change py-file name

 from flask import Flask, request, send_file
-from extract_feature4 import extract
-from verification4_merge import load_model, load_enroll_embeddings,perform_verification
-from identification4 import perform_identification
-from enroll4_merge import split_enroll_and_test,enroll_per_spk
+from extract_server import extract
+from verification_server import load_model, load_enroll_embeddings, perform_verification
+from identification_server import perform_identification
+from enroll_server import split_enroll_and_test, enroll_per_spk
 import os
 import shutil
 
@@ -11,7 +11,7 @@ log_dir = '../new_model4_merge' # Where the checkpoints are saved
 embedding_dir = '../enroll_embeddings4_merge'  # Where embeddings are saved
 test_dir = '../feat_logfbank_nfilt40/test/'  # Where test features are saved
 
-    # Settings
+# Settings
 use_cuda = True  # Use cuda or not
 embedding_size = 128  # Dimension of speaker embeddings
 cp_num = 50  # Which checkpoint to use?
@@ -35,19 +35,21 @@ def enrollment():
     except Exception as e:
         print(e)
 
+
 def verification(enroll_speaker):
     test_speaker = 'TEST_SPEAKER'
     thres = 0.95
     # Perform the test
-    return  perform_verification(use_cuda, model, embeddings, enroll_speaker, test_path, test_frames, thres)
+    return perform_verification(use_cuda, model, embeddings, enroll_speaker,
+                                test_path, test_frames, thres)
+
 
 def identification():
-    best_spk = perform_identification(use_cuda, model, embeddings, test_path, test_frames, spk_list)
+    best_spk = perform_identification(use_cuda, model, embeddings, test_path,
+                                      test_frames, spk_list)
     return best_spk
 
 
-
-
 @app.route('/enroll', methods=['POST', "GET"])
 def enroll_controller():
     if request.method == 'POST':
@@ -55,10 +57,10 @@ def enroll_controller():
         enroll_speaker = request.form['enroll_speaker']
         print(f.name)
         f.save('./myrequest_enroll.wav')
-        extract('./myrequest_enroll.wav',enroll_speaker)
-        new_path = '../feat_logfbank_nfilt40/test/'+enroll_speaker+'/'
+        extract('./myrequest_enroll.wav', enroll_speaker)
+        new_path = '../feat_logfbank_nfilt40/test/' + enroll_speaker + '/'
         os.mkdir(new_path)
-        shutil.move('./enroll.p',new_path+'enroll.p')
+        shutil.move('./enroll.p', new_path + 'enroll.p')
 
         try:
             enrollment()
@@ -67,12 +69,10 @@ def enroll_controller():
         except:
             return 'failed'
 
-
         #return 'post'
     return 'get'
 
 
-
 @app.route('/verification', methods=['POST', "GET"])
 def verfication_controller():
     if request.method == 'POST':
@@ -87,6 +87,7 @@ def verfication_controller():
         #return 'post'
     return 'get'
 
+
 @app.route('/identification', methods=['POST', "GET"])
 def identification_controller():
     if request.method == 'POST':
@@ -100,14 +101,16 @@ def identification_controller():
         #return 'post'
     return 'get'
 
+
 @app.route('/debugger', methods=['GET'])
 def debugger():
     return anything
 
-@app.route('/robots.txt',methods=['GET'])
+
+@app.route('/robots.txt', methods=['GET'])
 def antirobot():
     return send_file('robots.txt')
 
+
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port="7777", debug=True)
-

-import librosa
-import numpy as np
-from python_speech_features import fbank
-import pickle
-sample_rate=16000
-#filename='./sunghwan/8sec2.wav'
-
-def normalize_frames(m,Scale=True):
-    if Scale:
-        return (m - np.mean(m, axis=0)) / (np.std(m, axis=0) + 2e-12)
-    else:
-        return (m - np.mean(m, axis=0))
-
-def extract(filename,savename='test.p'):
-	audio, sr = librosa.load(filename, sr=sample_rate, mono=True)
-	filter_banks, energies = fbank(audio, samplerate=sample_rate, nfilt=40, winlen=0.025)
-	filter_banks = 20 * np.log10(np.maximum(filter_banks,1e-5))
-	feature = normalize_frames(filter_banks, Scale=False)
-        label = savename.split('.')[0]
-	todump = {'feat': feature, 'label': label}
-	with open(savename,'wb') as f:
-    		pickle.dump(todump,f)
-
-