| 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) |
