commented out print stat

server.py

@@ -45,7 +45,7 @@ async def predictor(names, file_uploads, usersNum, recordingsNum):
     speaker_wavs_list = []
     fileInd = 0
     names.pop()  # to remove key named "test"
-    print("file_uploads ", file_uploads, "recordingNums ", recordingsNum)
+    # print("file_uploads ", file_uploads, "recordingNums ", recordingsNum)
     for name in names:
         wav_fpaths = []
         for ind in range(int(recordingsNum)):
@@ -59,46 +59,46 @@ async def predictor(names, file_uploads, usersNum, recordingsNum):
                 file_object.write(data)
             wav_fpaths.append(Path(file_path))
             fileInd += 1
-            print("wav_fpaths len", len(wav_fpaths), "name", name)
+            # print("wav_fpaths len", len(wav_fpaths), "name", name)
         try:
             speaker_wavs = {speaker: list(map(preprocess_wav, wav_fpaths)) for speaker, wav_fpaths in
                             groupby(tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit="wavs"),
                                     lambda wav_fpath: os.path.basename(wav_fpath).split("¬")[0])}  # extracting person's name from file name
             speaker_wavs_list.append(speaker_wavs)
         except Exception as e:
-            print("error ", e)
+            print("An exception occurred:", type(error).__name__)
+            print("Exception details:", error)
 
     # make a list of the pre-processed audios ki arrays
     for sp_wvs in speaker_wavs_list:
         speaker_embed_list.append(
             np.array([encoder.embed_speaker(wavs) for wavs in sp_wvs.values()]))
 
-    print("preprocessed audio ki array ", speaker_embed_list)
+    # print("preprocessed audio ki array ", speaker_embed_list)
     # making preprocessed test audio
     wav_fpaths = []
     file_upload = file_uploads[-1]
     data = await file_upload.read()
-    print("data", data)
+    # print("data", data)
     filename = "test¬"+file_upload.filename
     file_path = UPLOAD_DIR / filename
-    print("filepath", file_path)
+    # print("filepath", file_path)
     with open(file_path, "wb") as file_object:
         file_object.write(data)
     
     wav_fpaths.append(Path(file_path))
-    print("wav_fpath", wav_fpaths)
-    print("about to test\n")
+    # print("wav_fpath", wav_fpaths)
+    print("about to test")
     try:
         test_pos_wavs = {speaker: list(map(preprocess_wav, wav_fpaths)) for speaker, wav_fpaths in
                         groupby(tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit="wavs"),
                                 lambda wav_fpath: "test")}
-        print("test_pos_wavs", test_pos_wavs)
+        # print("test_pos_wavs", test_pos_wavs)
     except Exception as error:
         print("An exception occurred:", type(error).__name__)
         print("Exception details:", error)
     test_pos_emb = np.array([encoder.embed_speaker(wavs)
                             for wavs in test_pos_wavs.values()])
-        # print("test error ", error)
 
     # calculates cosine similarity between the ground truth (test file) and registered audios
     speakers = {}