app.py

import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import tempfile
import os
import subprocess

from pydub import AudioSegment
from scipy.fft import rfft, rfftfreq
from scipy.signal import get_window

# Names for the 12 musical notes
NOTE_NAMES = ["C", "C#", "D", "D#", "E", "F",
              "F#", "G", "G#", "A", "A#", "B"]

def freq_to_note_index(freq, base_freq=440.0):
    """
    Maps a frequency value (freq) to an index (0-11) corresponding to a musical note.
    Uses A=440 Hz as the reference.
    
    Returns None if freq <= 0.
    For example:
      - freq_to_note_index(440) -> 9  (which corresponds to "A")
      - freq_to_note_index(261.63) -> 0 (approximately C)
    """
    if freq <= 0:
        return None
    # Calculate the number of semitones relative to A4 = 440Hz
    semitone = round(12 * np.log2(freq / base_freq))
    # Shift so that semitone=0 (A4) maps to index=9 within our 12-note array.
    # This means:
    #   index = (9 + semitone) mod 12
    note_index = (9 + semitone) % 12
    return note_index

def main():
    st.title("Doremi Frequency Decomposition Animation")

    uploaded_file = st.file_uploader("Upload an MP3 file to analyze:", type=["mp3"])
    
    if uploaded_file is not None:
        st.write("File uploaded. Generating video...")

        # --- Step 1: Convert MP3 to WAV & load into a NumPy array ---
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_mp3:
            temp_mp3.write(uploaded_file.read())
            audio = AudioSegment.from_file(temp_mp3.name)
        
        # Convert stereo to mono and normalize to -1..+1
        samples = np.array(audio.get_array_of_samples(), dtype=float)
        sample_rate = audio.frame_rate
        if audio.channels == 2:
            samples = samples.reshape((-1, 2)).mean(axis=1)
        max_val = np.max(np.abs(samples))
        if max_val != 0:
            samples /= max_val

        # --- Parameters for the FFT ---
        chunk_size = 2048       # FFT size
        overlap = 1024          # Overlap
        step_size = chunk_size - overlap
        window = get_window("hann", chunk_size)

        # Calculate how many chunks we can process
        n_chunks = (len(samples) - chunk_size) // step_size + 1
        if n_chunks < 1:
            st.error("Audio is too short to process. Please upload a longer file.")
            return
        
        # Determine how long the resulting animation will be (in seconds)
        total_seconds = n_chunks * (step_size / sample_rate)

        # If the audio is longer than total_seconds, trim it to match
        if len(audio) > int(total_seconds * 1000):
            audio = audio[: int(total_seconds * 1000)]
        
        # Frequency axis for the FFT
        freqs = rfftfreq(chunk_size, d=1.0 / sample_rate)

        # --- Step 2: Compute the energies in the 12 notes for each chunk ---
        note_energies_list = []
        for i in range(n_chunks):
            start = i * step_size
            end = start + chunk_size
            chunk = samples[start:end] * window

            spectrum = np.abs(rfft(chunk))  # Magnitude spectrum (0..Nyquist)
            
            energies = np.zeros(12, dtype=float)
            # For each frequency bin, find which note it belongs to, then accumulate amplitude
            for bin_idx, amp in enumerate(spectrum):
                freq = freqs[bin_idx]
                note_idx = freq_to_note_index(freq, base_freq=440.0)
                if note_idx is not None:
                    energies[note_idx] += amp
            
            note_energies_list.append(energies)

        note_energies_list = np.array(note_energies_list)
        max_energy = np.max(note_energies_list)

        # --- Step 3: Create a bar-chart animation with Matplotlib ---
        fig, ax = plt.subplots(figsize=(6, 4))
        fig.patch.set_facecolor("black")
        ax.set_facecolor("black")
        ax.set_ylim(0, max_energy * 1.1)  # Y-limit to accommodate peaks
        ax.set_xticks(range(12))
        ax.set_xticklabels(NOTE_NAMES, color="white")
        ax.tick_params(axis='y', colors='white')
        for spine in ax.spines.values():
            spine.set_color('white')

        # Use a color map for the bars
        cmap = plt.cm.get_cmap('rainbow', 12)
        bar_colors = [cmap(i) for i in range(12)]
        bars = ax.bar(range(12), note_energies_list[0], color=bar_colors)

        def update(frame):
            energies = note_energies_list[frame]
            for b, e in zip(bars, energies):
                b.set_height(e)
            return bars

        # Calculate FPS so video length matches audio length
        fps = sample_rate / step_size
        ani = FuncAnimation(
            fig,
            update,
            frames=n_chunks,
            interval=1000 / fps,  # interval in milliseconds
            blit=True
        )

        # --- Step 4: Save the Matplotlib animation as MP4 ---
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
            ani.save(temp_video.name, fps=fps, extra_args=["-vcodec", "libx264"])
            video_path = temp_video.name

        # --- Step 5: Export the trimmed audio to WAV ---
        audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name
        audio.export(audio_path, format="wav")

        # --- Step 6: Merge video and audio with FFmpeg ---
        output_path = tempfile.NamedTemporaryFile(delete=False, suffix="_output.mp4").name
        ffmpeg_command = [
            "ffmpeg", "-y",
            "-i", video_path,
            "-i", audio_path,
            "-c:v", "copy",
            "-c:a", "aac",
            output_path
        ]
        subprocess.run(ffmpeg_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

        # --- Step 7: Display the final video in Streamlit ---
        st.write("**Here is your Doremi decomposition video:**")
        st.video(output_path)

        # Cleanup temp files
        os.remove(temp_mp3.name)
        os.remove(video_path)
        os.remove(audio_path)
        os.remove(output_path)

if __name__ == "__main__":
    main()