updated server

server.py

@@ -78,21 +78,26 @@ async def predictor(names, file_uploads, usersNum, recordingsNum):
     wav_fpaths = []
     file_upload = file_uploads[-1]
     data = await file_upload.read()
+    print("data", data)
     filename = "test¬"+file_upload.filename
     file_path = UPLOAD_DIR / filename
+    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")
     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")}
-        test_pos_emb = np.array([encoder.embed_speaker(wavs)
-                                for wavs in test_pos_wavs.values()])
+        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