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from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
app = FastAPI()
# Load model and tokenizer once on startup
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codet5p-220m")
model = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/codet5p-220m")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
class GenerationRequest(BaseModel):
prompt: str
max_length: int = 2048
num_beams: int = 3
early_stopping: bool = True
no_repeat_ngram_size: int = 3
@app.post("/generate")
async def generate_text(request: GenerationRequest):
inputs = tokenizer(request.prompt, return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
max_length=request.max_length,
num_beams=request.num_beams,
early_stopping=request.early_stopping,
no_repeat_ngram_size=request.no_repeat_ngram_size,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return {"generated_text": output_text}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8080)
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