app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "Lucid-research/lucentcode-1-py"  # Change this to your model repo ID

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID)

def format_prompt(user_input):
    return f"### Instruction:\n{user_input}\n\n### Output:\n"

def generate_code(user_input):
    prompt = format_prompt(user_input)
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_length=1000,
        temperature=0.7,
        do_sample=True,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id,
    )
    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    # Return only the generated part after "### Output:"
    return text.split("### Output:")[-1].strip()

iface = gr.Interface(
    fn=generate_code,
    inputs=gr.Textbox(lines=4, label="Instruction"),
    outputs=gr.Textbox(lines=8, label="Generated Output"),
    title="Python Generation With LucentCode-1-py",
    description="Enter an instruction and get a generated Python function.",
)

if __name__ == "__main__":
    iface.launch()