app.py

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

# FastAPI app
app = FastAPI(title="Check-In Expansion API")

@app.get("/")
def read_root():
    return {"status": "ok"}
# Load model
generator = pipeline(
    "summarization",  # use summarization since text2text-generation is deprecated in HF metadata
    model="Dc-4nderson/checkin-model",
    tokenizer="Dc-4nderson/checkin-model"
)
# Request/Response schema
class CheckInRequest(BaseModel):
    task: str

class CheckInResponse(BaseModel):
    checkin: str

@app.post("/predict", response_model=CheckInResponse)
def predict(request: CheckInRequest):
    """Expand a short input into a detailed check-in."""
    result = generator(request.task, max_length=200, num_beams=4, do_sample=False)
    return {"checkin": result[0]["summary_text"]}