Update app.py

app.py

@@ -17,4 +17,68 @@ if file_name is not None:
 
     col2.header("Probabilities")
     for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+
+# 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)