Delete app.py

app.py

@@ -1,96 +0,0 @@
-import time
-import streamlit as st
-from transformers import pipeline
-import os
-import torch
-import datetime
-import numpy as np
-import soundfile
-from wavmark.utils import file_reader
-
-# 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 create_default_value():
-    if "def_value" not in st.session_state:
-        def_val_npy = np.random.choice([0, 1], size=32 - len_start_bit)
-        def_val_str = "".join([str(i) for i in def_val_npy])
-        st.session_state.def_value = def_val_str
-
-# Main web app
-def main():
-    create_default_value()
-
-    # st.title("MDS07")
-    # st.write("https://github.com/wavmark/wavmark")
-    markdown_text = """
-    # MDS07
-    [AudioSeal](https://github.com/jcha0155/AudioSealEnhanced) is the next-generation watermarking tool driven by AI. 
-    You can upload an audio file and encode a custom 16-bit watermark or perform decoding from a watermarked audio.
-    
-    This page is for demonstration usage and only process **the first minute** of the audio. 
-    If you have longer files for processing, we recommend using [our python toolkit](https://github.com/jcha0155/AudioSealEnhanced).
-    """
-
-    # 使用st.markdown渲染Markdown文本
-    st.markdown(markdown_text)
-
-    audio_file = st.file_uploader("Upload Audio", type=["wav", "mp3"], accept_multiple_files=False)
-
-    if audio_file:
-        # 保存文件到本地：
-        tmp_input_audio_file = os.path.join("/tmp/", audio_file.name)
-        with open(tmp_input_audio_file, "wb") as f:
-            f.write(audio_file.getbuffer())
-
-        # 展示文件到页面上
-        # st.audio(tmp_input_audio_file, format="audio/wav")
-
-        action = st.selectbox("Select Action", ["Add Watermark", "Decode Watermark"])
-
-#         if action == "Add Watermark":
-#             watermark_text = st.text_input("The watermark (0, 1 list of length-16):", value=st.session_state.def_value)
-#             add_watermark_button = st.button("Add Watermark", key="add_watermark_btn")
-#             if add_watermark_button:  # 点击按钮后执行的
-#                 if audio_file and watermark_text:
-#                     with st.spinner("Adding Watermark..."):
-#                         watermarked_audio, encode_time_cost = add_watermark(tmp_input_audio_file, watermark_text)
-#                         st.write("Watermarked Audio:")
-#                         print("watermarked_audio:", watermarked_audio)
-#                         st.audio(watermarked_audio, format="audio/wav")
-#                         st.write("Time Cost: %d seconds" % encode_time_cost)
-
-#                         # st.button("Add Watermark", disabled=False)
-#         elif action == "Decode Watermark":
-#             if st.button("Decode"):
-#                 with st.spinner("Decoding..."):
-#                     decode_watermark(tmp_input_audio_file)
-
-
-if __name__ == "__main__":
-    # default_sr = 16000
-    # max_second_encode = 60
-    # max_second_decode = 30
-    # len_start_bit = 16
-    # device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    # model = wavmark.load_model().to(device)
-    main()
-
-    # audio_path = "/Users/my/Library/Mobile Documents/com~apple~CloudDocs/CODE/PycharmProjects/4_语音水印/419_huggingface水印/WavMark/example.wav"
-
-    # decoded_watermark, decode_cost = decode_watermark(audio_path)
-    # print(decoded_watermark)