from fastapi import FastAPI
from pydantic import BaseModel
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import uvicorn

class CodeRequest(BaseModel):
    prompt: str

app = FastAPI()

model = GPT2LMHeadModel.from_pretrained('./codegen_model')
tokenizer = GPT2Tokenizer.from_pretrained('./codegen_model')

@app.post("/generate-code/")
def generate_code(request: CodeRequest):
    inputs = tokenizer.encode(request.prompt, return_tensors='pt')
    outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"generated_code": generated_code}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)