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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load CAD-Coder model from Hugging Face
MODEL_NAME = "CADCODER/CAD-Coder"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
trust_remote_code=True,
torch_dtype=torch.float16,
device_map="auto"
)
def generate_code(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Gradio UI
demo = gr.Interface(
fn=generate_code,
inputs=gr.Textbox(lines=5, placeholder="Enter your CAD design prompt..."),
outputs="text",
title="CAD-Coder Inference",
description="Generate CAD code from natural language using CAD-Coder."
)
if __name__ == "__main__":
demo.launch()
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