import base64
import json
from fastapi import FastAPI, HTTPException, Depends, Header, Request, Body
from schemas import AudioInput, DetectionResult
from model_service import get_model_service, ModelService

app = FastAPI(title="AI Voice Detection API", version="1.0.0")

@app.on_event("startup")
async def startup_event():
    # Fixes 504 Timeout
    print("API is starting up...")

API_KEY = "my_secret_key_123"

async def verify_api_key(x_api_key: str = Header(...)):
    if x_api_key != API_KEY:
        raise HTTPException(status_code=401, detail="Invalid API Key")
    return x_api_key

@app.post("/detect", response_model=DetectionResult)
async def detect_voice(
    request: Request,
    payload: dict = Body(..., example={"audioBase64": "PASTE_HERE"}),
    service: ModelService = Depends(get_model_service),
    api_key: str = Depends(verify_api_key)
):
    try:
        audio_b64 = None
        # Try JSON first (Portal vs Manual)
        try:
            body = await request.json()
            keys = ["audioBase64", "audio_base_64", "audio", "data", "file"]
            for k in keys:
                if k in body and body[k]:
                    audio_b64 = body[k]
                    break
            if not audio_b64:
                for v in body.values():
                    if isinstance(v, str) and len(v) > 100:
                        audio_b64 = v
                        break
        except Exception:
            # FALLBACK: Handle if the body is just the raw Base64 string
            raw_body = await request.body()
            audio_b64 = raw_body.decode('utf-8')
        
        if not audio_b64 or len(str(audio_b64)) < 20:
             raise HTTPException(status_code=422, detail="No valid audio data.")

        # CLEANUP: Remove quotes, spaces, and Data URI prefix
        audio_b64 = str(audio_b64).strip().strip('"').strip("'")
        if "," in audio_b64:
            audio_b64 = audio_b64.split(",")[1]

        audio_bytes = base64.b64decode(audio_b64)
    except Exception as e:
        if isinstance(e, HTTPException): raise e
        raise HTTPException(status_code=400, detail=f"Parsing error: {str(e)}")

    try:
        # Inference (HUMAN or AI_GENERATED)
        label, confidence = service.predict(audio_bytes)
        return DetectionResult(label=label, confidence=confidence, message="Analysis successful")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Model error: {str(e)}")

@app.get("/")
def read_root(): return {"status": "Running"}