update app.py

app.py

@@ -3,7 +3,6 @@ import torch
 import gradio as gr
 import pytube as pt
 from transformers import pipeline
-from huggingface_hub import model_info
 
 MODEL_NAME = "anuragshas/whisper-large-v2-hi"
 lang = "hi"
@@ -61,8 +60,8 @@ demo = gr.Blocks()
 mf_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Audio(source="upload", type="filepath", optional=True),
+        gr.Audio(sources="microphone", type="filepath"),
+        gr.Audio(sources="upload", type="filepath"),
     ],
     outputs="text",
     layout="horizontal",
@@ -73,25 +72,23 @@ mf_transcribe = gr.Interface(
         f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
         " of arbitrary length."
     ),
-    allow_flagging="never",
+    flagging_mode="never",
 )
 
 yt_transcribe = gr.Interface(
     fn=yt_transcribe,
-    inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
+    inputs=[gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
     outputs=["html", "text"],
-    layout="horizontal",
-    theme="huggingface",
     title="Whisper Demo: Transcribe YouTube",
     description=(
         "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
         f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
         " arbitrary length."
     ),
-    allow_flagging="never",
+    flagging_mode="never",
 )
 
 with demo:
     gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
 
-demo.launch(enable_queue=True)
+demo.launch(mcp_server=True, show_api=False)