"""
Text Analyzer Tool - Analyzes policy text to identify sections and concerns
"""
from crewai.tools import tool
from typing import List, Dict
import re
import time

from utils.logger import log_agent_action

# Keywords for identifying sections
SECTION_KEYWORDS = {
    'data_collection': ['collect', 'gather', 'information we collect', 'personal data'],
    'data_sharing': ['share', 'third party', 'partners', 'disclose', 'sell'],
    'user_rights': ['your rights', 'opt-out', 'delete', 'access your data', 'gdpr', 'ccpa'],
    'data_retention': ['retain', 'retention', 'how long', 'keep your'],
    'security': ['security', 'protect', 'encryption', 'safeguard'],
    'cookies': ['cookie', 'tracking', 'analytics'],
}

# Red flag keywords
RED_FLAG_KEYWORDS = [
    'sell your data', 'sell your information', 'share with third parties',
    'advertising partners', 'indefinitely', 'without notice',
    'at our discretion', 'waive your right', 'arbitration', 'class action waiver'
]


def chunk_text(text: str, chunk_size: int = 2000, overlap: int = 200) -> List[str]:
    """Split text into overlapping chunks."""
    if len(text) <= chunk_size:
        return [text]
    
    chunks = []
    start = 0
    
    while start < len(text):
        end = start + chunk_size
        if end < len(text):
            para_break = text.rfind('\n\n', start, end)
            if para_break > start + chunk_size // 2:
                end = para_break
        
        chunks.append(text[start:end].strip())
        start = end - overlap
        
        if start >= len(text) - overlap:
            break
    
    return chunks


def identify_sections(text: str) -> Dict[str, List[str]]:
    """Identify relevant sections in the policy text."""
    sections = {key: [] for key in SECTION_KEYWORDS}
    paragraphs = re.split(r'\n{2,}', text)
    
    for paragraph in paragraphs:
        para_lower = paragraph.lower()
        for section_type, keywords in SECTION_KEYWORDS.items():
            for keyword in keywords:
                if keyword in para_lower:
                    excerpt = paragraph[:500] + "..." if len(paragraph) > 500 else paragraph
                    if excerpt not in sections[section_type]:
                        sections[section_type].append(excerpt)
                    break
    
    return sections


def find_red_flags(text: str) -> List[Dict[str, str]]:
    """Find potential concerns in the policy."""
    red_flags = []
    text_lower = text.lower()
    
    for keyword in RED_FLAG_KEYWORDS:
        if keyword in text_lower:
            idx = text_lower.find(keyword)
            start = max(0, idx - 100)
            end = min(len(text), idx + len(keyword) + 100)
            context = text[start:end].strip()
            red_flags.append({'keyword': keyword, 'context': context})
    
    return red_flags


@tool("text_analyzer")
def text_analyzer_tool(text: str) -> str:
    """
    Analyzes policy text to identify key sections and potential concerns.
    
    Args:
        text: The policy text content to analyze
        
    Returns:
        Structured analysis with sections and red flags
    """
    start_time = time.time()
    
    if not text or len(text.strip()) < 100:
        error_msg = "Text too short for analysis"
        log_agent_action("Text Analyzer Tool", "Validation", f"Received {len(text) if text else 0} chars",
                        error_msg, time.time() - start_time, False, error_msg)
        return f"Error: {error_msg}"
    
    try:
        chunks = chunk_text(text)
        all_sections = {key: [] for key in SECTION_KEYWORDS}
        all_red_flags = []
        
        for chunk in chunks:
            sections = identify_sections(chunk)
            for key, excerpts in sections.items():
                all_sections[key].extend(excerpts)
            
            flags = find_red_flags(chunk)
            all_red_flags.extend(flags)
        
        # Deduplicate
        for key in all_sections:
            all_sections[key] = list(set(all_sections[key]))[:3]
        
        seen_keywords = set()
        unique_flags = []
        for flag in all_red_flags:
            if flag['keyword'] not in seen_keywords:
                seen_keywords.add(flag['keyword'])
                unique_flags.append(flag)
        all_red_flags = unique_flags[:10]
        
        # Build result
        result_parts = ["=== POLICY ANALYSIS ===\n"]
        
        result_parts.append("## KEY SECTIONS:\n")
        for section_type, excerpts in all_sections.items():
            if excerpts:
                result_parts.append(f"\n### {section_type.upper().replace('_', ' ')}:")
                for i, excerpt in enumerate(excerpts, 1):
                    result_parts.append(f"{i}. {excerpt[:300]}...")
        
        result_parts.append("\n\n## POTENTIAL CONCERNS:\n")
        if all_red_flags:
            for i, flag in enumerate(all_red_flags, 1):
                result_parts.append(f"{i}. **{flag['keyword'].upper()}**")
                result_parts.append(f"   Context: \"{flag['context']}\"")
        else:
            result_parts.append("No major red flags identified.")
        
        result_parts.append(f"\n\n## STATS: {len(text)} chars, {len(chunks)} chunks, {len(all_red_flags)} concerns")
        
        result = "\n".join(result_parts)
        
        log_agent_action("Text Analyzer Tool", "Analysis", f"Analyzed {len(chunks)} chunks",
                        f"Found {len(all_red_flags)} concerns", time.time() - start_time, True)
        
        return result
        
    except Exception as e:
        error_msg = f"Analysis error: {str(e)}"
        log_agent_action("Text Analyzer Tool", "Analysis", "Processing text", error_msg,
                        time.time() - start_time, False, error_msg)
        return f"Error: {error_msg}"