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from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, T5ForConditionalGeneration, AutoConfig
import torch
app = FastAPI(title="CodeT5+ Backend on HuggingFace")
# ==== LOAD MODEL ====
base_ckpt = "Salesforce/codet5p-770m"
finetuned_ckpt = "OSS-forge/codet5p-770m-pyresbugs"
print("Loading tokenizer + config...")
tokenizer = AutoTokenizer.from_pretrained(base_ckpt)
config = AutoConfig.from_pretrained(base_ckpt)
print("Loading fine-tuned model weights...")
model = T5ForConditionalGeneration.from_pretrained(
finetuned_ckpt,
config=config
)
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Running on:", device)
model = model.to(device)
model.eval()
# ==== REQUEST / RESPONSE MODELS ====
class GenerateRequest(BaseModel):
prompt: str
language: str | None = "Python"
task: str = "generate"
max_new_tokens: int = 128
num_beams: int = 4
temperature: float = 0.7
class GenerateResponse(BaseModel):
output: str
def build_prompt(req: GenerateRequest):
if req.task == "generate":
return f"Generate {req.language} code:\n{req.prompt}"
elif req.task == "fix":
return f"Fix the bug in the following {req.language} code:\n{req.prompt}\n\nCorrected code:"
else:
return req.prompt
@app.post("/generate", response_model=GenerateResponse)
def generate(req: GenerateRequest):
prompt = build_prompt(req)
inputs = tokenizer(prompt, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=req.max_new_tokens,
num_beams=req.num_beams,
temperature=req.temperature,
early_stopping=True
)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return GenerateResponse(output=text)
@app.get("/")
def root():
return {"status": "CodeT5+ backend is running 🚀"}
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