app.py

# app.py

import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp"
from fastapi import FastAPI
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("papahawk/keya-560m")

model = AutoModelForCausalLM.from_pretrained("papahawk/keya-560m")

# Specify the model name
model_name = "papahawk/keya-560m"

# Check if the model has been downloaded
if not os.path.exists(model_name):
    # If not, download the model
    tokenizer = AutoTokenizer.from_pretrained("papahawk/keya-560m")
    model = AutoModelForCausalLM.from_pretrained("papahawk/keya-560m")
    # Save the model and tokenizer locally
    tokenizer.save_pretrained(model_name)
    model.save_pretrained(model_name)
else:
    # If the model has been downloaded, load it from the local file system
    tokenizer = AutoTokenizer.from_pretrained(model_name, local_files_only=True)
    model = AutoModelForCausalLM.from_pretrained(model_name, local_files_only=True)

app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello": "World"}

@app.post("/generate")
def generate_text(prompt: Optional[str] = None):
    if prompt is None:
        with open('prompt.txt', 'r') as file:
            prompt = file.read()
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(inputs["input_ids"])
    text = tokenizer.decode(outputs[0])
    return {"generated_text": text}