crew.py

"""
CrewAI Configuration - Policy Summarizer
"""
import os
from crewai import Agent, Task, Crew, Process
from tools.web_scraper import web_scraper_tool
from tools.text_analyzer import text_analyzer_tool
from utils.logger import log_agent_action, clear_logs


def create_agents():
    """Create the 3 agents"""
    
    orchestrator = Agent(
        role="Policy Analysis Orchestrator",
        goal="Coordinate the policy analysis and create a user-friendly summary",
        backstory="""You are an expert at analyzing legal documents and presenting 
        complex information in simple terms. You coordinate the analysis workflow.""",
        verbose=True,
        allow_delegation=True
    )
    
    scraper = Agent(
        role="Web Content Scraper",
        goal="Extract clean policy text from web URLs",
        backstory="""You specialize in web scraping and content extraction.
        You can extract policy text while filtering out irrelevant content.""",
        verbose=True,
        allow_delegation=False,
        tools=[web_scraper_tool]
    )
    
    analyzer = Agent(
        role="Policy Analyzer", 
        goal="Analyze policies to identify key points, rights, and concerns",
        backstory="""You are a legal expert who analyzes terms of service and 
        privacy policies. You identify user rights and potential red flags.""",
        verbose=True,
        allow_delegation=False,
        tools=[text_analyzer_tool]
    )
    
    return orchestrator, scraper, analyzer


def create_tasks(orchestrator, scraper, analyzer, url: str):
    """Create the tasks for each agent"""
    
    scrape_task = Task(
        description=f"""
        Scrape the policy content from: {url}
        Use the web_scraper_tool to fetch and extract the text.
        Return the full policy text content.
        """,
        expected_output="The extracted policy text content",
        agent=scraper
    )
    
    analyze_task = Task(
        description="""
        Analyze the scraped policy content:
        1. Use text_analyzer_tool to identify key sections
        2. Find user rights (deletion, access, opt-out, etc.)
        3. Identify concerns and red flags
        4. Note data collection and sharing practices
        """,
        expected_output="Structured analysis with sections, rights, and concerns",
        agent=analyzer,
        context=[scrape_task]
    )
    
    summary_task = Task(
        description="""
        Create a user-friendly summary with these sections:
        
        ## 📄 Policy Summary
        [3-5 key points about this policy]
        
        ## ✅ Your Rights
        [List user rights with brief explanations]
        
        ## ⚠️ Concerns & Warnings  
        [List red flags with severity: 🔴 High, 🟡 Medium, 🟢 Low]
        
        ## 💡 Recommendation
        [Overall assessment and advice]
        
        Use simple language, avoid legal jargon.
        """,
        expected_output="A formatted, user-friendly policy summary",
        agent=orchestrator,
        context=[scrape_task, analyze_task]
    )
    
    return [scrape_task, analyze_task, summary_task]


def run_policy_analysis(url: str) -> str:
    """Main function to analyze a policy URL"""
    
    clear_logs()
    
    log_agent_action(
        agent_name="System",
        action="Starting Analysis",
        input_summary=f"URL length: {len(url)}",
        output_summary="Initializing agents...",
        duration_seconds=0,
        success=True
    )
    
    try:
        orchestrator, scraper, analyzer = create_agents()
        tasks = create_tasks(orchestrator, scraper, analyzer, url)
        
        crew = Crew(
            agents=[orchestrator, scraper, analyzer],
            tasks=tasks,
            process=Process.sequential,
            verbose=True
        )
        
        result = crew.kickoff()
        return str(result)
        
    except Exception as e:
        return f"❌ Error: {str(e)}"