Update app.py

app.py

@@ -33,87 +33,93 @@ def label_data(ranges):
 
 def preprocess_data():
     global global_data
-    if 'Unnamed: 0' in global_data.columns:
-        global_data.drop(columns='Unnamed: 0', axis=1, inplace=True)
-    global_data.columns = ['raw_eeg', 'label']
-    raw_data = global_data['raw_eeg']
-    labels_old = global_data['label']
-    
-    sampling_rate = 512
-    notch_freq = 50.0
-    lowcut, highcut = 0.5, 30.0
-    
-    nyquist = (0.5 * sampling_rate)
-    notch_freq_normalized = notch_freq / nyquist
-    b_notch, a_notch = signal.iirnotch(notch_freq_normalized, Q=0.05, fs=sampling_rate)
-    
-    lowcut_normalized = lowcut / nyquist
-    highcut_normalized = highcut / nyquist
-    b_bandpass, a_bandpass = signal.butter(4, [lowcut_normalized, highcut_normalized], btype='band')
-    
-    features = []
-    labels = []
-
-    def calculate_psd_features(segment, sampling_rate):
-        f, psd_values = scipy.signal.welch(segment, fs=sampling_rate, nperseg=len(segment))
-        alpha_indices = np.where((f >= 8) & (f <= 13))
-        beta_indices = np.where((f >= 14) & (f <= 30))
-        theta_indices = np.where((f >= 4) & (f <= 7))
-        delta_indices = np.where((f >= 0.5) & (f <= 3))
-        energy_alpha = np.sum(psd_values[alpha_indices])
-        energy_beta = np.sum(psd_values[beta_indices])
-        energy_theta = np.sum(psd_values[theta_indices])
-        energy_delta = np.sum(psd_values[delta_indices])
-        alpha_beta_ratio = energy_alpha / energy_beta
-        return {
-            'E_alpha': energy_alpha,
-            'E_beta': energy_beta,
-            'E_theta': energy_theta,
-            'E_delta': energy_delta,
-            'alpha_beta_ratio': alpha_beta_ratio
-        }
-
-    def calculate_additional_features(segment, sampling_rate):
-        f, psd = scipy.signal.welch(segment, fs=sampling_rate, nperseg=len(segment))
-        peak_frequency = f[np.argmax(psd)]
-        spectral_centroid = np.sum(f * psd) / np.sum(psd)
-        log_f = np.log(f[1:])
-        log_psd = np.log(psd[1:])
-        spectral_slope = np.polyfit(log_f, log_psd, 1)[0]
-        return {
-            'peak_frequency': peak_frequency,
-            'spectral_centroid': spectral_centroid,
-            'spectral_slope': spectral_slope
-        }
-
-    for i in range(0, len(raw_data) - 512, 256):
-        segment = raw_data.loc[i:i+512]
-        segment = pd.to_numeric(segment, errors='coerce')
-        segment = signal.filtfilt(b_notch, a_notch, segment)
-        segment = signal.filtfilt(b_bandpass, a_bandpass, segment)
-        segment_features = calculate_psd_features(segment, 512)
-        additional_features = calculate_additional_features(segment, 512)
-        segment_features = {**segment_features, **additional_features}
-        features.append(segment_features)
-        labels.append(labels_old[i])
-    
-    columns = ['E_alpha', 'E_beta', 'E_theta', 'E_delta', 'alpha_beta_ratio', 'peak_frequency', 'spectral_centroid', 'spectral_slope']
-    df_features = pd.DataFrame(features, columns=columns)
-    df_features['label'] = labels
-    
-    scaler = StandardScaler()
-    X_scaled = scaler.fit_transform(df_features.drop('label', axis=1))
-    df_scaled = pd.DataFrame(X_scaled, columns=columns)
-    df_scaled['label'] = df_features['label']
-    
-    processed_data_filename = 'processed_data.csv'
-    df_scaled.to_csv(processed_data_filename, index=False)
-    
-    scaler_filename = 'scaler.pkl'
-    with open(scaler_filename, 'wb') as file:
-        pickle.dump(scaler, file)
+    try:
+        if 'Unnamed: 0' in global_data.columns:
+            global_data.drop(columns='Unnamed: 0', axis=1, inplace=True)
+        global_data.columns = ['raw_eeg', 'label']
+        raw_data = global_data['raw_eeg']
+        labels_old = global_data['label']
+
+        sampling_rate = 512
+        notch_freq = 50.0
+        lowcut, highcut = 0.5, 30.0
+
+        nyquist = (0.5 * sampling_rate)
+        notch_freq_normalized = notch_freq / nyquist
+        b_notch, a_notch = signal.iirnotch(notch_freq_normalized, Q=0.05, fs=sampling_rate)
+
+        lowcut_normalized = lowcut / nyquist
+        highcut_normalized = highcut / nyquist
+        b_bandpass, a_bandpass = signal.butter(4, [lowcut_normalized, highcut_normalized], btype='band')
+
+        features = []
+        labels = []
+
+        def calculate_psd_features(segment, sampling_rate):
+            f, psd_values = scipy.signal.welch(segment, fs=sampling_rate, nperseg=len(segment))
+            alpha_indices = np.where((f >= 8) & (f <= 13))
+            beta_indices = np.where((f >= 14) & (f <= 30))
+            theta_indices = np.where((f >= 4) & (f <= 7))
+            delta_indices = np.where((f >= 0.5) & (f <= 3))
+            energy_alpha = np.sum(psd_values[alpha_indices])
+            energy_beta = np.sum(psd_values[beta_indices])
+            energy_theta = np.sum(psd_values[theta_indices])
+            energy_delta = np.sum(psd_values[delta_indices])
+            alpha_beta_ratio = energy_alpha / energy_beta
+            return {
+                'E_alpha': energy_alpha,
+                'E_beta': energy_beta,
+                'E_theta': energy_theta,
+                'E_delta': energy_delta,
+                'alpha_beta_ratio': alpha_beta_ratio
+            }
+
+        def calculate_additional_features(segment, sampling_rate):
+            f, psd = scipy.signal.welch(segment, fs=sampling_rate, nperseg=len(segment))
+            peak_frequency = f[np.argmax(psd)]
+            spectral_centroid = np.sum(f * psd) / np.sum(psd)
+            log_f = np.log(f[1:])
+            log_psd = np.log(psd[1:])
+            spectral_slope = np.polyfit(log_f, log_psd, 1)[0]
+            return {
+                'peak_frequency': peak_frequency,
+                'spectral_centroid': spectral_centroid,
+                'spectral_slope': spectral_slope
+            }
+
+        for i in range(0, len(raw_data) - 512, 256):
+            print(f"Processing segment {i} to {i + 512}")
+            segment = raw_data.loc[i:i+512]
+            segment = pd.to_numeric(segment, errors='coerce')
+            segment = signal.filtfilt(b_notch, a_notch, segment)
+            segment = signal.filtfilt(b_bandpass, a_bandpass, segment)
+            segment_features = calculate_psd_features(segment, 512)
+            additional_features = calculate_additional_features(segment, 512)
+            segment_features = {**segment_features, **additional_features}
+            features.append(segment_features)
+            labels.append(labels_old[i])
+
+        columns = ['E_alpha', 'E_beta', 'E_theta', 'E_delta', 'alpha_beta_ratio', 'peak_frequency', 'spectral_centroid', 'spectral_slope']
+        df_features = pd.DataFrame(features, columns=columns)
+        df_features['label'] = labels
+
+        scaler = StandardScaler()
+        X_scaled = scaler.fit_transform(df_features.drop('label', axis=1))
+        df_scaled = pd.DataFrame(X_scaled, columns=columns)
+        df_scaled['label'] = df_features['label']
+
+        processed_data_filename = 'processed_data.csv'
+        df_scaled.to_csv(processed_data_filename, index=False)
+
+        scaler_filename = 'scaler.pkl'
+        with open(scaler_filename, 'wb') as file:
+            pickle.dump(scaler, file)
+
+        return "Data preprocessing complete! Download the processed data and scaler below.", processed_data_filename, scaler_filename
     
-    return "Data preprocessing complete! Download the processed data and scaler below.", processed_data_filename, scaler_filename
+    except Exception as e:
+        print(f"An error occurred during preprocessing: {e}")
+        return f"An error occurred during preprocessing: {e}", None, None
 
 with gr.Blocks() as demo:
     file_input = gr.File(label="Upload CSV File")