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Update app.py
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app.py
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@@ -563,7 +563,7 @@ def read_file_content(file,max_length):
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# 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS
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@st.cache_resource
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def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
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model = model_choice
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conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
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conversation.append({'role': 'user', 'content': prompt})
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filename = save_and_play_audio(audio_recorder)
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if filename is not None:
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transcription = transcribe_audio(filename)
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try:
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# Whisper to GPT: New!! ---------------------------------------------------------------------
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st.write('Reasoning with your inputs with GPT..')
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response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
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st.write('Response:')
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st.write(response)
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filename = generate_filename(user_prompt, choice)
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create_file(filename, user_prompt, response, should_save)
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# Whisper to GPT: New!! ---------------------------------------------------------------------
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import streamlit as st
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# 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS
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@st.cache_resource
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def chat_with_model(prompt, document_section='', model_choice='gpt-3.5-turbo'):
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model = model_choice
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conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
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conversation.append({'role': 'user', 'content': prompt})
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filename = save_and_play_audio(audio_recorder)
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if filename is not None:
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transcription = transcribe_audio(filename)
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#try:
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transcript = transcription['text']
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st.write(transcript)
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# Whisper to GPT: New!! ---------------------------------------------------------------------
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st.write('Reasoning with your inputs with GPT..')
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response = chat_with_model(transcript)
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st.write('Response:')
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st.write(response)
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filename = generate_filename(transcript, "txt")
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create_file(filename, transcript, response, should_save)
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# Whisper to GPT: New!! ---------------------------------------------------------------------
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# Whisper to Llama:
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response = StreamLLMChatResponse(transcript)
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filename_txt = generate_filename(transcript, ".txt")
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create_file(filename_txt, transcript, response, should_save)
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filename_wav = filename_txt.replace('.txt', '.wav')
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import shutil
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shutil.copyfile(filename, filename_wav)
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if os.path.exists(filename):
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os.remove(filename)
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#except:
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# st.write('Starting Whisper Model on GPU. Please retry in 30 seconds.')
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import streamlit as st
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