src/api/main.py

"""
FastAPI Main Application

Production-ready API for multimodal misinformation detection.

Features:
- Async endpoints
- Rate limiting
- Authentication
- Background task processing
- Comprehensive error handling
"""

from fastapi import FastAPI, File, UploadFile, HTTPException, Depends, BackgroundTasks, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from pydantic import BaseModel, Field
from typing import Optional, List, Dict
import uvicorn
from datetime import datetime
import logging
import asyncio
from pathlib import Path
import tempfile
import os

# Import detection modules
import sys
sys.path.append(str(Path(__file__).parent.parent))

from detection.deepfake_detector import DeepfakeDetector
from detection.ai_text_detector import AITextDetector
from detection.anomaly_detector import AnomalyDetector

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Initialize FastAPI app
app = FastAPI(
    title="Multimodal Misinformation Detection API",
    description="Production API for detecting deepfakes, AI-generated content, and coordinated campaigns",
    version="1.0.0",
    docs_url="/docs",
    redoc_url="/redoc"
)

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

# Security
security = HTTPBearer()

# Initialize detectors (lazy loading for performance)
_deepfake_detector = None
_ai_text_detector = None
_anomaly_detector = None


def get_deepfake_detector():
    """Lazy load deepfake detector."""
    global _deepfake_detector
    if _deepfake_detector is None:
        _deepfake_detector = DeepfakeDetector()
    return _deepfake_detector


def get_ai_text_detector():
    """Lazy load AI text detector."""
    global _ai_text_detector
    if _ai_text_detector is None:
        _ai_text_detector = AITextDetector()
    return _ai_text_detector


def get_anomaly_detector():
    """Lazy load anomaly detector."""
    global _anomaly_detector
    if _anomaly_detector is None:
        _anomaly_detector = AnomalyDetector()
    return _anomaly_detector


# Request/Response Models
class TextAnalysisRequest(BaseModel):
    text: str = Field(..., min_length=10, description="Text to analyze")
    detailed: bool = Field(default=True, description="Return detailed analysis")


class TextAnalysisResponse(BaseModel):
    verdict: str
    confidence: float
    perplexity: Optional[float] = None
    explanation: str
    timestamp: datetime
    processing_time_ms: float


class ImageAnalysisResponse(BaseModel):
    verdict: str
    confidence: float
    faces_analyzed: int
    explanation: str
    artifacts_detected: List[str]
    timestamp: datetime
    processing_time_ms: float


class HealthResponse(BaseModel):
    status: str
    version: str
    timestamp: datetime
    models_loaded: Dict[str, bool]


# Middleware for request timing and security headers
@app.middleware("http")
async def add_process_time_header(request: Request, call_next):
    """Add processing time and security headers to response."""
    start_time = datetime.utcnow()
    response = await call_next(request)
    process_time = (datetime.utcnow() - start_time).total_seconds() * 1000
    response.headers["X-Process-Time-Ms"] = str(process_time)
    
    # Add CSP header that allows Swagger UI to work
    if request.url.path in ["/docs", "/redoc"] or request.url.path.startswith("/openapi"):
        response.headers["Content-Security-Policy"] = (
            "default-src 'self'; "
            "script-src 'self' 'unsafe-inline' 'unsafe-eval' https://cdn.jsdelivr.net; "
            "style-src 'self' 'unsafe-inline' https://cdn.jsdelivr.net; "
            "img-src 'self' data: https:; "
            "font-src 'self' data: https://cdn.jsdelivr.net;"
        )
    
    return response


# Authentication dependency (simplified)
async def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
    """
    Verify API token.
    In production, implement proper JWT verification.
    """
    token = credentials.credentials
    
    # Simplified check - implement proper verification
    if token != os.getenv("API_TOKEN", "dev-token"):
        raise HTTPException(
            status_code=401,
            detail="Invalid authentication credentials"
        )
    return token


# API Endpoints

@app.get("/", response_model=HealthResponse)
async def root():
    """Root endpoint with API health status."""
    return {
        "status": "operational",
        "version": "1.0.0",
        "timestamp": datetime.utcnow(),
        "models_loaded": {
            "deepfake_detector": _deepfake_detector is not None,
            "ai_text_detector": _ai_text_detector is not None,
            "anomaly_detector": _anomaly_detector is not None
        }
    }


@app.get("/health")
async def health_check():
    """Health check endpoint for monitoring."""
    return {
        "status": "healthy",
        "timestamp": datetime.utcnow().isoformat()
    }


@app.post("/api/v1/analyze/text", response_model=TextAnalysisResponse)
async def analyze_text(
    request: TextAnalysisRequest,
    background_tasks: BackgroundTasks,
    # token: str = Depends(verify_token)  # Uncomment for auth
):
    """
    Analyze text for AI generation.
    
    **Example Request:**
    ```json
    {
        "text": "Your text here...",
        "detailed": true
    }
    ```
    """
    start_time = datetime.utcnow()
    
    try:
        detector = get_ai_text_detector()
        result = detector.analyze_text(request.text, detailed=request.detailed)
        
        processing_time = (datetime.utcnow() - start_time).total_seconds() * 1000
        
        # Log analytics in background
        background_tasks.add_task(
            log_analysis,
            "text",
            result['verdict'],
            processing_time
        )
        
        return {
            **result,
            "timestamp": datetime.utcnow(),
            "processing_time_ms": processing_time
        }
    
    except Exception as e:
        logger.error(f"Error analyzing text: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")


@app.post("/api/v1/analyze/image", response_model=ImageAnalysisResponse)
async def analyze_image(
    file: UploadFile = File(...),
    return_attention: bool = False,
    background_tasks: BackgroundTasks = BackgroundTasks(),
    # token: str = Depends(verify_token)
):
    """
    Analyze image for deepfake artifacts.
    
    **Supported formats:** JPG, PNG, WebP
    **Max size:** 10MB
    """
    start_time = datetime.utcnow()
    
    # Validate file
    if file.content_type not in ["image/jpeg", "image/png", "image/webp"]:
        raise HTTPException(
            status_code=400,
            detail="Invalid file type. Supported: JPEG, PNG, WebP"
        )
    
    # Save uploaded file temporarily
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=Path(file.filename).suffix) as tmp:
            content = await file.read()
            tmp.write(content)
            tmp_path = tmp.name
        
        # Analyze
        detector = get_deepfake_detector()
        result = detector.analyze_image(tmp_path, return_attention=return_attention)
        
        processing_time = (datetime.utcnow() - start_time).total_seconds() * 1000
        
        # Cleanup
        os.unlink(tmp_path)
        
        # Log in background
        background_tasks.add_task(
            log_analysis,
            "image",
            result['verdict'],
            processing_time
        )
        
        return {
            **result,
            "timestamp": datetime.utcnow(),
            "processing_time_ms": processing_time
        }
    
    except Exception as e:
        logger.error(f"Error analyzing image: {str(e)}")
        # Cleanup on error
        if 'tmp_path' in locals():
            try:
                os.unlink(tmp_path)
            except:
                pass
        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")


@app.post("/api/v1/analyze/video")
async def analyze_video(
    file: UploadFile = File(...),
    sample_rate: int = 5,
    max_frames: int = 100,
    background_tasks: BackgroundTasks = BackgroundTasks(),
    # token: str = Depends(verify_token)
):
    """
    Analyze video for deepfake artifacts.
    
    **Supported formats:** MP4, AVI, MOV
    **Max size:** 100MB
    **Processing:** Async with job ID returned immediately
    """
    start_time = datetime.utcnow()
    
    # Validate file
    if file.content_type not in ["video/mp4", "video/avi", "video/quicktime"]:
        raise HTTPException(
            status_code=400,
            detail="Invalid file type. Supported: MP4, AVI, MOV"
        )
    
    try:
        # Save file
        with tempfile.NamedTemporaryFile(delete=False, suffix=Path(file.filename).suffix) as tmp:
            content = await file.read()
            tmp.write(content)
            tmp_path = tmp.name
        
        # For large videos, process in background
        # For demo, process synchronously
        detector = get_deepfake_detector()
        result = detector.analyze_video(
            tmp_path,
            sample_rate=sample_rate,
            max_frames=max_frames
        )
        
        processing_time = (datetime.utcnow() - start_time).total_seconds() * 1000
        
        # Cleanup
        os.unlink(tmp_path)
        
        # Log in background
        background_tasks.add_task(
            log_analysis,
            "video",
            result['verdict'],
            processing_time
        )
        
        return {
            **result,
            "timestamp": datetime.utcnow(),
            "processing_time_ms": processing_time
        }
    
    except Exception as e:
        logger.error(f"Error analyzing video: {str(e)}")
        if 'tmp_path' in locals():
            try:
                os.unlink(tmp_path)
            except:
                pass
        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")


@app.post("/api/v1/batch/text")
async def batch_analyze_text(
    texts: List[str],
    background_tasks: BackgroundTasks,
    # token: str = Depends(verify_token)
):
    """
    Batch analyze multiple texts.
    
    **Limit:** 100 texts per request
    """
    if len(texts) > 100:
        raise HTTPException(
            status_code=400,
            detail="Maximum 100 texts per batch"
        )
    
    start_time = datetime.utcnow()
    
    try:
        detector = get_ai_text_detector()
        results = detector.batch_analyze(texts)
        
        processing_time = (datetime.utcnow() - start_time).total_seconds() * 1000
        
        return {
            "results": results,
            "total_analyzed": len(texts),
            "timestamp": datetime.utcnow(),
            "processing_time_ms": processing_time
        }
    
    except Exception as e:
        logger.error(f"Error in batch analysis: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Batch analysis failed: {str(e)}")


# Background task for logging
async def log_analysis(modality: str, verdict: str, processing_time: float):
    """Log analysis for monitoring and analytics."""
    logger.info(
        f"Analysis completed - Modality: {modality}, "
        f"Verdict: {verdict}, Time: {processing_time:.2f}ms"
    )
    # In production: send to monitoring system (Prometheus, CloudWatch, etc.)


# Error handlers
@app.exception_handler(HTTPException)
async def http_exception_handler(request: Request, exc: HTTPException):
    """Custom HTTP exception handler."""
    return JSONResponse(
        status_code=exc.status_code,
        content={
            "error": exc.detail,
            "timestamp": datetime.utcnow().isoformat()
        }
    )


@app.exception_handler(Exception)
async def general_exception_handler(request: Request, exc: Exception):
    """General exception handler."""
    logger.error(f"Unhandled exception: {str(exc)}")
    return JSONResponse(
        status_code=500,
        content={
            "error": "Internal server error",
            "timestamp": datetime.utcnow().isoformat()
        }
    )


# Startup/Shutdown events
@app.on_event("startup")
async def startup_event():
    """Initialize on startup."""
    logger.info("🚀 Starting Multimodal Misinformation Detection API")
    logger.info("📊 API Documentation: http://localhost:8000/docs")


@app.on_event("shutdown")
async def shutdown_event():
    """Cleanup on shutdown."""
    logger.info("🛑 Shutting down API")


if __name__ == "__main__":
    uvicorn.run(
        "main:app",
        host="0.0.0.0",
        port=8000,
        reload=True,
        log_level="info"
    )