Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+from pydub import AudioSegment
+from scipy.fftpack import fft
+import streamlit as st
+import tempfile
+import os
+
+# Streamlit App
+def main():
+    st.title("MP3 Fourier Transform Visualizer")
+
+    uploaded_file = st.file_uploader("Upload an MP3 file", type=["mp3"])
+
+    if uploaded_file is not None:
+        # Convert MP3 to WAV for easier processing
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_mp3:
+            temp_mp3.write(uploaded_file.read())
+            audio = AudioSegment.from_file(temp_mp3.name)
+
+        samples = np.array(audio.get_array_of_samples())
+        sample_rate = audio.frame_rate
+
+        # Normalize samples
+        if audio.channels == 2:
+            samples = samples.reshape((-1, 2))
+            samples = samples.mean(axis=1)  # Convert to mono
+
+        # Define FFT parameters
+        chunk_size = 2048  # Number of samples per frame
+        overlap = 1024     # Overlap between frames
+        step_size = chunk_size - overlap
+
+        # Calculate the FFT for each chunk
+        freqs = np.fft.rfftfreq(chunk_size, d=1/sample_rate)
+        n_chunks = (len(samples) - chunk_size) // step_size + 1
+        fft_frames = []
+
+        for i in range(n_chunks):
+            chunk = samples[i * step_size:i * step_size + chunk_size]
+            windowed = chunk * np.hanning(len(chunk))
+            spectrum = np.abs(fft(windowed)[:len(freqs)])
+            fft_frames.append(spectrum)
+
+        fft_frames = np.array(fft_frames)
+
+        # Create animation
+        fig, ax = plt.subplots()
+        line, = ax.plot(freqs, fft_frames[0])
+        ax.set_xlim(0, np.max(freqs))
+        ax.set_ylim(0, np.max(fft_frames))
+        ax.set_xlabel("Frequency (Hz)")
+        ax.set_ylabel("Amplitude")
+        ax.set_title("Frequency Spectrum Over Time")
+
+        def update(frame):
+            line.set_ydata(fft_frames[frame])
+            return line,
+
+        ani = FuncAnimation(fig, update, frames=len(fft_frames), blit=True)
+
+        # Save animation to a temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
+            ani.save(temp_video.name, fps=30, extra_args=['-vcodec', 'libx264'])
+            video_path = temp_video.name
+
+        st.video(video_path)
+
+        # Cleanup temporary files
+        os.remove(temp_mp3.name)
+        os.remove(video_path)
+
+if __name__ == "__main__":
+    main()