app.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

app = FastAPI(
    title="Roadblock Detection API",
    description="Detects whether a check-in contains a roadblock",
    version="1.0"
)

MODEL_NAME = "mjpsm/roadblock-classifier-v1"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

model.eval()


class Request(BaseModel):
    text: str


@app.get("/")
def root():
    return {"message": "Roadblock API is live"}


@app.post("/predict")
def predict(req: Request):
    inputs = tokenizer(
        req.text,
        return_tensors="pt",
        truncation=True,
        padding=True
    )

    # 🔥 FIX HERE
    inputs.pop("token_type_ids", None)

    with torch.no_grad():
        outputs = model(**inputs)

    logits = outputs.logits
    probs = torch.softmax(logits, dim=1)

    pred = torch.argmax(probs, dim=1).item()
    confidence = probs[0][pred].item()

    label = model.config.id2label[pred]

    return {
        "input": req.text,
        "prediction": label,
        "confidence": round(confidence, 3)
    }