| | 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) |
