Update app.py

app.py

@@ -3,12 +3,11 @@ import gradio as gr
 import json
 from transformers import pipeline
 
-# Load the translation pipeline with eager attention implementation
+# Load the translation pipeline
 text_translator = pipeline(
-    "translation", 
-    model="facebook/nllb-200-distilled-600M", 
-    torch_dtype=torch.bfloat16, 
-    attn_implementation="eager"
+    "translation",
+    model="facebook/nllb-200-distilled-600M",
+    torch_dtype=torch.bfloat16
 )
 
 # Load the JSON data for language codes
@@ -46,20 +45,20 @@ def translate_text(text, destination_language):
         return f"Error: Could not find FLORES code for language {destination_language}"
     
     try:
+        # Translation call
         translation = text_translator(text, src_lang="eng_Latn", tgt_lang=dest_code)
         return translation[0]["translation_text"]
     except Exception as e:
         return f"Error during translation: {str(e)}"
 
 # Initialize the speech-to-text pipeline (Whisper model)
-# Using the appropriate Whisper model for automatic speech recognition
 speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-small")
 
 # Function to transcribe audio to text
 def transcribe_audio(audio_file, destination_language):
     try:
         transcription_result = speech_to_text(audio_file)
-        print(f"Transcription result: {transcription_result}")  # Print the whole response to inspect
+        print(f"Transcription result: {transcription_result}")  # Debugging output
         if "text" in transcription_result:
             transcription = transcription_result["text"]
         else: