Update app.py

app.py

@@ -10,30 +10,14 @@ co = cohere.Client(os.environ.get('COHERE_API_KEY'))
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-whisper_model = pipeline(
-    task="automatic-speech-recognition",
-    model="openai/whisper-large-v2",
-    chunk_length_s=30,
-    device=device,
-)
+whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2")
 
-all_special_ids = whisper_model.tokenizer.all_special_ids
-transcribe_token_id = all_special_ids[-5]
-translate_token_id = all_special_ids[-6]
 
 def translate_or_transcribe(audio, task):
-    whisper_model.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="Transcribe in Spoken Language" else translate_token_id]]
-    text = whisper_model(audio)["text"]
-    return text
+    return whisper(audio, None, task, fn_index=0)
     
 def get_response_from_chatbot(text):
-    try:
-        resp = api.send_message(text)    
-        response = resp['message']
-      # logger.info(f"response_: {response}")
-    except:
-        response = "Sorry, the chatGPT queue is full. Please try again in some time"
-    return response
+    return text
 
 def chat(message, chat_history):      
     out_chat = []
@@ -153,7 +137,7 @@ with gr.Blocks(title='Talk to catGPT') as demo:
                                     )
             translate_btn = gr.Button("Check Whisper first ? 👍")
             
-        whisper_task = gr.Radio(["Translate to English", "Transcribe in Spoken Language"], value="Translate to English", show_label=False)
+        whisper_task = gr.Radio(["translate", "transcribe"], value="translate", show_label=False)
         with gr.Row(elem_id="prompt_row"):
             prompt_input = gr.Textbox(lines=2, label="Input text",show_label=True)
             chat_history = gr.Textbox(lines=4, label="prompt", visible=False)