Initial CADCoder Space with Gradio

app.py

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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()
+

requirements.txt

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+torch
+transformers
+gradio
+git+https://github.com/huggingface/accelerate.git
+