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from fastapi import FastAPI, Request, Response, status
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
import time
app = FastAPI()
# Create cache directory
os.makedirs("./model_cache", exist_ok=True)
# Track app status
app_status = {
"status": "initializing",
"model_name": "distilgpt2",
"model_loaded": False,
"tokenizer_loaded": False,
"startup_time": time.time(),
"errors": []
}
# Load model and tokenizer once at startup
model_name = "distilgpt2" # change this to your own model
try:
# Try to load tokenizer
app_status["status"] = "loading_tokenizer"
tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir="./model_cache", local_files_only=False)
app_status["tokenizer_loaded"] = True
# Try to load model
app_status["status"] = "loading_model"
model = AutoModelForCausalLM.from_pretrained(model_name, cache_dir="./model_cache", local_files_only=False)
app_status["model_loaded"] = True
app_status["status"] = "ready"
except Exception as e:
error_msg = f"Error loading model or tokenizer: {str(e)}"
app_status["status"] = "error"
app_status["errors"].append(error_msg)
print(error_msg)
class PromptRequest(BaseModel):
prompt: str
max_new_tokens: int = 50
@app.post("/generate")
async def generate_text(req: PromptRequest, response: Response):
if app_status["status"] != "ready":
response.status_code = status.HTTP_503_SERVICE_UNAVAILABLE
return {"error": "Model not ready", "status": app_status["status"]}
try:
inputs = tokenizer(req.prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=req.max_new_tokens,
do_sample=True,
temperature=0.8,
top_p=0.95,
)
generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
return {"generated_text": generated}
except Exception as e:
response.status_code = status.HTTP_500_INTERNAL_SERVER_ERROR
return {"error": str(e)}
@app.get("/")
async def root():
return {"message": "API is running", "status": app_status["status"]}
@app.get("/status")
async def get_status():
# Calculate uptime
uptime = time.time() - app_status["startup_time"]
return {
"status": app_status["status"],
"model_name": app_status["model_name"],
"model_loaded": app_status["model_loaded"],
"tokenizer_loaded": app_status["tokenizer_loaded"],
"uptime_seconds": uptime,
"errors": app_status["errors"]
} |