app.py

from fastapi import FastAPI, HTTPException
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import torch

app = FastAPI()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load config first
config = AutoConfig.from_pretrained("SrivarshiniGanesan/finetuned-stress-model")
model = AutoModelForSequenceClassification.from_pretrained(
    "SrivarshiniGanesan/finetuned-stress-model",
    config=config
).to(device)
tokenizer = AutoTokenizer.from_pretrained("SrivarshiniGanesan/finetuned-stress-model")

@app.post("/predict/")
def predict(text: str):
    try:
        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
        with torch.no_grad():
            outputs = model(**inputs)
        
        probs = torch.softmax(outputs.logits, dim=-1)
        class_labels = config.id2label if config.id2label else {0: "No Stress", 1: "Stress"}
        stress_idx = list(class_labels.values()).index("Stress")
        
        return {"stress_probability": probs[0, stress_idx].item()}
    
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Prediction failed: {str(e)}"
        )