Update app.py

app.py

@@ -1,6 +1,5 @@
 import io
-import re 
-import gradio as gr
+import re
 import torch
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
 import requests
@@ -13,6 +12,10 @@ from pydub import AudioSegment
 import librosa
 import numpy as np
 from pyannote.audio import Pipeline
+from pywebio import start_server, config, session
+from pywebio.input import input, input_group
+from pywebio.output import put_text, put_markdown, put_file
+from pywebio.session import run_js
 
 # Initialize the speaker diarization pipeline
 try:
@@ -165,16 +168,19 @@ def download_transcript(transcript):
         temp_file_path = temp_file.name
     return temp_file_path
 
-# Create the Gradio interface
-with gr.Blocks(title="Video Transcription") as demo:
-    gr.Markdown("# Video Transcription")
-    video_url = gr.Textbox(label="Video URL")
-    transcribe_button = gr.Button("Transcribe")
-    transcript_output = gr.Textbox(label="Transcript", lines=20)
-    download_button = gr.Button("Download Transcript")
-    download_link = gr.File(label="Download Transcript")
-    
-    transcribe_button.click(fn=transcribe_video, inputs=video_url, outputs=transcript_output)
-    download_button.click(fn=download_transcript, inputs=transcript_output, outputs=download_link)
-
-demo.launch()
+def pdf_compressor():
+    put_markdown("# Video Transcription")
+    video_url = input(label="Video URL")
+    if video_url:
+        put_text("Transcribing video...")
+        transcript = transcribe_video(video_url)
+        if transcript:
+            put_text(transcript)
+            download_link = download_transcript(transcript)
+            put_file(download_link, label="Download Transcript")
+        else:
+            put_text("Failed to transcribe video.")
+
+if __name__ == '__main__':
+    config(title="Video Transcription", description="Transcribe audio from a video URL using Whisper and PyAnnote")
+    start_server(pdf_compressor, host='0.0.0.0', port=7860, debug=True, enable_rate_limit=True, max_payload_size='200M')