app.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import os

app = FastAPI()

# Model and token
model_name = "meta-llama/Llama-4-11B-Instruct"
token = os.getenv("test")  # use the secret you named in HF Space

# Load tokenizer + model
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    use_auth_token=token
)

# Input schema
class InputText(BaseModel):
    text: str

# API endpoint
@app.post("/predict")
def predict(item: InputText):
    inputs = tokenizer(item.text, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=200)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"result": result}