Spaces:

DarForm
/

ProHelper

Sleeping

App Files Files Community

ProHelper / app.py

DarForm

Update app.py

8b9e5cd verified 4 months ago

raw history blame contribute delete

No virus

10.9 kB

	import streamlit as st
	from streamlit_option_menu import option_menu
	from streamlit_lottie import st_lottie
	import json
	from streamlit_chat import message
	from emb import EmbeddingsManager
	from history import HistoryManager
	from llm import LLMManager
	from settings import SettingManager
	import os


	#Defining my parameters
	vector_store="prohelper"

	@st.cache_resource
	def get_EMB():
	emb=EmbeddingsManager(get_settings(),emb="hkunlp/instructor-large")
	emb.set=get_settings()
	return emb

	@st.cache_resource
	def get_History():
	return HistoryManager()

	@st.cache_resource
	def get_llm():
	llm=LLMManager(get_settings())
	llm.set=get_settings()
	return llm

	@st.cache_resource
	def get_settings():
	return SettingManager()

	#Main page config
	st.set_page_config(
	page_title="ProHelper",
	layout="wide",
	initial_sidebar_state="expanded")

	#Data Pull and Functions
	st.markdown("""
	<style>
	.big-font {
	font-size:80px !important;
	}
	</style>
	""", unsafe_allow_html=True)

	#HideStreamlit
	hide_streamlit_style = """
	<style>
	#MainMenu {visibility: hidden;}
	footer {visibility: hidden;}
	</style>
	"""
	st.markdown(hide_streamlit_style, unsafe_allow_html=True)

	@st.cache_data
	def load_lottiefile(filepath: str):
	with open(filepath,"r") as f:
	return json.load(f)

	# Display conversation history using Streamlit messages
	def display_conversation(history_manager):
	if not history_manager.chat_exists(vector_store):
	history_manager.add_message(vector_store,"Assistant","Hi, how can i help you?")
	message("Hi, how can i help you?",False)
	else:
	for sender,mess in history_manager.get_messages(vector_store):
	is_user=not (sender=="Assistant")
	message(mess, is_user=is_user)

	def clear_text():
	st.session_state.my_text = st.session_state.input
	st.session_state.input = ""

	def process_answer(user_input):
	history=get_History().format_chat(vector_store)
	context=get_EMB().get_formatted_context(vector_store,user_input,history)
	prompt=get_llm().get_prompt(user_input,context,history)
	return get_llm().get_text(prompt),context,history

	def create_setting():
	st.title('ProHelper Settings')
	st.divider()

	st.subheader('Main LLM Settings')

	# Create a main container with two columns
	left_column, right_column = st.columns([1, 2])

	# Add Ai assisted search button
	checked = left_column.checkbox('AI assisted search',value=get_settings().ai_assisted_search, help="An additional LLM will pre process the question to get improved search words for RAG")

	#Add max new token settings
	max_new_token = left_column.number_input(label='Max new token', value=get_settings().max_new_token,help="The maximum number of tokens that the LLM can generate")
	get_settings().max_new_token=max_new_token

	#Add topP setting
	top_p = left_column.slider('Top p', min_value=0.0, max_value=1.0, value=get_settings().top_p, step=0.01, help="Used to control the randomness of the generated text.")
	get_settings().top_p=top_p

	#Add temperature setting
	temperature = left_column.slider('Temperature', min_value=0.01, max_value=1.0, value=get_settings().temperature, step=0.01, help="Another parameter used to control the randomness of the generated text.")
	get_settings().temperature=temperature

	#Add repetition penality setting
	repetition_penalty = left_column.slider('Repetition penality', min_value=0.7, max_value=2.0, value=get_settings().repetition_penalty, step=0.01, help="Used to discourage the model from repeating the same phrases or content.")
	get_settings().repetition_penalty=repetition_penalty

	#Add LLM settings
	llm_options = get_settings().listLLMMap
	selected_option = left_column.selectbox('Chat LLM', llm_options, index=get_settings().defaultLLM)
	selected_llm = llm_options.index(selected_option)
	get_settings().defaultLLM=selected_llm
	get_llm().selectLLM(selected_option)

	# Add content to the right column based on the checkbox state
	if checked:
	get_settings().ai_assisted_search=True
	else:
	get_settings().ai_assisted_search=False

	#Add prompt setting
	system_prompt = right_column.text_area('System prompt:', get_settings().system_prompt,height=500,help=r"Text that provides context and instructions to the model before it generates a response. The special fields {context}, {history} and {question} will be replaced with their corresponding values")
	get_settings().system_prompt=system_prompt

	st.divider()
	st.subheader('RAG Settings')

	# Create a main container with two columns
	left_column_RAG, right_column_RAG = st.columns([1, 2])

	#Add return documents settings
	n_doc_return = left_column_RAG.number_input(label='N doc return', value=get_settings().n_doc_return,help="The number of documents to be returned by the search")
	get_settings().n_doc_return=n_doc_return

	#Add default search method
	rag_methods = get_settings().available_search_methods
	print(get_settings().search_method)
	selected_option_RAG = left_column_RAG.selectbox('Search Methods', rag_methods, index=rag_methods.index(get_settings().search_method))
	get_settings().search_method=selected_option_RAG

	#Add prompt setting
	RAG_prompt = right_column_RAG.text_area('RAG prompt:', get_settings().default_ai_search_prompt,height=500,help=r"Text that provides context and instructions to the model used for search terms. The special fields {history} and {question} will be replaced with their corresponding values")
	get_settings().default_ai_search_prompt=RAG_prompt

	#Add max new token settings
	RAG_max_new_token = left_column_RAG.number_input(label='RAG max new token', value=get_settings().RAG_max_new_token,help="The maximum number of tokens that the LLM can generate")
	get_settings().RAG_max_new_token=RAG_max_new_token

	#Add topP setting
	RAG_top_p = left_column_RAG.slider('RAG top p', min_value=0.0, max_value=1.0, value=get_settings().RAG_top_p, step=0.01, help="Used to control the randomness of the generated text.")
	get_settings().RAG_top_p=RAG_top_p

	#Add temperature setting
	RAG_temperature = left_column_RAG.slider('RAG temperature', min_value=0.01, max_value=1.0, value=get_settings().RAG_temperature, step=0.01, help="Another parameter used to control the randomness of the generated text.")
	get_settings().RAG_temperature=RAG_temperature

	#Add repetition penality setting
	RAG_repetition_penalty = left_column_RAG.slider('RAG repetition penality', min_value=0.7, max_value=2.0, value=get_settings().RAG_repetition_penalty, step=0.01, help="Used to discourage the model from repeating the same phrases or content.")
	get_settings().RAG_repetition_penalty=RAG_repetition_penalty




	def create_info():
	#Header
	st.title('Welcome to ProHelper')
	st.subheader('A new tool to help you with process problems')

	st.divider()

	#Use Cases
	with st.container():
	col1,col2=st.columns(2)
	with col1:
	st.header('Knlowledge base:')
	st.markdown(
	"""
	- _Perrys chemical engineers handbook_
	- _Coulson & Richardson's Chemical Engineering Vol.6 Chemical Engineering Design 4th Edition_
	- _Chemical-Process-Equipment-Selection-and-Design-by-Stanley-M.-Walas_
	"""
	)
	with col2:
	#current_dir = os.path.dirname(__file__)
	#lottie_path = os.path.join(current_dir, r"res/lottie/Piping.json")
	lottie2 = load_lottiefile(r"/home/user/app/res/lottie/Piping.json")
	st_lottie(lottie2,key='place',height=300,width=300)
	st.divider()

	def main():

	#SIDE BAR------------------------------------------------------------------------------------------------------------------------------
	with st.sidebar:
	selected = option_menu('ProHelper', ["Info", 'About','Settings','Bots'],
	icons=['info-circle','question','gear'],menu_icon='droplet-fill', default_index=0)

	#INTRO PAGE----------------------------------------------------------------------------------------------------------------------------
	if selected=="Info":
	create_info()

	#SETTING PAGE----------------------------------------------------------------------------------------------------------------------------
	if selected=="Settings":
	create_setting()

	#ABOUT PAGE----------------------------------------------------------------------------------------------------------------------------
	if selected=="About":
	# Header
	st.title('About ProHelper')

	# Content
	st.write("ProHelper is a testing program created by DFO.")
	st.write("This program is designed for testing purposes only.")
	st.write("For more information, please contact DFO.")


	#BOTS PAGE-----------------------------------------------------------------------------------------------------------------------------
	if selected=="Bots":
	if "messages" not in st.session_state.keys():
	st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

	# Display or clear chat messages
	for message in st.session_state.messages:
	with st.chat_message(message["role"]):
	st.write(message["content"])

	# User-provided prompt
	if prompt := st.chat_input("Ask me anything"):
	st.session_state.messages.append({"role": "user", "content": prompt})
	with st.chat_message("user"):
	st.write(prompt)


	if st.session_state.messages[-1]["role"] != "assistant":
	prompt=st.session_state.messages[-1]["content"]
	with st.chat_message("assistant"):
	with st.spinner("Thinking..."):
	response,cont,hist = process_answer(prompt)
	placeholder = st.empty()
	full_response = ''
	for item in response:
	full_response += item
	placeholder.markdown(full_response)
	placeholder.markdown(full_response)
	message = {"role": "assistant", "content": full_response}
	st.session_state.messages.append(message)

	rag_context = "context"
	with st.expander("Sources"):
	st.write(f"Context: {cont}")
	st.write(f"History: {hist}")

	get_History().add_message(vector_store,"User",prompt)
	get_History().add_message(vector_store,"Assistant",full_response)




	if __name__ == "__main__":
	main()