src/api.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import BertForSequenceClassification, AutoTokenizer
from safetensors.torch import load_file
import torch

app = FastAPI()

tokenizer = AutoTokenizer.from_pretrained("Reyall/nlp-disease-model")
model = BertForSequenceClassification.from_pretrained("Reyall/nlp-disease-model")



state_dict = load_file("model.safetensors")
model.load_state_dict(state_dict)
model.eval()

class TextRequest(BaseModel):
    text: str

@app.post("/predict")
async def predict_endpoint(request: TextRequest):
    inputs = tokenizer(request.text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze().tolist()
    return {"probs": probs}