Update app.py

app.py

@@ -9,10 +9,6 @@ transcriber = pipeline(model="openai/whisper-base")
 # Load summarization model
 summarization_model = pipeline("summarization")
 
-# Load question-answering model
-model_name = "deepset/roberta-base-squad2"
-nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
-
 def translate_audio(audio):
     # Step 1: Transcribe audio to text
     transcription = transcriber(audio)
@@ -24,13 +20,6 @@ def translate_audio(audio):
 
     return transcription['text'], summary[0]['summary_text']
 
-def answer_question(context, question):
-    QA_input = {
-        'question': question,
-        'context': context
-    }
-    print('----QA_input----', QA_input)
-    return nlp(QA_input)['answer']
 
 # Create Gradio interface
 with gr.Blocks() as iface:
@@ -53,19 +42,5 @@ with gr.Blocks() as iface:
         outputs=[transcription_output, translation_output]
     )
 
-    def respond(message, chat_history, context):
-      bot_message = answer_question(context, message)
-      print('----bot_message---', bot_message)
-      chat_history.append((message, bot_message))
-      time.sleep(2)
-      return "", chat_history
-
-    with gr.Blocks() as demo:
-      chatbot = gr.Chatbot()
-      msg = gr.Textbox()
-      clear = gr.ClearButton([msg, chatbot])
-
-      msg.submit(respond, [msg, chatbot, transcription_output], [msg, chatbot])
-
 # Launch the app
 iface.launch(share=True)  # 'share=True' to get a public link