding: 2rem;
    margin-bottom: 1rem;
    border: 2px dashed #6b7280;
    transition: all 0.3s ease;
    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
}
.upload-area {
    display: flex;
    flex-direction: column;
    align-items: center;
    justify-content: center;
    gap: 1rem;
    padding: 1.5rem 0;
}
.icon-text-container {
    color: #ffffff;
    font-size: 1.3rem !important;
    font-weight: 600 !important;
    letter-spacing: 0.02em !important;
}
.upload-hint {
    color: #ffffff !important;
    font-size: 1rem !important;
    margin-top: 0.5rem !important;
    font-style: italic !important;
    font-weight: 500 !important;
}
.analyzing-status {
    margin: 1rem 0;
    background-color: #1f2937;
    border-radius: 12px;
    padding: 1.5rem;
    text-align: center;
    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
}
.analyzing-text {
    color: #ffffff !important;
    font-size: 1.4rem !important;
    font-weight: 600 !important;
    margin: 0 !important;
    letter-spacing: 0.02em !important;
}
.results-container {
    background-color: #1f2937;
    border-radius: 12px;
    padding: 1.5rem;
    margin-top: 1rem;
    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
}
.result-title {
    color: #ffffff !important;
    font-size: 1.6rem !important;
    font-weight: 700 !important;
    margin-bottom: 1rem !important;
    letter-spacing: 0.02em !important;
}
.healing-index {
    font-size: 3rem !important;
    font-weight: 800 !important;
    text-align: center;
    color: #ffffff !important;
    margin: 1rem 0 !important;
    letter-spacing: 0.05em !important;
}
.result-text {
    color: #ffffff !important;
    font-size: 1.2rem !important;
    font-weight: 600 !important;
    letter-spacing: 0.02em !important;
    line-height: 1.5 !important;
    padding: 1rem !important;
    border-radius: 8px !important;
}
"""

# Create Gradio interface
with gr.Blocks(
    title="Healing Music Classifier",
    css=custom_css,
    theme=gr.themes.Default()
) as demo:
    with gr.Column(elem_classes="container"):
        with gr.Row(elem_classes="header"):
            gr.Markdown("&#127925; Healing Music Classifier", elem_classes="title")
        
        gr.Markdown(
            "Upload your music file, and our model will analyze its healing potential!",
            elem_classes="subtitle"
        )
        
        with gr.Column(elem_classes="upload-box"):
            with gr.Column(elem_classes="upload-area"):
                gr.Markdown("&#9729;&#65039; Drop your audio file here", elem_classes="icon-text-container")
                audio_input = gr.Audio(
                    label="Audio Input",
                    sources=["upload"],
                    type="filepath",
                    elem_classes="audio-input",
                    interactive=True,
                    label_visible=False
                )
            gr.Markdown("Limit 200MB per file • MP3, WAV", elem_classes="upload-hint")
        
        with gr.Column(elem_classes="analyzing-status", visible=False) as analyzing:
            gr.Markdown(
                """<div style="display: flex; align-items: center; justify-content: center; gap: 0.5rem;">
                   <div class="loading-spinner"></div>
                   <span style="color: #60a5fa;">Analyzing your music...</span>
                   </div>""",
                elem_classes="analyzing-text"
            )
        
        with gr.Column(elem_classes="results-container", visible=False) as results:
            gr.Markdown("Analysis Results", elem_classes="result-title")
            healing_index = gr.Markdown("", elem_classes="healing-index")
            result_text = gr.Markdown("", elem_classes="result-text")

    # Audio analysis event
    audio_input.upload(
        fn=analyze_audio,
        inputs=[audio_input],
        outputs=[
            results,
            analyzing,
            healing_index,
            result_text
        ],
        queue=True
    )

    # Enable queue for the entire app
    demo.queue()

# Launch application
if __name__ == "__main__":
    demo.launch()