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4fc93b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | """API route handlers."""
import logging
from fastapi import APIRouter, HTTPException
from app.models.schemas import (
AnalysisRequest,
AnalysisResponse,
ErrorResponse,
HealthResponse,
)
from app.services.download import download_file
from app.services.detector import get_detector
from app.core.config import get_settings
from app.utils.exceptions import DeepfakeDetectionError
logger = logging.getLogger(__name__)
router = APIRouter()
@router.get(
"/",
response_model=HealthResponse,
tags=["Health"],
summary="Health check endpoint",
)
async def health_check() -> HealthResponse:
"""
Health check endpoint to verify service is running.
Returns:
Service status and version information
"""
settings = get_settings()
logger.info("Health check endpoint accessed")
available_models = ["mock"] # Add more as you implement them
return HealthResponse(
status="ok",
service="Deepfake Detection Service",
version=settings.APP_VERSION,
available_models=available_models,
)
@router.post(
"/analyze",
response_model=AnalysisResponse,
responses={
400: {"model": ErrorResponse, "description": "Bad request"},
408: {"model": ErrorResponse, "description": "Request timeout"},
500: {"model": ErrorResponse, "description": "Internal server error"},
},
tags=["Analysis"],
summary="Analyze file for deepfake detection",
)
async def analyze(request: AnalysisRequest) -> AnalysisResponse:
"""
Analyze a file for deepfake detection.
Args:
request: AnalysisRequest containing file_url and optional model selection
Returns:
AnalysisResponse with detection results
Raises:
HTTPException: For various error conditions during processing
"""
settings = get_settings()
detector_model = request.model or settings.DEFAULT_DETECTOR_MODEL
logger.info(
f"Received analysis request for URL: {request.file_url} "
f"using model: {detector_model}"
)
try:
try:
detector = get_detector(detector_model)
except ValueError as e:
logger.error(f"Invalid detector model: {str(e)}")
raise HTTPException(
status_code=400,
detail=str(e),
)
file_bytes = await download_file(str(request.file_url))
if not file_bytes:
logger.error("File download returned empty bytes")
raise HTTPException(
status_code=500,
detail="Failed to download and process file",
)
analysis_result = await detector.detect(file_bytes)
logger.info(
f"Analysis request completed successfully. "
f"File URL: {request.file_url}, Model: {detector_model}, "
f"Result: {analysis_result}"
)
return AnalysisResponse(
is_deepfake=analysis_result["is_deepfake"],
confidence=analysis_result["confidence"],
analysis_time=analysis_result["analysis_time"],
model_used=detector_model,
)
except HTTPException:
raise
except DeepfakeDetectionError as e:
logger.error(f"Detection error: {e.message}")
raise HTTPException(
status_code=e.status_code,
detail=e.message,
)
except Exception as e:
logger.error(f"Unexpected error during analysis: {str(e)}", exc_info=True)
raise HTTPException(
status_code=500,
detail="An unexpected error occurred during analysis. Please try again later.",
)
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