streamlit_app.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
Streamlit web app for chorus detection in audio files.
"""

import os
import sys
import logging
import base64
import tempfile
import warnings
import io
from typing import Optional, Tuple, List

import matplotlib.pyplot as plt
import streamlit as st
import tensorflow as tf
import librosa
import soundfile as sf
import numpy as np
from pydub import AudioSegment

# Configure logging
logger = logging.getLogger("streamlit-app")

# Suppress TensorFlow and other warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
warnings.filterwarnings("ignore")
tf.get_logger().setLevel('ERROR')

# Import components
try:
    from download_model import ensure_model_exists
    from chorus_detection.audio.data_processing import process_audio
    from chorus_detection.audio.processor import extract_audio
    from chorus_detection.models.crnn import load_CRNN_model, make_predictions
    from chorus_detection.utils.cli import is_youtube_url
    from chorus_detection.utils.logging import logger
    logger.info("Successfully imported chorus_detection modules")
except ImportError as e:
    logger.error(f"Error importing modules: {e}")
    raise

# Define model path
MODEL_PATH = os.path.join(os.getcwd(), "models", "CRNN", "best_model_V3.h5")
if not os.path.exists(MODEL_PATH):
    MODEL_PATH = ensure_model_exists()

# UI theme colors
THEME_COLORS = {
    'background': '#121212',
    'card_bg': '#181818',
    'primary': '#1DB954',
    'secondary': '#1ED760',
    'text': '#FFFFFF',
    'subtext': '#B3B3B3',
    'highlight': '#1DB954',
    'border': '#333333',
}


def get_binary_file_downloader_html(bin_file: str, file_label: str = 'File') -> str:
    """Generate HTML for file download link."""
    with open(bin_file, 'rb') as f:
        data = f.read()
    b64 = base64.b64encode(data).decode()
    return f'<a href="data:application/octet-stream;base64,{b64}" download="{os.path.basename(bin_file)}">{file_label}</a>'


def set_custom_theme() -> None:
    """Apply custom Spotify-inspired theme to Streamlit UI."""
    custom_theme = f"""
    <style>
        .stApp {{
            background-color: {THEME_COLORS['background']};
            color: {THEME_COLORS['text']};
        }}
        .css-18e3th9 {{
            padding-top: 2rem;
            padding-bottom: 10rem;
            padding-left: 5rem;
            padding-right: 5rem;
        }}
        h1, h2, h3, h4, h5, h6 {{
            color: {THEME_COLORS['text']} !important;
            font-weight: 700 !important;
        }}
        .stSidebar .sidebar-content {{
            background-color: {THEME_COLORS['card_bg']};
        }}
        .stButton>button {{
            background-color: {THEME_COLORS['primary']};
            color: white;
            border-radius: 500px;
            padding: 8px 32px;
            font-weight: 600;
            border: none;
            transition: all 0.3s ease;
        }}
        .stButton>button:hover {{
            background-color: {THEME_COLORS['secondary']};
            transform: scale(1.04);
        }}
    </style>
    """
    st.markdown(custom_theme, unsafe_allow_html=True)


def process_youtube(url: str) -> Tuple[Optional[str], Optional[str]]:
    """Process a YouTube URL and extract audio."""
    try:
        with st.spinner('Downloading audio from YouTube...'):
            audio_path, video_name = extract_audio(url)
            return audio_path, video_name
    except Exception as e:
        st.error(f"Error processing YouTube URL: {e}")
        logger.error(f"Error processing YouTube URL: {e}", exc_info=True)
        return None, None


def process_uploaded_file(uploaded_file) -> Tuple[Optional[str], Optional[str]]:
    """Process an uploaded audio file."""
    try:
        with st.spinner('Processing uploaded file...'):
            temp_dir = tempfile.mkdtemp()
            file_name = uploaded_file.name
            temp_path = os.path.join(temp_dir, file_name)
            
            with open(temp_path, 'wb') as f:
                f.write(uploaded_file.getbuffer())
            
            return temp_path, file_name.split('.')[0]
    except Exception as e:
        st.error(f"Error processing uploaded file: {e}")
        logger.error(f"Error processing uploaded file: {e}", exc_info=True)
        return None, None


def extract_chorus_segments(y: np.ndarray, sr: int, smoothed_predictions: np.ndarray, 
                       meter_grid_times: np.ndarray) -> List[Tuple[float, float, np.ndarray]]:
    """Extract chorus segments from predictions."""
    threshold = 0.5
    chorus_mask = smoothed_predictions > threshold
    segments = []
    current_segment = None
    
    for i, is_chorus in enumerate(chorus_mask):
        time = meter_grid_times[i]
        
        if is_chorus and current_segment is None:
            current_segment = (time, None, None)
        elif not is_chorus and current_segment is not None:
            start_time = current_segment[0]
            current_segment = (start_time, time, None)
            segments.append(current_segment)
            current_segment = None
    
    # Handle the case where the last segment extends to the end of the song
    if current_segment is not None:
        start_time = current_segment[0]
        segments.append((start_time, meter_grid_times[-1], None))
    
    # Extract the actual audio for each segment
    segments_with_audio = []
    for start_time, end_time, _ in segments:
        start_idx = int(start_time * sr)
        end_idx = int(end_time * sr)
        segment_audio = y[start_idx:end_idx]
        segments_with_audio.append((start_time, end_time, segment_audio))
    
    return segments_with_audio


def create_chorus_compilation(segments: List[Tuple[float, float, np.ndarray]], 
                         sr: int, fade_duration: float = 0.3) -> Tuple[np.ndarray, str]:
    """Create a compilation of chorus segments."""
    if not segments:
        return np.array([]), "No chorus segments found"
    
    fade_samples = int(fade_duration * sr)
    processed_segments = []
    segment_descriptions = []
    
    for i, (start_time, end_time, audio) in enumerate(segments):
        segment_length = len(audio)
        
        if segment_length <= 2 * fade_samples:
            continue
        
        fade_in = np.linspace(0, 1, fade_samples)
        fade_out = np.linspace(1, 0, fade_samples)
        
        audio_faded = audio.copy()
        audio_faded[:fade_samples] *= fade_in
        audio_faded[-fade_samples:] *= fade_out
        
        processed_segments.append(audio_faded)
        
        start_fmt = format_time(start_time)
        end_fmt = format_time(end_time)
        segment_descriptions.append(f"Chorus {i+1}: {start_fmt} - {end_fmt}")
    
    if not processed_segments:
        return np.array([]), "No chorus segments long enough for compilation"
    
    compilation = np.concatenate(processed_segments)
    description = "\n".join(segment_descriptions)
    
    return compilation, description


def save_audio_for_streamlit(audio_data: np.ndarray, sr: int, file_format: str = 'mp3') -> bytes:
    """Save audio data to a format suitable for Streamlit audio playback."""
    with io.BytesIO() as buffer:
        sf.write(buffer, audio_data, sr, format=file_format)
        buffer.seek(0)
        return buffer.read()


def format_time(seconds: float) -> str:
    """Format seconds as MM:SS."""
    minutes = int(seconds // 60)
    seconds = int(seconds % 60)
    return f"{minutes:02d}:{seconds:02d}"


def main() -> None:
    """Main function for the Streamlit app."""
    # Set page config
    st.set_page_config(
        page_title="Chorus Detection",
        page_icon="🎵",
        layout="wide",
        initial_sidebar_state="collapsed",
    )
    
    # Apply custom theme
    set_custom_theme()
    
    # App title and description
    st.title("Chorus Detection")
    st.markdown("""
    <div class="subheader">
    Upload a song or enter a YouTube URL to automatically detect chorus sections using AI
    </div>
    """, unsafe_allow_html=True)
    
    # User input section - stacked vertically instead of in columns
    st.markdown('<div class="input-option">', unsafe_allow_html=True)
    st.subheader("Option 1: Upload an audio file")
    uploaded_file = st.file_uploader("Choose an audio file", type=['mp3', 'wav', 'ogg', 'flac', 'm4a'])
    st.markdown('</div>', unsafe_allow_html=True)
    
    st.markdown('<div class="input-option">', unsafe_allow_html=True)
    st.subheader("Option 2: YouTube URL")
    st.warning("⚠️ The YouTube download option may not work due to platform restrictions. It's recommended to use the file upload option instead.")
    youtube_url = st.text_input("Enter a YouTube URL", placeholder="https://www.youtube.com/watch?v=...")
    st.markdown('</div>', unsafe_allow_html=True)
    
    # Process button
    if st.button("Analyze"):
        # Check the input method
        audio_path = None
        file_name = None
        
        if uploaded_file is not None:
            audio_path, file_name = process_uploaded_file(uploaded_file)
        elif youtube_url:
            if is_youtube_url(youtube_url):
                audio_path, file_name = process_youtube(youtube_url)
            else:
                st.error("Invalid YouTube URL. Please enter a valid YouTube URL.")
        else:
            st.error("Please upload an audio file or enter a YouTube URL.")
        
        # If we have a valid audio path, process it
        if audio_path and file_name:
            try:
                # Load and process the audio file
                with st.spinner('Processing audio...'):
                    # Load audio and extract features
                    y, sr = librosa.load(audio_path, sr=22050)
                    temp_output_dir = tempfile.mkdtemp()
                    model = load_CRNN_model(MODEL_PATH)
                    
                    # Process audio and make predictions
                    audio_features, _ = process_audio(audio_path, output_path=temp_output_dir)
                    meter_grid_times, predictions = make_predictions(model, audio_features)
                    
                    # Smooth predictions to avoid rapid transitions
                    smoothed_predictions = np.convolve(predictions, np.ones(5)/5, mode='same')
                    
                    # Extract chorus segments and create compilation
                    chorus_segments = extract_chorus_segments(y, sr, smoothed_predictions, meter_grid_times)
                    compilation_audio, segments_desc = create_chorus_compilation(chorus_segments, sr)
                    
                # Display results
                st.markdown(f"""
                <div class="result-container">
                    <div class="song-title">{file_name}</div>
                </div>
                """, unsafe_allow_html=True)
                
                # Display waveform with highlighted chorus sections
                fig, ax = plt.subplots(figsize=(14, 5))
                
                # Plot the waveform
                times = np.linspace(0, len(y)/sr, len(y))
                ax.plot(times, y, color='#b3b3b3', alpha=0.5, linewidth=1)
                ax.set_xlabel('Time (s)')
                ax.set_ylabel('Amplitude')
                ax.set_title('Audio Waveform with Chorus Sections Highlighted')
                
                # Highlight chorus sections
                for start_time, end_time, _ in chorus_segments:
                    ax.axvspan(start_time, end_time, alpha=0.3, color=THEME_COLORS['primary'])
                    ax.annotate('Chorus', 
                               xy=(start_time, 0.8 * max(y)), 
                               xytext=(start_time + 0.5, 0.9 * max(y)),
                               color=THEME_COLORS['primary'],
                               weight='bold')
                
                # Customize plot appearance
                ax.set_facecolor(THEME_COLORS['card_bg'])
                fig.patch.set_facecolor(THEME_COLORS['background'])
                ax.spines['top'].set_visible(False)
                ax.spines['right'].set_visible(False)
                ax.spines['bottom'].set_color(THEME_COLORS['border'])
                ax.spines['left'].set_color(THEME_COLORS['border'])
                ax.tick_params(axis='x', colors=THEME_COLORS['text'])
                ax.tick_params(axis='y', colors=THEME_COLORS['text'])
                ax.xaxis.label.set_color(THEME_COLORS['text'])
                ax.yaxis.label.set_color(THEME_COLORS['text'])
                ax.title.set_color(THEME_COLORS['text'])
                
                st.pyplot(fig)
                
                # Display chorus segments
                if chorus_segments:
                    st.markdown('<div class="chorus-card">', unsafe_allow_html=True)
                    st.subheader("Chorus Segments")
                    for i, (start_time, end_time, segment_audio) in enumerate(chorus_segments):
                        st.markdown(f"""
                        <div class="time-stamp">Chorus {i+1}: {format_time(start_time)} - {format_time(end_time)}</div>
                        """, unsafe_allow_html=True)
                        
                        # Convert segment audio to bytes for playback
                        audio_bytes = save_audio_for_streamlit(segment_audio, sr)
                        st.audio(audio_bytes, format='audio/mp3')
                    st.markdown('</div>', unsafe_allow_html=True)
                    
                    # Chorus compilation
                    if len(compilation_audio) > 0:
                        st.markdown('<div class="chorus-card">', unsafe_allow_html=True)
                        st.subheader("Chorus Compilation")
                        st.markdown("All chorus segments combined into one track:")
                        
                        compilation_bytes = save_audio_for_streamlit(compilation_audio, sr)
                        st.audio(compilation_bytes, format='audio/mp3')
                        st.markdown('</div>', unsafe_allow_html=True)
                else:
                    st.info("No chorus sections detected in this audio.")
                
            except Exception as e:
                st.error(f"Error processing audio: {e}")
                logger.error(f"Error processing audio: {e}", exc_info=True)

if __name__ == "__main__":
    main()