Spaces:

omdena-mhc-team5
/

mhc_brighterdays_mentor

Sleeping

App Files Files Community

mhc_brighterdays_mentor / chat_agent.py

kishoregajjala

final demo code

577f02c 3 months ago

raw history blame contribute delete

No virus

4.82 kB


	import streamlit as st
	from dotenv import load_dotenv, find_dotenv
	import os
	import time
	from langchain.chains import LLMChain
	from langchain_community.llms import HuggingFaceEndpoint
	from langchain.prompts import PromptTemplate
	from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
	from langchain.prompts.chat import (
	ChatPromptTemplate,
	HumanMessagePromptTemplate,
	SystemMessagePromptTemplate,
	)
	from langchain.memory import ChatMessageHistory, ConversationSummaryBufferMemory, ConversationBufferMemory, ConversationSummaryMemory
	from langchain.chains import LLMChain, ConversationChain


	# Please ensure you have a .env file available with 'HUGGINGFACEHUB_API_TOKEN'
	load_dotenv(find_dotenv())
	HUGGINGFACEHUB_API_TOKEN = os.environ["HUGGINGFACEHUB_API_TOKEN"]

	repo_id ="mistralai/Mistral-7B-Instruct-v0.2"
	def choose_model1(model):
	global repo_id
	if model == "mistral-7b-base-model":
	repo_id="mistralai/Mistral-7B-Instruct-v0.2"
	print("model chosen from chat",repo_id)
	else:
	repo_id="GRMenon/mental-health-mistral-7b-instructv0.2-finetuned-V2"
	print("model chosen from chat",repo_id)

	query2 = " "
	def main():
	llm = HuggingFaceEndpoint(
	repo_id=repo_id, max_length=512, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
	)

	# template="""Act as a therapist, and conduct therapy sessions with the user. Your goal analyse their mental health
	# problem, based following input:{query}. Do not show your thought process, only output a single question.
	# Your output should contain consolation related to the query and a single question. Only ask one question at a time."""

	# def ConvoLLM(query: str):
	# prompt_template=PromptTemplate(input_variables=['query'],template= template)
	# prompt_template.format(query= query)
	# chain=LLMChain(llm=llm,prompt=prompt_template)
	# response = chain.run(query)
	# return response

	#---------------------------------------------------------------------------------------------------------------------------------------



	# memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=10)
	# memory.save_context({"input": "hi"}, {"output": "whats up"})

	# def ConvoLLM(query: str):
	# conversation.predict(input=query)

	#---------------------------------------------------------------------------------------------------------------------------------------
	# print(conversation.predict(input="I am feeling low"))
	# print(conversation.predict(input="I am alone at home"))
	# print(conversation.memory.buffer)
	global conversation,memory
	template = """ Act as an expert mental health therapist, and conduct therapy sessions with the user. You are an expert Mental Health therapist who is asking the user questions to learn what professional mental health well-being advice could help the user.
	Your goal is to analyse their mental health problem, based following input:{input}. You will always ask questions to the user to get them to explain more about whatever mental health condition is ailing them.
	DO NOT give the user any mental health advice or medical advice, ONLY ask for more information about their symptoms.
	Do not show your thought process, only output a single question. Your output should contain consolation related to the query and a single question.
	Only ask one question a time.

	Current conversation:
	{history}

	Human: {input}
	AI Assistant:"""


	PROMPT = PromptTemplate(input_variables=["history","input"], template=template)
	memory = ConversationBufferMemory(llm=llm)
	# memory.save_context({"input": "hi"}, {"output": "whats up"})
	# memory.save_context({"input": "not much you"}, {"output": "not much"})
	# memory.save_context({"input": "feeling sad"}, {"output": "I am happy you feel that way"})

	conversation = ConversationChain(
	prompt=PROMPT,
	llm=llm,
	memory=memory,
	# verbose=True
	)


	def convo(query):
	global conversation, memory, query2
	response = conversation.predict(input=query)
	# memory.save_context({"input": query}, {"output": ""})
	query2 = query2 + "," + query
	print("\n query2----------",query2)
	print("\n chat_agent.py----------",memory.chat_memory)
	summary = query2
	return response, summary


	def delete_all_variables():
	global query2
	query2 = " "
	main()

	# main()

	# convo("I am feeling sad")
	# convo("I am feeling Lonely")
	# delete_all_variables()
	# convo("I am feeling hungry")