Update app.py

app.py

@@ -2,35 +2,6 @@ import gradio as gr
 from transformers import pipeline
 import torch
 
-# Load the Whisper model pipeline for speech recognition with optimizations
-model_name = "Vira21/Whisper-Base-KhmerV2"
-whisper_pipeline = pipeline(
-    "automatic-speech-recognition", 
-    model=model_name,
-    device=0 if torch.cuda.is_available() else -1  # Use GPU if available, otherwise use CPU
-)
+interface = gr.load("models/Vira21/Whisper-Base-KhmerV2")
 
-def transcribe_audio(audio):
-    try:
-        # Process and transcribe the audio
-        result = whisper_pipeline(audio)["text"]
-        return result
-    except Exception as e:
-        # Handle errors and return an error message
-        return f"An error occurred during transcription: {str(e)}"
-
-# Gradio Interface with optimizations
-interface = gr.Interface(
-    fn=transcribe_audio,
-    inputs=gr.Audio(type="filepath"),  
-    outputs="text",
-    title="Whisper Khmer Speech-to-Text",
-    description="Upload an audio file or record your voice to get the transcription in Khmer.",
-    examples=[["Example Audio/126.wav"]],
-    allow_flagging="never"  # Disables flagging to save resources
-)
-
-# Launch the app with queue enabled for better handling on free CPU
-if __name__ == "__main__":
-    interface.queue()  # Enable asynchronous queuing for better performance
-    interface.launch()
+interface.launch()