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mistral-chatbot / app.py

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	'''
	Taken directly from : https://huggingface.co/spaces/Sagar23p/mistralAI_chatBoat/tree/main
	'''
	import streamlit as st
	from huggingface_hub import InferenceClient
	import os
	import sys

	st.title("ChatGPT-like Chatbot")

	base_url="https://api-inference.huggingface.co/models/"

	API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
	# print(API_KEY)
	# headers = {"Authorization":"Bearer "+API_KEY}

	model_links ={
	"Mistral-7B":base_url+"mistralai/Mistral-7B-Instruct-v0.2",
	"Mistral-22B":base_url+"mistral-community/Mixtral-8x22B-v0.1",
	# "Gemma-2B":base_url+"google/gemma-2b-it",
	# "Zephyr-7B-β":base_url+"HuggingFaceH4/zephyr-7b-beta",
	# "Llama-2":"meta-llama/Llama-2-7b-chat-hf"
	}

	#Pull info about the model to display
	model_info ={
	"Mistral-7B":
	{'description':"""The Mistral model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \
	\nIt was created by the [Mistral AI](https://mistral.ai/news/announcing-mistral-7b/) team as has over 7 billion parameters. \n""",
	'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},
	"Mistral-22B":
	{'description':"""The Mistral model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \
	\nIt was created by the [Mistral AI](https://mistral.ai/news/announcing-mistral-22b/) team as has over 22 billion parameters. \n""",
	'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'}

	# "Gemma-7B":
	# {'description':"""The Gemma model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \
	# \nIt was created by the [Google's AI Team](https://blog.google/technology/developers/gemma-open-models/) team as has over 7 billion parameters. \n""",
	# 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'},
	# "Gemma-2B":
	# {'description':"""The Gemma model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \
	# \nIt was created by the [Google's AI Team](https://blog.google/technology/developers/gemma-open-models/) team as has over 2 billion parameters. \n""",
	# 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'},
	# "Zephyr-7B":
	# {'description':"""The Zephyr model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \
	# \nFrom Huggingface: \n\
	# Zephyr is a series of language models that are trained to act as helpful assistants. \
	# [Zephyr 7B Gemma](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\
	# is the third model in the series, and is a fine-tuned version of google/gemma-7b \
	# that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
	# 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png'},
	# "Zephyr-7B-β":
	# {'description':"""The Zephyr model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \
	# \nFrom Huggingface: \n\
	# Zephyr is a series of language models that are trained to act as helpful assistants. \
	# [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\
	# is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 \
	# that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
	# 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png'},

	}

	def format_promt(message, custom_instructions=None):
	prompt = ""
	if custom_instructions:
	prompt += f"[INST] {custom_instructions} [/INST]"
	prompt += f"[INST] {message} [/INST]"
	return prompt

	def reset_conversation():
	'''
	Resets Conversation
	'''
	st.session_state.conversation = []
	st.session_state.messages = []
	return None

	models =[key for key in model_links.keys()]

	# Create the sidebar with the dropdown for model selection
	selected_model = st.sidebar.selectbox("Select Model", models)

	#Create a temperature slider
	temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))

	#Add reset button to clear conversation
	st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button

	# Create model description
	st.sidebar.write(f"You're now chatting with {selected_model}")
	st.sidebar.markdown(model_info[selected_model]['description'])
	st.sidebar.image(model_info[selected_model]['logo'])
	st.sidebar.markdown("Generated content may be inaccurate or false.")
	st.sidebar.markdown("\nLearn how to build this chatbot by original author of this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).")

	if "prev_option" not in st.session_state:
	st.session_state.prev_option = selected_model

	if st.session_state.prev_option != selected_model:
	st.session_state.messages = []
	# st.write(f"Changed to {selected_model}")
	st.session_state.prev_option = selected_model
	reset_conversation()

	#Pull in the model we want to use
	repo_id = model_links[selected_model]

	st.subheader(f'AI - {selected_model}')
	# st.title(f'ChatBot Using {selected_model}')

	# Initialize chat history
	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"]):
	st.markdown(message["content"])


	# Accept user input
	if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):

	custom_instruction = "Act like a Human in conversation"

	# Display user message in chat message container
	with st.chat_message("user"):
	st.markdown(prompt)
	# Add user message to chat history
	st.session_state.messages.append({"role": "user", "content": prompt})

	formated_text = format_promt(prompt, custom_instruction)

	# Display assistant response in chat message container
	with st.chat_message("assistant"):
	client = InferenceClient(
	model=model_links[selected_model],)
	# headers=headers)

	output = client.text_generation(
	formated_text,
	temperature=temp_values,#0.5
	max_new_tokens=3000,
	stream=True
	)

	response = st.write_stream(output)
	st.session_state.messages.append({"role": "assistant", "content": response})