Update app.py

app.py

@@ -23,29 +23,6 @@ sample_rate = 44100
 amplitude_scaling_factor = 10.0
 
 
-import numpy as np
-from scipy.io.wavfile import write
-from scipy.signal import find_peaks
-from scipy.fft import fft
-from tqdm import tqdm
-import time
-import matplotlib.pyplot as plt
-from scipy.io.wavfile import read
-from scipy import signal
-import gradio as gr
-import reedsolo
-import wavio
-from scipy.signal import butter, lfilter
-
-# ---------------Parameters--------------- #
-
-low_frequency = 18000
-high_frequency = 19000
-bit_duration = 0.007
-sample_rate = 44100
-amplitude_scaling_factor = 10.0
-
-
 # -----------------Record----------------- #
 
 def record(audio):
@@ -81,34 +58,77 @@ def record(audio):
 
 # -----------------Filter----------------- #
 
-def butter_bandpass(lowcut, highcut, sr, order=5):
+def butter_bandpass(sr, order=5):
+    """
+    This function designs a Butterworth bandpass filter.
+
+    Parameters:
+    sr (int): The sample rate of the audio.
+    order (int): The order of the filter.
+
+    Returns:
+    tuple: The filter coefficients `b` and `a`.
+    """
+    # Calculate the Nyquist frequency
     nyquist = 0.5 * sr
-    low = lowcut / nyquist
-    high = highcut / nyquist
-    coef = butter(order, [low, high], btype='band')
-    b = coef[0]
-    a = coef[1]
+
+    # Normalize the cutoff frequencies
+    low = low_frequency / nyquist
+    high = high_frequency / nyquist
+
+    # Design the Butterworth bandpass filter
+    coefficient = butter(order, [low, high], btype='band')
+
+    # Extract the filter coefficients
+    b = coefficient[0]
+    a = coefficient[1]
+
     return b, a
 
 
-def butter_bandpass_filter(data, lowcut, highcut, sr, order=5):
-    b, a = butter_bandpass(lowcut, highcut, sr, order=order)
+def butter_bandpass_filter(data, sr, order=5):
+    """
+    This function applies the Butterworth bandpass filter to a given data.
+
+    Parameters:
+    data (array): The audio data to be filtered.
+    sr (int): The sample rate of the audio.
+    order (int): The order of the filter.
+
+    Returns:
+    array: The filtered audio data.
+    """
+    # Get the filter coefficients
+    b, a = butter_bandpass(sr, order=order)
+
+    # Apply the filter to the data
     y = lfilter(b, a, data)
+
     return y
 
 
-def main():
-    input_file = 'recorded.wav'
-    output_file = 'output_filtered_receiver.wav'
-    lowcut = 17500
-    highcut = 19500
+def filtered():
+    """
+    This function reads an audio file, applies the bandpass filter to the audio data,
+    and then writes the filtered data to an output file.
+
+    Returns:
+    str: A success message if the audio is filtered correctly, otherwise an error message.
+    """
+    try:
+        # Read the audio data from the input file
+        sr, data = read(input_file)
+
+        # Apply the bandpass filter to the audio data
+        filtered_data = butter_bandpass_filter(data, sr)
 
-    sr, data = read(input_file)
+        # Write the filtered data to the output file
+        write(output_file, sr, np.int16(filtered_data))
 
-    filtered_data = butter_bandpass_filter(data, lowcut, highcut, sr)
-    write(output_file, sr, np.int16(filtered_data))
-    return "Filtered Audio Generated"
-    
+        return "Filtered Audio Generated"
+    except Exception as e:
+        # If an error occurs, return an error message
+        return f"Error: {str(e)}"
 
 # -----------------Frame----------------- #