#main.py
from fastapi import FastAPI, HTTPException, Depends, status
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from sqlalchemy.orm import Session
import models
from database import engine, SessionLocal
from ml_utils import ScamDetectionService
from typing import Optional
import time
from datetime import datetime

# Create FastAPI app
app = FastAPI(
    title="Multilingual Scam Detection API",
    description="Simple API to detect scams in text messages and URLs",
    version="1.0.0"
)

# Add CORS middleware
# app.add_middleware(
#     CORSMiddleware,
#     allow_origins=["*"],  # Configure for production
#     allow_credentials=True,
#     allow_methods=["*"],
#     allow_headers=["*"],
# )

# Create database tables
models.Base.metadata.create_all(bind=engine)

# Initialize scam detection service
scam_service = ScamDetectionService()


# Database dependency
def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()


# Pydantic models for requests/responses
class TextAnalysisRequest(BaseModel):
    text: str = Field(min_length=1, max_length=5000)
    language: Optional[str] = Field(default=None, description="Language code (en, es, fr)")


class URLAnalysisRequest(BaseModel):
    url: str = Field(min_length=1)
    context: Optional[str] = Field(default="", description="Additional context")


class ScamAnalysisResponse(BaseModel):
    risk_level: str
    confidence: float
    reasoning: str
    detected_language: Optional[str] = None
    processing_time: float
    timestamp: datetime


class URLAnalysisResponse(ScamAnalysisResponse):
    url_status: str
    domain: str


# Root endpoint
@app.get("/")
def root():
    return {
        "message": "Multilingual Scam Detection API",
        "version": "1.0.0",
        "endpoints": {
            "analyze_text": "/analyze/text/",
            "analyze_url": "/analyze/url/",
            "health": "/health/",
            "analytics": "/analytics/summary/"
        }
    }


# Health check
@app.get("/health/")
def health_check():
    return {
        "status": "healthy",
        "timestamp": datetime.now(),
        "database": "connected",
        "ml_service": "active"
    }


# Text analysis endpoint
@app.post("/analyze/text/", response_model=ScamAnalysisResponse)
def analyze_text(request: TextAnalysisRequest, db: Session = Depends(get_db)):
    """Analyze text message for scam indicators"""
    start_time = time.time()

    try:
        # Detect language if not provided
        detected_language = request.language or scam_service.detect_language(request.text)

        # Analyze for scam
        analysis = scam_service.analyze_text_scam(
            text=request.text,
            language=detected_language
        )

        processing_time = time.time() - start_time

        # Save to database
        scam_record = models.ScamAnalysis(
            content=request.text,
            content_type="text",
            risk_level=analysis["risk_level"],
            confidence=analysis["confidence"],
            reasoning=analysis["reasoning"],
            detected_language=detected_language,
            processing_time=processing_time
        )

        db.add(scam_record)
        db.commit()

        return ScamAnalysisResponse(
            risk_level=analysis["risk_level"],
            confidence=analysis["confidence"],
            reasoning=analysis["reasoning"],
            detected_language=detected_language,
            processing_time=round(processing_time, 3),
            timestamp=datetime.now()
        )

    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Analysis failed: {str(e)}"
        )


# URL analysis endpoint
@app.post("/analyze/url/", response_model=URLAnalysisResponse)
def analyze_url(request: URLAnalysisRequest, db: Session = Depends(get_db)):
    """Analyze URL for phishing/malicious behavior"""
    start_time = time.time()

    try:
        # Analyze URL
        analysis = scam_service.analyze_url_scam(
            url=request.url,
            context=request.context
        )

        processing_time = time.time() - start_time

        # Save to database
        scam_record = models.ScamAnalysis(
            content=request.url,
            content_type="url",
            risk_level=analysis["risk_level"],
            confidence=analysis["confidence"],
            reasoning=analysis["reasoning"],
            processing_time=processing_time,
            additional_data={"domain": analysis["domain"], "url_status": analysis["url_status"]}
        )

        db.add(scam_record)
        db.commit()

        return URLAnalysisResponse(
            risk_level=analysis["risk_level"],
            confidence=analysis["confidence"],
            reasoning=analysis["reasoning"],
            url_status=analysis["url_status"],
            domain=analysis["domain"],
            processing_time=round(processing_time, 3),
            timestamp=datetime.now()
        )

    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"URL analysis failed: {str(e)}"
        )


# Language detection endpoint
@app.post("/detect/language/")
def detect_language(text: str):
    """Detect language of input text"""
    try:
        detected_lang = scam_service.detect_language(text)
        confidence = scam_service.get_language_confidence(text)

        return {
            "text_preview": text[:100] + "..." if len(text) > 100 else text,
            "detected_language": detected_lang,
            "confidence": confidence,
            "supported_languages": ["en", "es", "fr"]
        }

    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Language detection failed: {str(e)}"
        )


# Basic analytics
@app.get("/analytics/summary/")
def get_analytics(db: Session = Depends(get_db)):
    """Get basic analytics summary"""
    try:
        total_analyses = db.query(models.ScamAnalysis).count()

        # Count by risk level
        safe_count = db.query(models.ScamAnalysis).filter(
            models.ScamAnalysis.risk_level == "Safe"
        ).count()

        suspicious_count = db.query(models.ScamAnalysis).filter(
            models.ScamAnalysis.risk_level == "Suspicious"
        ).count()

        scam_count = db.query(models.ScamAnalysis).filter(
            models.ScamAnalysis.risk_level == "Scam"
        ).count()

        # Count by content type
        text_count = db.query(models.ScamAnalysis).filter(
            models.ScamAnalysis.content_type == "text"
        ).count()

        url_count = db.query(models.ScamAnalysis).filter(
            models.ScamAnalysis.content_type == "url"
        ).count()

        return {
            "total_analyses": total_analyses,
            "risk_distribution": {
                "Safe": safe_count,
                "Suspicious": suspicious_count,
                "Scam": scam_count
            },
            "content_distribution": {
                "text": text_count,
                "url": url_count
            },
            "supported_languages": ["en", "es", "fr"]
        }

    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Analytics failed: {str(e)}"
        )


# Recent analyses
@app.get("/history/recent/")
def get_recent_analyses(limit: int = 10, db: Session = Depends(get_db)):
    """Get recent scam analyses"""
    try:
        recent = db.query(models.ScamAnalysis).order_by(
            models.ScamAnalysis.created_at.desc()
        ).limit(limit).all()

        return {
            "recent_analyses": [
                {
                    "id": analysis.id,
                    "content_type": analysis.content_type,
                    "risk_level": analysis.risk_level,
                    "confidence": analysis.confidence,
                    "detected_language": analysis.detected_language,
                    "created_at": analysis.created_at,
                    "content_preview": (
                        analysis.content[:50] + "..."
                        if len(analysis.content) > 50
                        else analysis.content
                    )
                }
                for analysis in recent
            ],
            "total_shown": len(recent)
        }

    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"History retrieval failed: {str(e)}"
        )


# Test endpoint with sample data
@app.get("/test/samples/")
def test_samples():
    """Test with sample scam messages"""
    samples = [
        {
            "text": "URGENT! Your bank account will be suspended. Click here: bit.ly/urgent-bank",
            "expected_risk": "Scam"
        },
        {
            "text": "Hello, how are you today? Hope you're doing well!",
            "expected_risk": "Safe"
        },
        {
            "text": "Congratulations! You've won $1000. Click to claim your prize now!",
            "expected_risk": "Suspicious"
        }
    ]

    results = []
    for sample in samples:
        analysis = scam_service.analyze_text_scam(sample["text"])
        results.append({
            "text": sample["text"],
            "expected": sample["expected_risk"],
            "detected": analysis["risk_level"],
            "confidence": analysis["confidence"],
            "reasoning": analysis["reasoning"]
        })

    return {
        "test_results": results,
        "total_tests": len(results)
    }


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)