app/api/routes.py

"""API Routes for VoiceGuard - Matching Hackathon Requirements Exactly.

Endpoint: POST /api/voice-detection
Auth: x-api-key header
Request: {language, audioFormat, audioBase64}
Response: {status, language, classification, confidenceScore, explanation}
"""

import time
from fastapi import APIRouter, HTTPException, Header, UploadFile, File, Form
from typing import Optional

from app.api.schemas import (
    VoiceDetectionRequest,
    VoiceDetectionResponse,
    ErrorResponse,
    HealthResponse
)
from app.core.audio_processor import (
    audio_processor,
    AudioProcessingError,
    InvalidBase64Error,
)
from app.core.detector import detector
from app.config import settings


# Create router
router = APIRouter()


# ============== API KEY VALIDATION ==============

def validate_api_key(x_api_key: Optional[str] = Header(None, alias="x-api-key")) -> str:
    """Validate API key from header."""
    if not x_api_key:
        raise HTTPException(
            status_code=401,
            detail={"status": "error", "message": "Missing API key. Use x-api-key header."}
        )
    
    if x_api_key != settings.API_KEY:
        raise HTTPException(
            status_code=401,
            detail={"status": "error", "message": "Invalid API key"}
        )
    
    return x_api_key


# ============== LOGIC HELPER ==============



async def _process_detection_logic(
    audio_bytes: bytes, 
    language: str
) -> VoiceDetectionResponse:
    """Core logic to process audio/bytes and generate response using Hybrid Detector."""
    start_time = time.time()
    
    # Step 1: Process audio (bytes → convert → resample to 16kHz)
    print(f"📝 Processing audio for language: {language}")
    waveform, duration = audio_processor.process_bytes(audio_bytes)
    print(f"✅ Audio processed: {duration:.2f}s duration")
    
    # Step 2: Run Detection (HuBERT Model)
    print("🔍 Running deepfake detection...")
    result = detector.detect(waveform, language=language)
    
    # Calculate processing time
    processing_time_ms = int((time.time() - start_time) * 1000)
    print(f"⏱️  Total processing time: {processing_time_ms}ms")
    
    # Build response
    return VoiceDetectionResponse(
        status="success",
        language=language.capitalize(),
        classification=result.classification,
        confidenceScore=round(result.confidence, 2),
        explanation=result.explanation
    )


# ============== MAIN DETECTION ENDPOINT (Reference Implementation) ==============
# Matches exact hackathon requirements (JSON Base64)

@router.post(
    "/voice-detection",
    response_model=VoiceDetectionResponse,
    responses={
        401: {"model": ErrorResponse, "description": "Invalid API key"},
        400: {"model": ErrorResponse, "description": "Invalid input"},
        500: {"model": ErrorResponse, "description": "Processing error"}
    },
    summary="Detect AI-Generated Voice (JSON)",
    description="Analyze an MP3 audio file (Base64) to detect if it's AI-generated."
)
async def detect_voice_json(
    request: VoiceDetectionRequest,
    x_api_key: str = Header(..., alias="x-api-key")
):
    """Main detection endpoint (JSON/Base64)."""
    validate_api_key(x_api_key)
    
    try:
        # Decode base64 to bytes locally to reuse common logic
        # audio_processor.decode_base64 raises InvalidBase64Error
        audio_bytes = audio_processor.decode_base64(request.audioBase64)
        
        return await _process_detection_logic(audio_bytes, request.language)
    
    except InvalidBase64Error as e:
        raise HTTPException(
            status_code=400,
            detail={"status": "error", "message": f"Invalid Base64 encoding: {str(e)}"}
        )
    except AudioProcessingError as e:
        raise HTTPException(
            status_code=400,
            detail={"status": "error", "message": str(e)}
        )
    except Exception as e:
        print(f"❌ Error during detection: {e}")
        raise HTTPException(
            status_code=500,
            detail={"status": "error", "message": f"Detection failed: {str(e)}"}
        )


# ============== FILE UPLOAD ENDPOINT (For Testing/Ease of Use) ==============

@router.post(
    "/voice-detection/file",
    response_model=VoiceDetectionResponse,
    responses={
        401: {"model": ErrorResponse, "description": "Invalid API key"},
        400: {"model": ErrorResponse, "description": "Invalid input"},
    },
    summary="Detect AI-Generated Voice (File Upload)",
    description="Upload an audio file (MP3/WAV) to detect if it's AI-generated."
)
async def detect_voice_file(
    file: UploadFile = File(..., description="Audio file (MP3/WAV)"),
    language: str = Form(..., description="Language: Tamil, English, Hindi, Malayalam, Telugu"),
    x_api_key: str = Header(..., alias="x-api-key")
):
    """File upload detection endpoint."""
    validate_api_key(x_api_key)
    
    try:
        content = await file.read()
        return await _process_detection_logic(content, language)
        
    except AudioProcessingError as e:
        raise HTTPException(
            status_code=400,
            detail={"status": "error", "message": str(e)}
        )
    except Exception as e:
        print(f"❌ Error during detection: {e}")
        raise HTTPException(
            status_code=500,
            detail={"status": "error", "message": f"Detection failed: {str(e)}"}
        )


# ============== HEALTH CHECK (bonus) ==============

@router.get(
    "/health",
    response_model=HealthResponse,
    summary="Health Check",
    description="Check API health and model status"
)
async def health_check():
    """Health check endpoint."""
    return HealthResponse(
        status="healthy",
        model_loaded=detector.is_loaded,
        version="1.0.0"
    )