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from fastapi import FastAPI |
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from pydantic import BaseModel |
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from transformers import GPT2LMHeadModel, GPT2Tokenizer |
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import uvicorn |
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class CodeRequest(BaseModel): |
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prompt: str |
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app = FastAPI() |
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model = GPT2LMHeadModel.from_pretrained('./codegen_model') |
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tokenizer = GPT2Tokenizer.from_pretrained('./codegen_model') |
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@app.post("/generate-code/") |
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def generate_code(request: CodeRequest): |
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inputs = tokenizer.encode(request.prompt, return_tensors='pt') |
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outputs = model.generate(inputs, max_length=150, num_return_sequences=1) |
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generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return {"generated_code": generated_code} |
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if __name__ == "__main__": |
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uvicorn.run(app, host="0.0.0.0", port=8000) |
