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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"
)
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