app.py

from fastapi import FastAPI
from pydantic import BaseModel
import torch
from transformers import RobertaTokenizer

app = FastAPI()

# Load tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = torch.load("roberta_model.pkl", map_location=torch.device("cpu"))
model.eval()

class RequestBody(BaseModel):
    text: str

@app.post("/predict")
def predict(data: RequestBody):
    inputs = tokenizer(data.text, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits if hasattr(outputs, "logits") else outputs
    preds = torch.sigmoid(logits).cpu().numpy().tolist()
    return {"predictions": preds}