Add CADFusion Gradio demo

app.py

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+# app.py
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+MODEL_ID = "microsoft/CADFusion"
+
+def load_model():
+    print("Loading tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
+    print("Trying to load model in 4-bit (bitsandbytes)...")
+    try:
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_compute_dtype=torch.float16,
+        )
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID,
+            quantization_config=bnb_config,
+            device_map="auto",
+            trust_remote_code=True,
+        )
+        print("Loaded in 4-bit")
+    except Exception as e:
+        print("4-bit load failed:", e)
+        print("Falling back to fp16 (may require larger GPU RAM)...")
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID,
+            device_map="auto",
+            torch_dtype=torch.float16,
+            trust_remote_code=True,
+        )
+
+    model.eval()
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+def generate(prompt, max_new_tokens=256):
+    if prompt is None or prompt.strip() == "":
+        return "Please provide a text description of the CAD model."
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        out = model.generate(**inputs, max_new_tokens=int(max_new_tokens), do_sample=False)
+    text = tokenizer.decode(out[0], skip_special_tokens=True)
+    return text
+
+with gr.Blocks() as demo:
+    gr.Markdown("# CADFusion demo (microsoft/CADFusion)\nEnter a design description and hit Generate.")
+    with gr.Row():
+        prompt = gr.Textbox(lines=5, placeholder="e.g. 'a coffee mug with cylindrical body and curved handle'")
+        tokens = gr.Slider(64, 1024, value=256, label="max_new_tokens")
+    out = gr.Textbox(lines=20)
+    btn = gr.Button("Generate")
+    btn.click(fn=generate, inputs=[prompt, tokens], outputs=out)
+
+if __name__ == "__main__":
+    demo.launch()
+

requirements.txt

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+gradio>=3.30
+transformers>=4.32
+bitsandbytes
+torch
+accelerate
+safetensors
+sentencepiece
+huggingface-hub
+

spaces-config.json

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+{
+  "sdk": "gradio",
+  "app_file": "app.py",
+  "suggested_hardware": "gpu",
+  "suggested_storage": "large"
+}
+