app.py

import os
os.environ["HF_HOME"] = "/tmp"

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

app = FastAPI()

model_name = "zakihassan04/beero_somali"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

class Query(BaseModel):
    inputs: str

@app.get("/")
def read_root():
    return {"status": "ok"}

@app.post("/generate")
async def generate_text(query: Query):
    input_ids = tokenizer.encode(query.inputs, return_tensors="pt").to(device)
    output_ids = model.generate(input_ids, max_length=128)
    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return {"generated_text": response}