app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline

app = FastAPI()

clf = pipeline(
    "text-classification",
    model="DelaliScratchwerk/time-period-classifier-bert",
    # If your pipeline supports it, you can also set device here later
)

class PredictRequest(BaseModel):
    inputs: str

@app.post("/predict")
def predict(req: PredictRequest):
    text = (req.inputs or "").strip()
    if not text:
        raise HTTPException(status_code=400, detail="inputs is empty")

    # Hard guard against huge payloads (prevents space crashes)
    if len(text) > 8000:
        text = text[:8000]

    try:
        # Force truncation so the model never errors on long inputs
        result = clf(text, truncation=True, max_length=512)
        return result
    except Exception as e:
        # Return a 400 with the error instead of 500
        raise HTTPException(status_code=400, detail=str(e))