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CRW / app.py

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Update app.py

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	#####################################
	##
	#####################################

	from langchain_community.llms import HuggingFaceHub

	#from langchain_community.llms import HuggingFaceHub

	llm_zephyr_7b_beta = HuggingFaceHub(
	repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
	task="text-generation",
	model_kwargs={
	"max_new_tokens": 1000,
	"top_k": 2,
	"temperature": 0.1,
	"repetition_penalty": 1.03,
	},
	)

	import os
	from crewai import Agent, Task, Crew, Process
	from crewai_tools.tools import SerperDevTool

	#from crewai_tools import SerperDevTool

	search_tool = SerperDevTool()

	# Define your agents with roles and goals
	researcher = Agent(
	role='Senior Research Analyst',
	goal='Uncover cutting-edge developments in AI and data science',
	backstory="""You work at a leading tech think tank.
	Your expertise lies in identifying emerging trends.
	You have a knack for dissecting complex data and presenting actionable insights.""",
	verbose=True,
	allow_delegation=False,
	tools=[search_tool],
	max_execution_time=120,
	max_iter=50, # Optional
	max_rpm=25, # Optional
	llm=llm_zephyr_7b_beta
	# You can pass an optional llm attribute specifying what mode you wanna use.
	# It can be a local model through Ollama / LM Studio or a remote
	# model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
	#
	# import os
	# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
	#
	# OR
	#
	# from langchain_openai import ChatOpenAI
	# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
	)

	writer = Agent(
	role='Tech Content Strategist',
	goal='Craft compelling content on tech advancements',
	backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
	You transform complex concepts into compelling narratives.""",
	verbose=True,
	max_execution_time=120,
	max_iter=50, # Optional
	max_rpm=25, # Optional
	allow_delegation=False,
	llm=llm_zephyr_7b_beta
	)

	# Create tasks for your agents
	task1 = Task(
	description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
	Identify key trends, breakthrough technologies, and potential industry impacts.""",
	expected_output="Full analysis report in bullet points",
	agent=researcher
	)

	task2 = Task(
	description="""Using the insights provided, develop an engaging blog
	post that highlights the most significant AI advancements.
	Your post should be informative yet accessible, catering to a tech-savvy audience.
	Make it sound cool, avoid complex words so it doesn't sound like AI.""",
	expected_output="Full blog post of at least 4 paragraphs",
	agent=writer
	)

	# Instantiate your crew with a sequential process
	crew = Crew(
	agents=[researcher, writer],
	tasks=[task1, task2],
	verbose=1, # You can set it to 1 or 2 to different logging levels
	)

	# Get your crew to work!
	#result = crew.kickoff()

	#print("######################")
	#print(result)

	##################
	###### other models:
	# "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
	# "bn22/Mistral-7B-Instruct-v0.1-sharded"
	# "HuggingFaceH4/zephyr-7b-beta"

	# function for loading 4-bit quantized model
	def load_model( ):

	model = HuggingFaceHub(
	repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
	model_kwargs={"max_length": 1048, "temperature":0.2, "max_new_tokens":256, "top_p":0.95, "repetition_penalty":1.0},
	)

	return model

	###############
	#####
	#####
	#####
	####
	from fastapi import FastAPI
	from fastapi.middleware.cors import CORSMiddleware

	app = FastAPI()

	# middlewares to allow cross orgin communications
	app.add_middleware(
	CORSMiddleware,
	allow_origins=['*'],
	allow_credentials=True,
	allow_methods=['*'],
	allow_headers=['*'],
	)


	@app.post("/generate/")
	def generate(user_input, history=[]):
	print("######################")

	result = crew.kickoff()
	print("######################")
	print(result)
	return result

	# load the model asynchronously on startup and save it into memory
	@app.on_event("startup")
	async def startup():
	# Get your crew to work!
	# result = crew.kickoff()

	print("######################")
	# print(result)