from langchain_community.tools import DuckDuckGoSearchRun
import os
from crewai import Agent, Task, Crew, Process
from dotenv import load_dotenv
import os

current_directory = os.path.dirname(os.path.realpath(__file__))
config_directory = os.path.expanduser("~/.config/fabric")
env_file = os.path.join(config_directory, ".env")
load_dotenv(env_file)
os.environ['OPENAI_MODEL_NAME'] = 'gpt-4-0125-preview'

# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
# osOPENAI_API_BASE='http://localhost:11434/v1'
# OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model
# OPENAI_API_KEY=''

# Install duckduckgo-search for this example:
# !pip install -U duckduckgo-search

search_tool = DuckDuckGoSearchRun()

# 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]
    # 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
    #
    # 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,
    allow_delegation=True
)

# 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=2,  # You can set it to 1 or 2 to different logging levels
)

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

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