Update app.py

app.py

@@ -1,32 +1,25 @@
+with open("app.py", "w") as f:
+    f.write("""
 import gradio as gr
 from transformers import pipeline
-import os # Import os module to handle file paths
+import os
 
-# Load the transcription model
-# Using a smaller model that might fit in memory, or consider running on CPU if memory is still an issue
-# For demonstration, let's try 'openai/whisper-small'
 print("🎤 Loading transcription pipeline...")
 try:
-    transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-small", device="cuda:0") # Explicitly use GPU if available
+    transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-small", device="cuda:0")
 except Exception as e:
     print(f"Could not load model on GPU: {e}. Trying on CPU.")
-    transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-small", device="cpu") # Fallback to CPU
+    transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-small", device="cpu")
 
 
 def transcribe_audio(audio_file_path):
-    """
-    Transcribes the audio file using the loaded pipeline.
-    """
     if audio_file_path is None:
         return "Please upload an audio file."
 
-    # Ensure the file path is accessible
     if not os.path.exists(audio_file_path):
         return f"Error: Audio file not found at {audio_file_path}"
 
     print(f" transcribe the audio file: {audio_file_path}")
-    # Run transcription on the audio file with chunking
-    # Adjust chunk_length_s and return_timestamps as needed for your audio
     try:
         transcription_result = transcriber(audio_file_path, chunk_length_s=30, return_timestamps=True)
         return transcription_result["text"]
@@ -34,18 +27,18 @@ def transcribe_audio(audio_file_path):
         return f"Error during transcription: {e}"
 
 
-# Create the Gradio interface
 print("🚀 Creating Gradio interface for Transcription...")
 iface = gr.Interface(
     fn=transcribe_audio,
-    inputs=gr.Audio(type="filepath", label="Upload Audio File"), # Use type="filepath" to get the path to the temporary file
+    inputs=gr.Audio(type="filepath", label="Upload Audio File"),
     outputs=gr.Textbox(label="Transcription"),
     title="Audio Transcription Pipeline",
     description="Upload an audio file (e.g., MP3, WAV) to get a transcription."
 )
 
-# Launch the interface
 print("✨ Launching Gradio interface...")
-# Set share=True to get a public link for sharing (optional)
-iface.launch(debug=True, share=True)
-print("\n✅ Gradio interface launched.")
+iface.launch()
+print("\n✅ Gradio interface launched.")
+""")
+
+print("✅ Saved Gradio app code to app.py")