Update app.py

app.py

@@ -14,67 +14,6 @@ import io
 import librosa
 # import numpy
 
-# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-
-# st.title("Hot Dog? Or Not?")
-
-# file_name = st.file_uploader("Upload a hot dog candidate image")
-
-# if file_name is not None:
-#     col1, col2 = st.columns(2)
-
-#     image = Image.open(file_name)
-#     col1.image(image, use_column_width=True)
-#     predictions = pipeline(image)
-
-#     col2.header("Probabilities")
-#     for p in predictions:
-#         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
-
-# def read_as_single_channel_16k(audio_file, def_sr=16000, verbose=True, aim_second=None):
-#     assert os.path.exists(audio_file)
-#     st.markdown(os.path.exists(audio_file))
-    
-#     file_extension = os.path.splitext(audio_file)[1].lower()
-#     st.markdown(file_extension)
-
-#     if file_extension == ".mp3":
-#         data, origin_sr = librosa.load(audio_file, sr=None)
-#     elif file_extension in [".wav", ".flac"]:
-#         data, origin_sr = soundfile.read(audio_file)
-#     else:
-#         raise Exception("unsupported file:" + file_extension)
-
-#     # channel check
-#     if len(data.shape) == 2:
-#         left_channel = data[:, 0]
-#         if verbose:
-#             print("Warning! the input audio has multiple chanel, this tool only use the first channel!")
-#         data = left_channel
-
-#     # sample rate check
-#     if origin_sr != def_sr:
-#         data = resampy.resample(data, origin_sr, def_sr)
-#         if verbose:
-#             print("Warning! The original samplerate is not 16Khz; the watermarked audio will be re-sampled to 16KHz")
-
-#     sr = def_sr
-#     audio_length_second = 1.0 * len(data) / sr
-#     # if verbose:
-#     #     print("input length :%d second" % audio_length_second)
-
-#     if aim_second is not None:
-#         signal = data
-#         assert len(signal) > 0
-#         current_second = len(signal) / sr
-#         if current_second < aim_second:
-#             repeat_count = int(aim_second / current_second) + 1
-#             signal = np.repeat(signal, repeat_count)
-#         data = signal[0:sr * aim_second]
-
-#     return data, sr, audio_length_second
-
-    
 # def my_read_file(audio_path, max_second):
 #     signal, sr, audio_length_second = read_as_single_channel_16k(audio_path, default_sr)
 #     if audio_length_second > max_second: