Update app.py

app.py

@@ -57,7 +57,7 @@ SIDEBAR_INFO = f"""
 
 #              ------------transcribe section------------
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+#device = "cuda" if torch.cuda.is_available() else "cpu"
 
 @spaces.GPU()
 def convert_to_wav(filepath):
@@ -66,7 +66,7 @@ def convert_to_wav(filepath):
     os.system(f'ffmpeg -i "{filepath}" -ar 16000 -ac 1 -c:a pcm_s16le "{audio_file}"')
     return audio_file
 
-pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large", chunk_length_s=30).to(device)
+pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large", chunk_length_s=30)
 
 @spaces.GPU()
 def transcribe_audio(audio_file, batch_size=16):
@@ -268,7 +268,7 @@ with iface:
            
             """)
         
-            summarize_transcribed_button_text_rank = gr.Button("Summary of Transcribed Text, Click Here")
+      @      summarize_transcribed_button_text_rank = gr.Button("Summary of Transcribed Text, Click Here")
             summarize_transcribed_button_text_rank.click(fn=lambda text: text_rank_summary(text), inputs=[text_output], outputs=[summary_output_text_rank])
             summarize_uploaded_button_text_rank = gr.Button("Upload Text to Summarize, Click Here")
             summarize_uploaded_button_text_rank.click(fn=text_rank_summary, inputs=[text_input_text_rank], outputs=[summary_output_text_rank])