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Chat-with-Llama-2-70b-st-voice / app.py

Illia56

Update app.py

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	import streamlit as st
	from gradio_client import Client
	from st_audiorec import st_audiorec


	# Constants
	TITLE = "Llama2 70B Chatbot"
	DESCRIPTION = """
	This Space demonstrates model [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) by Meta, a Llama 2 model with 70B parameters fine-tuned for chat instructions.
	\| Model \| Llama2 \| Llama2-hf \| Llama2-chat \| Llama2-chat-hf \|
	\|---\|---\|---\|---\|---\|
	\| 70B \| [Link](https://huggingface.co/meta-llama/Llama-2-70b) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) \|

	---
	"""

	# Initialize client


	with st.sidebar:
	system_promptSide = st.text_input("Optional system prompt:")
	temperatureSide = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
	max_new_tokensSide = st.slider("Max new tokens", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
	ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
	RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)

	whisper_client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/")


	def transcribe(wav_path):

	return whisper_client.predict(
	wav_path, # str (filepath or URL to file) in 'inputs' Audio component
	"transcribe", # str in 'Task' Radio component
	api_name="/predict"
	)

	# Prediction function
	def predict(message, system_prompt='', temperature=0.7, max_new_tokens=4096,Topp=0.5,Repetitionpenalty=1.2):
	with st.status("Starting client"):
	client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/")
	st.write("Requesting client")
	with st.status("Requesting LLama-2"):
	st.write("Requesting API")
	response = client.predict(
	message, # str in 'Message' Textbox component
	system_prompt, # str in 'Optional system prompt' Textbox component
	temperature, # int \| float (numeric value between 0.0 and 1.0)
	max_new_tokens, # int \| float (numeric value between 0 and 4096)
	Topp, # int \| float (numeric value between 0.0 and 1)
	Repetitionpenalty, # int \| float (numeric value between 1.0 and 2.0)
	api_name="/chat_1"
	)
	st.write("Done")
	return response

	# Streamlit UI
	st.title(TITLE)
	st.write(DESCRIPTION)





	if "messages" not in st.session_state:
	st.session_state.messages = []

	# Display chat messages from history on app rerun
	for message in st.session_state.messages:
	with st.chat_message(message["role"], avatar=("🧑‍💻" if message["role"] == 'human' else '🦙')):
	st.markdown(message["content"])

	textinput = st.chat_input("Ask LLama-2-70b anything...")
	wav_audio_data = st_audiorec()

	if wav_audio_data != None:
	with st.status("Transcribing audio"):
	# save audio
	with open("audio.wav", "wb") as f:
	f.write(wav_audio_data)
	prompt = transcribe("audio.wav")

	st.write("Transcribed audio")

	st.chat_message("human",avatar = "🧑‍💻").markdown(prompt)
	st.session_state.messages.append({"role": "human", "content": prompt})

	# transcribe audio
	response = predict(message= prompt)

	with st.chat_message("assistant", avatar='🦙'):
	st.markdown(response)
	# Add assistant response to chat history
	st.session_state.messages.append({"role": "assistant", "content": response})

	# React to user input
	if prompt := textinput:
	# Display user message in chat message container
	st.chat_message("human",avatar = "🧑‍💻").markdown(prompt)
	# Add user message to chat history
	st.session_state.messages.append({"role": "human", "content": prompt})

	response = predict(message=prompt)#, temperature= temperatureSide,max_new_tokens=max_new_tokensSide, Topp=ToppSide,Repetitionpenalty=RepetitionpenaltySide)
	# Display assistant response in chat message container
	with st.chat_message("assistant", avatar='🦙'):
	st.markdown(response)
	# Add assistant response to chat history
	st.session_state.messages.append({"role": "assistant", "content": response})