from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional, Dict, Any
from datetime import datetime

from ..core.detector import ContentCategory, ProfanityDetector
from ..core.delexicalizer import Delexicalizer
from ..core.classifier import ContextClassifier, ToxicityLevel
from ..core.ai_classifier import AIClassifier
from ..utils.logger import audit_logger

app = FastAPI(
    title="Profanity Handler API",
    description="Context-aware profanity handling system for AI-assisted reporting",
    version="0.1.0"
)

detector = ProfanityDetector()
delexicalizer = Delexicalizer()
classifier = ContextClassifier()
ai_classifier = AIClassifier()

class TextRequest(BaseModel):
    text: str
    context: ContentCategory
    strict_mode: Optional[bool] = False
    use_ai: Optional[bool] = True
    include_explicit_in_export: Optional[bool] = False

class TextResponse(BaseModel):
    request_id: str
    contains_profanity: bool
    toxicity_level: ToxicityLevel
    safe_text: Optional[str] = None
    export_text: Optional[str] = None
    message: str
    ai_confidence: Optional[Dict[str, float]] = None
    detected_words: Optional[list] = None

@app.post("/analyze", response_model=TextResponse)
async def analyze_text(request: TextRequest) -> TextResponse:
    """Analyze text for profanity with context awareness."""
    
    # Reset delexicalizer for new request
    delexicalizer.reset()
    
    # Check for profanity
    has_profanity = detector.detect_profanity(
        request.text,
        request.context,
        request.strict_mode
    )
    
    # Classify toxicity (use AI if requested and available)
    ai_confidence = None
    if request.use_ai:
        toxicity, ai_confidence = ai_classifier.classify(request.text)
    else:
        toxicity = classifier.classify_context(request.text)
    
    # Get detected words
    detected_words = list(detector.get_detected_words()) if has_profanity else []
    
    # Handle based on context and toxicity
    if has_profanity:
        safe_text = delexicalizer.delexicalize(request.text)
        message = (
            "Heads up: explicit language detected in record names. "
            "Proceeding with safe rendering."
        )
    else:
        safe_text = request.text
        message = "No issues detected."
    
    # Handle export text based on user preference
    export_text = request.text if request.include_explicit_in_export else safe_text
    
    # Log the request
    request_id = audit_logger.log_request(
        text=request.text,
        context=request.context.value,
        contains_profanity=has_profanity,
        toxicity_level=toxicity.value,
        safe_text=safe_text,
        metadata={
            "strict_mode": request.strict_mode,
            "use_ai": request.use_ai,
            "include_explicit_in_export": request.include_explicit_in_export
        }
    )
    
    return TextResponse(
        request_id=request_id,
        contains_profanity=has_profanity,
        toxicity_level=toxicity,
        safe_text=safe_text,
        export_text=export_text,
        message=message,
        ai_confidence=ai_confidence,
        detected_words=detected_words
    )

@app.get("/logs/redacted")
async def get_redacted_logs(date: Optional[str] = None):
    """Get redacted logs for analytics (safe to expose)."""
    logs = audit_logger.get_redacted_logs(date)
    return {"logs": logs, "count": len(logs)}

@app.get("/logs/verbatim/{request_id}")
async def get_verbatim_log(request_id: str, date: Optional[str] = None):
    """
    Get verbatim log for compliance (should be access-controlled in production).
    This endpoint demonstrates RBAC - in production, this would require special permissions.
    """
    log = audit_logger.get_verbatim_log(request_id, date)
    return log