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MindfulMedia_Mentor / llm_response_generator.py

jaelin215

Changed default temperature to 0.5 and token length 128 * 4

b28c76d verified 2 months ago

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	#---
	#- Author: Jaelin Lee
	#- Date: Mar 16, 2024
	#- Description: Calls HuggingFace API to generate natural response.
	#- Credit: The initial code is from Abhishek Dutta.
	# Most of the code is kept as he created.
	# I only added a modification to convert it to class.
	# And, I tweaked the prompt to feed into the `streamlit_app.py` file.
	#---

	import os
	from langchain_community.llms import HuggingFaceHub
	from langchain_community.llms import OpenAI
	# from langchain.llms import HuggingFaceHub, OpenAI
	from langchain.chains import LLMChain
	from langchain.prompts import PromptTemplate
	import warnings

	warnings.filterwarnings("ignore")

	class LLLResponseGenerator():

	def __init__(self):
	print("initialized")


	def llm_inference(
	self,
	model_type: str,
	question: str,
	prompt_template: str,
	context: str,
	ai_tone: str,
	questionnaire: str,
	user_text: str,
	openai_model_name: str = "",
	# hf_repo_id: str = "tiiuae/falcon-7b-instruct",
	hf_repo_id: str = "mistralai/Mistral-7B-Instruct-v0.2",
	temperature: float = 0.5,
	max_length: int = 128 * 4,
	) -> str:
	"""Call HuggingFace/OpenAI model for inference

	Given a question, prompt_template, and other parameters, this function calls the relevant
	API to fetch LLM inference results.

	Args:
	model_str: Denotes the LLM vendor's name. Can be either 'huggingface' or 'openai'
	question: The question to be asked to the LLM.
	prompt_template: The prompt template itself.
	context: Instructions for the LLM.
	ai_tone: Can be either empathy, encouragement or suggest medical help.
	questionnaire: Can be either depression, anxiety or adhd.
	user_text: Response given by the user.
	hf_repo_id: The Huggingface model's repo_id
	temperature: (Default: 1.0). Range: Float (0.0-100.0). The temperature of the sampling operation. 1 means regular sampling, 0 means always take the highest score, 100.0 is getting closer to uniform probability.
	max_length: Integer to define the maximum length in tokens of the output summary.

	Returns:
	A Python string which contains the inference result.

	HuggingFace repo_id examples:
	- google/flan-t5-xxl
	- tiiuae/falcon-7b-instruct

	"""
	prompt = PromptTemplate(
	template=prompt_template,
	input_variables=[
	"context",
	"ai_tone",
	"questionnaire",
	"question",
	"user_text",
	],
	)

	if model_type == "openai":
	# https://api.python.langchain.com/en/stable/llms/langchain.llms.openai.OpenAI.html#langchain.llms.openai.OpenAI
	llm = OpenAI(
	model_name=openai_model_name, temperature=temperature, max_tokens=max_length
	)
	llm_chain = LLMChain(prompt=prompt, llm=llm)
	return llm_chain.run(
	context=context,
	ai_tone=ai_tone,
	questionnaire=questionnaire,
	question=question,
	user_text=user_text,
	)

	elif model_type == "huggingface":
	# https://python.langchain.com/docs/integrations/llms/huggingface_hub
	llm = HuggingFaceHub(
	repo_id=hf_repo_id,
	model_kwargs={"temperature": temperature, "max_length": max_length},
	)

	llm_chain = LLMChain(prompt=prompt, llm=llm)
	response = llm_chain.run(
	context=context,
	ai_tone=ai_tone,
	questionnaire=questionnaire,
	question=question,
	user_text=user_text,
	)
	print(response)
	# Extracting only the response part from the output
	response_start_index = response.find("Response;")
	return response[response_start_index + len("Response;"):].strip()

	else:
	print(
	"Please use the correct value of model_type parameter: It can have a value of either openai or huggingface"
	)


	if __name__ == "__main__":
	# Please ensure you have a .env file available with 'HUGGINGFACEHUB_API_TOKEN' and 'OPENAI_API_KEY' values.
	HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')

	context = "You are a mental health supporting non-medical assistant. DO NOT PROVIDE any medical advice with conviction."

	ai_tone = "EMPATHY"
	questionnaire = "ADHD"
	question = (
	"How often do you find yourself having trouble focusing on tasks or activities?"
	)
	user_text = "I feel distracted all the time, and I am never able to finish"

	# The user may have signs of {questionnaire}.
	template = """INSTRUCTIONS: {context}

	Respond to the user with a tone of {ai_tone}.

	Question asked to the user: {question}

	Response by the user: {user_text}

	Provide some advice and ask a relevant question back to the user.

	Response;
	"""

	temperature = 0.5
	max_length = 128 *4

	model = LLLResponseGenerator()


	llm_response = model.llm_inference(
	model_type="huggingface",
	question=question,
	prompt_template=template,
	context=context,
	ai_tone=ai_tone,
	questionnaire=questionnaire,
	user_text=user_text,
	temperature=temperature,
	max_length=max_length,
	)

	print(llm_response)