| import gradio as gr |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
| tokenizer = AutoTokenizer.from_pretrained("SattwikAyyagari/Qwen2.5-Coder-1.5B-NL-Java") |
| model = AutoModelForCausalLM.from_pretrained("SattwikAyyagari/Qwen2.5-Coder-1.5B-NL-Java") |
|
|
|
|
| def generate_code(nl_input: str): |
| inputs=tokenizer(f"### Instruction:\n\n{nl_input}\n\n### Response:\n\n",return_tensors="pt") |
| outputs=model.generate(**inputs,max_new_tokens=128,eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id) |
| decoded=tokenizer.decode(outputs[0],skip_special_tokens=True) |
| return decoded.split("### Response:\n\n")[-1].strip() |
|
|
| demo=gr.Interface( |
| fn=generate_code, |
| inputs=gr.Textbox(label="NL Description"), |
| outputs=gr.Textbox(label="Generated JAVA Code"), |
| title="CodeGen NL to Java" |
| ) |
|
|
| demo.launch() |
