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README.md

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 ---
-title: CQML
-emoji: 📚
-colorFrom: purple
-colorTo: indigo
+title: GLM-4.5V CAD Generator
+emoji: 🔧
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 5.43.1
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 license: mit
+hardware: zero-gpu
+python_version: 3.11
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🔧 GLM-4.5V CAD Generator
+
+Generate CADQuery Python code from 3D CAD model images using GLM-4.5V vision-language models!
+
+## Features
+
+- **Multiple GLM-4.5V variants**: AWQ, FP8, and full precision models
+- **Advanced prompting**: Simple, detailed, and chain-of-thought styles  
+- **Real-time generation**: GPU-accelerated inference on Zero GPU
+- **Code extraction**: Automatic cleaning and formatting of generated CADQuery code
+- **Evaluation ready**: Compatible with existing VSR and IoU metrics
+
+## Usage
+
+1. Upload a clear 3D CAD model image
+2. Select your preferred GLM-4.5V model variant
+3. Choose a prompting style 
+4. Click "Generate CADQuery Code"
+5. Copy the generated Python code and run it locally
+
+## Models
+
+- **GLM-4.5V-AWQ**: Fastest startup, good quality
+- **GLM-4.5V-FP8**: Balanced speed and quality
+- **GLM-4.5V**: Best quality, slower startup
+
+## Tips
+
+- Use clear, well-lit CAD images for best results
+- Simple geometric shapes work better than complex assemblies
+- Try different prompt styles if first attempt isn't satisfactory
+- Generated code may need minor adjustments for complex geometries
+
+## Technical Details
+
+This app uses Hugging Face Zero GPU (H200 GPUs, 70GB VRAM) for serverless GPU allocation and the latest GLM-4.5V vision-language models for image-to-code generation. Built with Gradio 5.42.0 for optimal 2025 compatibility and enhanced authentication support. The generated CADQuery code follows standard patterns with proper imports and result variables.

app.py

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+import spaces
+import gradio as gr
+import torch
+from transformers import pipeline
+from PIL import Image
+import time
+import traceback
+
+# Global model storage for Zero GPU compatibility
+models = {}
+
+@spaces.GPU(duration=300)
+def load_model_on_gpu(model_choice):
+    """Load GLM model on GPU - separated for clarity."""
+    model_map = {
+        "GLM-4.5V-AWQ": "QuantTrio/GLM-4.5V-AWQ",
+        "GLM-4.5V-FP8": "zai-org/GLM-4.5V-FP8", 
+        "GLM-4.5V": "zai-org/GLM-4.5V"
+    }
+    
+    model_name = model_map.get(model_choice)
+    if not model_name:
+        return False, f"Unknown model: {model_choice}"
+    
+    if model_name in models:
+        return True, f"✅ {model_choice} already loaded"
+    
+    try:
+        pipe = pipeline(
+            "image-text-to-text",
+            model=model_name,
+            device_map="auto",
+            torch_dtype=torch.float16,
+            trust_remote_code=True
+        )
+        models[model_name] = pipe
+        return True, f"✅ {model_choice} loaded successfully"
+    except Exception as e:
+        return False, f"❌ Failed to load {model_choice}: {str(e)[:200]}"
+
+@spaces.GPU(duration=120)
+def generate_code(image, model_choice, prompt_style):
+    """Generate CADQuery code - main GPU function."""
+    if image is None:
+        return "❌ Please upload an image first."
+    
+    # Create prompts
+    prompts = {
+        "Simple": "Generate CADQuery Python code for this 3D model:",
+        "Detailed": "Analyze this 3D CAD model and generate Python CADQuery code.\n\nRequirements:\n- Import cadquery as cq\n- Store result in 'result' variable\n- Use proper CADQuery syntax\n\nCode:",
+        "Chain-of-Thought": "Analyze this 3D CAD model step by step:\n\nStep 1: Identify the basic geometry\nStep 2: Note any features\nStep 3: Generate clean CADQuery Python code\n\n```python\nimport cadquery as cq\n\n# Generated code:"
+    }
+    
+    try:
+        # Load model if needed
+        model_map = {
+            "GLM-4.5V-AWQ": "QuantTrio/GLM-4.5V-AWQ",
+            "GLM-4.5V-FP8": "zai-org/GLM-4.5V-FP8",
+            "GLM-4.5V": "zai-org/GLM-4.5V"
+        }
+        
+        model_name = model_map[model_choice]
+        
+        if model_name not in models:
+            pipe = pipeline(
+                "image-text-to-text",
+                model=model_name,
+                device_map="auto", 
+                torch_dtype=torch.float16,
+                trust_remote_code=True
+            )
+            models[model_name] = pipe
+        else:
+            pipe = models[model_name]
+        
+        # Generate
+        messages = [{
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": prompts[prompt_style]}
+            ]
+        }]
+        
+        result = pipe(messages, max_new_tokens=512, temperature=0.7)
+        
+        if isinstance(result, list) and len(result) > 0:
+            generated_text = result[0].get("generated_text", str(result))
+        else:
+            generated_text = str(result)
+        
+        # Simple code extraction
+        code = generated_text.strip()
+        if "```python" in code:
+            start = code.find("```python") + 9
+            end = code.find("```", start)
+            if end > start:
+                code = code[start:end].strip()
+        
+        if "import cadquery" not in code:
+            code = "import cadquery as cq\n\n" + code
+        
+        return f"""## 🎯 Generated CADQuery Code
+
+```python
+{code}
+```
+
+## 📊 Info
+- **Model**: {model_choice}
+- **Prompt**: {prompt_style}
+- **Device**: {"GPU" if torch.cuda.is_available() else "CPU"}
+
+## 🔧 Usage
+```bash
+pip install cadquery
+python your_script.py
+```
+"""
+        
+    except Exception as e:
+        return f"❌ **Generation Failed**: {str(e)[:500]}"
+
+def test_model(model_choice):
+    """Test model loading."""
+    success, message = load_model_on_gpu(model_choice)
+    return f"## Test Result\n\n{message}"
+
+def system_info():
+    """Get system info."""
+    info = f"""## 🖥️ System Information
+
+- **CUDA Available**: {torch.cuda.is_available()}
+- **CUDA Devices**: {torch.cuda.device_count() if torch.cuda.is_available() else 0}
+- **PyTorch Version**: {torch.__version__}
+- **Device**: {"GPU" if torch.cuda.is_available() else "CPU"}
+"""
+    return info
+
+# Create interface
+with gr.Blocks(title="GLM-4.5V CAD Generator", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🔧 GLM-4.5V CAD Generator
+    
+    Generate CADQuery Python code from 3D CAD model images using GLM-4.5V models!
+    
+    **Models**: GLM-4.5V-AWQ (fastest) | GLM-4.5V-FP8 (balanced) | GLM-4.5V (best quality)
+    """)
+    
+    with gr.Tab("🚀 Generate"):
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(type="pil", label="Upload CAD Model Image")
+                model_choice = gr.Dropdown(
+                    choices=["GLM-4.5V-AWQ", "GLM-4.5V-FP8", "GLM-4.5V"],
+                    value="GLM-4.5V-AWQ",
+                    label="Select Model"
+                )
+                prompt_style = gr.Dropdown(
+                    choices=["Simple", "Detailed", "Chain-of-Thought"],
+                    value="Chain-of-Thought", 
+                    label="Prompt Style"
+                )
+                generate_btn = gr.Button("🚀 Generate CADQuery Code", variant="primary")
+            
+            with gr.Column():
+                output = gr.Markdown("Upload an image and click Generate!")
+        
+        generate_btn.click(
+            fn=generate_code,
+            inputs=[image_input, model_choice, prompt_style],
+            outputs=output
+        )
+    
+    with gr.Tab("🧪 Test"):
+        with gr.Row():
+            with gr.Column():
+                test_model_choice = gr.Dropdown(
+                    choices=["GLM-4.5V-AWQ", "GLM-4.5V-FP8", "GLM-4.5V"],
+                    value="GLM-4.5V-AWQ",
+                    label="Model to Test"
+                )
+                test_btn = gr.Button("🧪 Test Model")
+            
+            with gr.Column():
+                test_output = gr.Markdown("Click Test Model to check loading.")
+        
+        test_btn.click(fn=test_model, inputs=test_model_choice, outputs=test_output)
+    
+    with gr.Tab("⚙️ System"):
+        info_display = gr.Markdown()
+        refresh_btn = gr.Button("🔄 Refresh")
+        
+        demo.load(fn=system_info, outputs=info_display)
+        refresh_btn.click(fn=system_info, outputs=info_display)
+
+if __name__ == "__main__":
+    print("🚀 Starting GLM-4.5V CAD Generator...")
+    print(f"CUDA available: {torch.cuda.is_available()}")
+    demo.launch(share=True, show_error=True)

requirements.txt

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+gradio==4.44.1
+torch==2.1.2
+transformers==4.37.0
+accelerate==0.25.0
+pillow==10.0.0
+spaces