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from fastapi import APIRouter, HTTPException, Query
from src.api.schemas import (
BatchPredictRequest,
BatchPredictResponse,
PredictRequest,
PredictResponse,
VideoRequest,
VideoResponse,
)
from src.api.services import predict_single, to_predict_response
from src.api.state import get_state
from src.api.youtube import CommentsFetchError, extract_video_id, fetch_comments
from src.db.supabase_client import list_predictions, save_prediction
router = APIRouter(tags=["Prediction"])
@router.post("/predict", response_model=PredictResponse)
async def predict(request: PredictRequest):
response = predict_single(request.text, request.threshold)
if request.persist:
save_prediction(
text=request.text,
result=response,
source="user_comment" if request.author else "api_direct",
video_id=request.video_id,
author=request.author,
threshold=request.threshold,
latency_ms=response.latency_ms,
)
return response
@router.post("/predict-batch", response_model=BatchPredictResponse)
async def predict_batch(request: BatchPredictRequest):
t0 = time.perf_counter()
results: list[PredictResponse] = []
for text in request.texts:
if not text.strip():
continue
single = predict_single(text.strip(), request.threshold)
results.append(single)
save_prediction(
text=text.strip(),
result=single,
source="api_direct",
threshold=request.threshold,
latency_ms=single.latency_ms,
)
total_ms = round((time.perf_counter() - t0) * 1000, 2)
toxic_count = sum(1 for r in results if r.is_toxic)
return BatchPredictResponse(
results=results,
total=len(results),
toxic_count=toxic_count,
latency_ms=total_ms,
)
@router.post("/predict-video", response_model=VideoResponse)
async def predict_video(request: VideoRequest):
try:
comments, source = fetch_comments(request.url, request.max_comments)
except CommentsFetchError as exc:
raise HTTPException(status_code=422, detail=str(exc)) from exc
except Exception as exc:
raise HTTPException(status_code=422, detail=f"Failed to fetch comments: {exc}") from exc
if not comments:
raise HTTPException(status_code=404, detail="No comments found for this video")
video_id = extract_video_id(request.url)
t0 = time.perf_counter()
results: list[PredictResponse] = []
service = get_state()["service"]
if service is None:
raise HTTPException(status_code=503, detail="Model not loaded")
for text in comments:
if not text.strip():
continue
raw = service.predict(text)
response = to_predict_response(text, raw, 0.0, request.threshold)
results.append(response)
save_prediction(
text=text,
result=response,
source="video_fetch",
video_id=video_id,
video_url=request.url,
threshold=request.threshold,
latency_ms=response.latency_ms,
)
total_ms = round((time.perf_counter() - t0) * 1000, 2)
toxic_count = sum(1 for r in results if r.is_toxic)
get_state()["predictions_served"] = get_state().get("predictions_served", 0) + len(results)
return VideoResponse(
video_url=request.url,
total_fetched=len(results),
toxic_count=toxic_count,
toxic_rate=round(toxic_count / len(results), 4) if results else 0.0,
results=results,
source=source,
)
@router.get("/predictions")
async def get_predictions(
video_id: str | None = Query(default=None),
source: str | None = Query(default=None),
limit: int = Query(default=50, ge=1, le=200),
):
rows = list_predictions(video_id=video_id, source=source, limit=limit)
return {"predictions": rows, "total": len(rows)}
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