Update app.py

app.py

@@ -5,6 +5,7 @@ import librosa
 import soundfile as sf
 import os
 import zipfile
+from huggingface_hub import HfApi, HfFolder, Repository
 
 # Function to download audio from YouTube and save it as a WAV file
 def download_youtube_audio(url, audio_name):
@@ -156,19 +157,37 @@ def zip_directory(directory_path,  audio_name):
                 zipf.write(file_path, arcname)
     return zip_file
 
+# Function to upload zip file to Hugging Face Hub
+def upload_to_huggingface(zip_file, repo_id, hf_token):
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj=zip_file,
+        path_in_repo=os.path.basename(zip_file),
+        repo_id=repo_id,
+        token=hf_token
+    )
+    return f"File uploaded to {repo_id}/{os.path.basename(zip_file)}"
+
 # Gradio interface
-def process_audio(url, audio_name):
+def process_audio(url, audio_name, hf_token, repo_id):
     file_path = download_youtube_audio(url, audio_name)
     dataset_path = slice_audio(file_path, audio_name)
     zip_file = zip_directory(dataset_path, audio_name)
-    return zip_file
+    upload_message = upload_to_huggingface(zip_file, repo_id, hf_token)
+    return zip_file, upload_message
 
 with gr.Blocks() as demo:
+    with gr.Tabs():
+        with gr.TabItem("make dataset"):
+            with gr.Row():
     url_input = gr.Textbox(label="YouTube URL")
     audio_name_input = gr.Textbox(label="Audio Name")
+    hf_token_input = gr.Textbox(label="Hugging Face Token", type="password")
+    repo_id_input = gr.Textbox(label="Repository ID")
     result_output = gr.File(label="Download Sliced Audio Zip")
+    upload_output = gr.Textbox(label="Hugging Face Upload Status")
 
     run_button = gr.Button("Download and Slice Audio")
-    run_button.click(fn=process_audio, inputs=[url_input, audio_name_input], outputs=result_output)
+    run_button.click(fn=process_audio, inputs=[url_input, audio_name_input, hf_token_input, repo_id_input], outputs=[result_output, upload_output])
 
 demo.launch()