"""
Task definitions for NBA data analysis workflow.
"""
from crewai import Task
from config import NBA_DATA_PATH


def create_data_engineering_task(engineer_agent, csv_path: str = None) -> Task:
    """
    Create the data engineering task for processing and cleaning data.
    
    Args:
        engineer_agent: The Engineer Agent to assign this task to
        csv_path: Path to CSV file (defaults to NBA_DATA_PATH from config)
        
    Returns:
        Task: Configured data engineering task
    """
    data_path = csv_path or NBA_DATA_PATH
    
    return Task(
        description=f"""
        Quickly examine the dataset located at {data_path}. 
        
        Your tasks (BE EFFICIENT - use tools only once):
        1. Get a brief summary of the dataset structure (use get_nba_data_summary ONCE)
        2. Note the key columns available
        3. Verify the data is ready for analysis
        
        IMPORTANT: 
        - Use get_nba_data_summary ONCE only - it provides all needed info
        - Do NOT call read_nba_data or analyze_nba_data multiple times
        - Keep your report concise (2-3 sentences)
        - The data is already clean and ready for analysis
        
        Provide a brief confirmation that the dataset is loaded and ready for analysis.
        """,
        agent=engineer_agent,
        expected_output="A brief confirmation (2-3 sentences) that the dataset is loaded and ready for analysis, including key column names."
    )


def create_data_analysis_task(analyst_agent, data_engineering_task: Task) -> Task:
    """
    Create the data analysis task for extracting insights from NBA data.
    
    Args:
        analyst_agent: The Analyst Agent to assign this task to
        data_engineering_task: The data engineering task for context
        
    Returns:
        Task: Configured data analysis task
    """
    return Task(
        description=f"""
        Using the cleaned NBA 2024-25 dataset, perform comprehensive analysis:
        
        Your tasks:
        1. Analyze player performance metrics:
           - Top performers by points, assists, rebounds
           - Shooting efficiency analysis (FG%, 3P%, FT%)
           - Player efficiency ratings
        2. Team performance analysis:
           - Win/loss records by team
           - Team offensive and defensive statistics
           - Team performance trends
        3. Game insights:
           - High-scoring games
           - Close games vs blowouts
           - Performance by date/period
        4. Identify key patterns and trends:
           - Best performing players
           - Most efficient teams
           - Statistical outliers
        5. Provide actionable insights and recommendations
        
        Create a comprehensive analysis report with key findings and insights.
        """,
        agent=analyst_agent,
        expected_output="A detailed analysis report with key insights, statistical findings, top performers, team analysis, and actionable recommendations based on the NBA 2024-25 data.",
        context=[data_engineering_task]
    )


def create_custom_analysis_task(analyst_agent, user_query: str, data_engineering_task: Task = None, csv_path: str = None) -> Task:
    """
    Create a custom data analysis task based on user input.
    
    Args:
        analyst_agent: The Analyst Agent to assign this task to
        user_query: The user's custom analysis query/task
        data_engineering_task: The data engineering task for context (optional for parallel execution)
        csv_path: Path to CSV file (for reference in description)
        
    Returns:
        Task: Configured custom analysis task
    """
    data_path = csv_path or NBA_DATA_PATH
    context = [data_engineering_task] if data_engineering_task else []
    
    return Task(
        description=f"""
        Using the dataset located at {data_path}, perform the following analysis as requested by the user:
        
        {user_query}
        
        IMPORTANT INSTRUCTIONS:
        1. For queries requiring aggregations (sum, count, average, top N, etc.), you MUST use the 'analyze_nba_data' tool.
        2. The 'analyze_nba_data' tool allows you to execute pandas code for grouping, aggregating, sorting, and filtering.
        3. Examples of when to use 'analyze_nba_data':
           - Finding top players by statistics (e.g., "top 5 three-point shooters")
           - Calculating totals or averages per player/team
           - Grouping and aggregating data
           - Statistical analysis requiring groupby operations
        4. Use 'semantic_search_nba_data' only for finding specific game records or examples, NOT for aggregations.
        5. Plan your analysis: First understand what data you need, then use the appropriate tool to get aggregated results.
        
        Steps to follow:
        1. If the query asks for "top N" or aggregations, use analyze_nba_data with pandas groupby operations
        2. For "top 5 three-point shooters": group by Player, sum the '3P' column, sort descending, take top 5
        3. Present the results clearly with player names and their statistics
        
        Provide a clear, comprehensive answer with relevant statistics, insights, and any supporting data from the dataset.
        """,
        agent=analyst_agent,
        expected_output="A detailed analysis report addressing the user's query with relevant insights, statistics, and findings from the data.",
        context=context
    )


def create_storyteller_task(storyteller_agent, analysis_task: Task) -> Task:
    """
    Create a storyteller task that creates headlines and storylines from the analysis results.
    
    Args:
        storyteller_agent: The Storyteller Agent to assign this task to
        analysis_task: The analysis task whose output will be used to create headlines and content
        
    Returns:
        Task: Configured storyteller task
    """
    return Task(
        description="""
        Review the data analysis results and create engaging headlines and compelling storylines that bring the data to life.
        
        Your tasks:
        1. Read and understand the analysis results thoroughly
        2. Identify the most important and interesting findings
        3. Create 3-5 compelling headlines that:
           - Are catchy and attention-grabbing
           - Accurately reflect the key insights
           - Use engaging sports journalism language
           - Are suitable for display to users
        
        4. Write engaging storylines/content for each headline that:
           - Tells a story about the findings
           - Provides context and narrative around the statistics
           - Makes the data come alive with compelling prose
           - Explains why these insights matter
           - Uses vivid language and storytelling techniques
           - Is 2-3 paragraphs per storyline (enough to be engaging but concise)
        
        5. Format your output as follows:
           HEADLINES:
           [List of 3-5 headlines, one per line]
           
           STORYLINES:
           [For each headline, write 2-3 paragraphs of engaging content that tells the story behind the data]
        
        Make both the headlines and storylines exciting, memorable, and true to the data insights. 
        Write like a sports journalist who knows how to make statistics compelling and human.
        """,
        agent=storyteller_agent,
        expected_output="A formatted output with 3-5 engaging headlines followed by detailed storylines (2-3 paragraphs each) that bring the data analysis to life with compelling narrative and context.",
        context=[analysis_task]
    )