# Uncomment to install required packages
# !pip install crewai crewai-tools python-dotenv -q

# Load environment variables
import os
from dotenv import load_dotenv

# Load API keys from .env file
load_dotenv()

# Required environment variables:
# OPENAI_API_KEY=your_openai_key_here
# SERPER_API_KEY=your_serper_key_here (for web search)

print("Environment variables loaded successfully!")
print(f"OpenAI API Key: {'✓ Set' if os.getenv('OPENAI_API_KEY') else '✗ Missing'}")
print(f"Serper API Key: {'✓ Set' if os.getenv('SERPER_API_KEY') else '✗ Missing'}")

from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool, FileWriterTool
from langchain_openai import ChatOpenAI

# Initialize tools
search_tool = SerperDevTool()
file_writer = FileWriterTool()

# Initialize LLM
llm = ChatOpenAI(
    model_name="gpt-4o-mini",
    temperature=0.1
)

print("Tools and LLM initialized successfully!")

# Research Agent - Expert at gathering information
researcher = Agent(
    role='Senior Research Analyst',
    goal='Conduct thorough research on given topics and provide comprehensive insights',
    backstory="""You are an experienced research analyst with expertise in gathering, 
    analyzing, and synthesizing information from multiple sources. You have a keen eye 
    for detail and excel at identifying key trends and insights.""",
    tools=[search_tool],
    llm=llm,
    verbose=False
)

# Writer Agent - Expert at content creation
writer = Agent(
    role='Content Writer',
    goal='Create engaging, informative articles based on research findings',
    backstory="""You are a skilled content writer with years of experience in creating 
    compelling articles. You excel at transforming complex research into accessible, 
    engaging content that resonates with readers.""",
    tools=[file_writer],
    llm=llm,
    verbose=False
)

# Editor Agent - Expert at content review and improvement
editor = Agent(
    role='Senior Editor',
    goal='Review and enhance content for clarity, accuracy, and engagement',
    backstory="""You are a meticulous editor with exceptional attention to detail. 
    You excel at improving content structure, clarity, and overall quality while 
    maintaining the author's voice and intent.""",
    llm=llm,
    verbose=False
)

# print("Agents created successfully!")

def create_tasks(topic):
    # Research Task
    research_task = Task(
        description=f"""Conduct comprehensive research on: {topic}
        
        Your research should include:
        - Current trends and developments
        - Key statistics and data points
        - Expert opinions and insights
        - Future implications and predictions
        
        Provide a detailed research summary with sources with 50 words.""",
        agent=researcher,
        expected_output="A comprehensive research summary with key findings and sources"
    )
    
    # Writing Task
    writing_task = Task(
        description=f"""Based on the research findings, write an engaging article about: {topic} in 50 words
        
        The article should:
        - Have a compelling introduction and conclusion
        - Include relevant statistics and expert insights
        - Be structured with clear headings and subheadings
        - Be written for a general audience
        
        """,
        agent=writer,
        context=[research_task],
        expected_output="A well-structured, engaging article saved as a markdown file"
    )
    
    # Editing Task
    editing_task = Task(
        description="""Review the written article and provide:
        
        1. Overall assessment of content quality
        2. Suggestions for improvement in:
           - Structure and flow
           - Clarity and readability
           - Factual accuracy
           - Engagement and interest
        3. A final edited version with improvements
        
        Provide both your review comments and the improved article within 50 words.""",
        agent=editor,
        context=[writing_task],
        expected_output="Editorial review with improvement suggestions and final edited article"
    )
    
    return [research_task, writing_task, editing_task]

print("Task creation function defined!")

# Define the topic for research and writing
topic = "Artificial Intelligence in Sales 2025"

# Create tasks for the topic
tasks = create_tasks(topic)

# Create the crew
content_crew = Crew(
    agents=[researcher, writer, editor],
    tasks=tasks,
    process=Process.sequential,  # Tasks executed one after another
    verbose=0
)

from IPython.display import display, Markdown

# Execute the crew
print("Starting crew execution...\n")

result = content_crew.kickoff()

print("\n" + "="*50)
print("CREW EXECUTION COMPLETED!")
print("="*50)
print("\nFinal Result:")

# Convert CrewOutput to text and display as markdown
if hasattr(result, 'raw') and result.raw:
    result_text = result.raw
elif hasattr(result, 'tasks_output') and result.tasks_output:
    result_text = "\n\n".join([str(task.output) for task in result.tasks_output])
else:
    result_text = str(result)

display(Markdown(result_text))