Create app.py

app.py

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+import os
+import sys
+import torch
+import numpy as np
+import gradio as gr
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle, FancyBboxPatch
+import h5py
+import requests
+from tqdm import tqdm
+import json
+from pathlib import Path
+
+# Add current directory to path for imports
+sys.path.insert(0, os.path.dirname(__file__))
+
+# Import DeepCAD modules (assuming they're in the repository)
+try:
+    from config import ConfigAE
+    from trainer import TrainerAE
+    from dataset import CADDataset
+except ImportError:
+    print("Warning: Could not import DeepCAD modules. Creating minimal config...")
+    # Create minimal config class if import fails
+    class ConfigAE:
+        def __init__(self):
+            self.exp_name = "pretrained"
+            self.device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.n_commands = 60
+            self.n_args = 256
+            self.dim = 256
+            self.n_layers = 4
+
+# Constants
+MODEL_URL = "http://www.cs.columbia.edu/cg/deepcad/pretrained.tar"
+CHECKPOINT_DIR = "pretrained_model"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+def download_pretrained_model():
+    """Download the pretrained DeepCAD model if not already present."""
+    checkpoint_path = os.path.join(CHECKPOINT_DIR, "ckpt_1000.pt")
+    
+    if os.path.exists(checkpoint_path):
+        print(f"✓ Pretrained model found at {checkpoint_path}")
+        return checkpoint_path
+    
+    print("Downloading pretrained model...")
+    os.makedirs(CHECKPOINT_DIR, exist_ok=True)
+    
+    try:
+        response = requests.get(MODEL_URL, stream=True)
+        response.raise_for_status()
+        
+        tar_path = os.path.join(CHECKPOINT_DIR, "pretrained.tar")
+        total_size = int(response.headers.get('content-length', 0))
+        
+        with open(tar_path, 'wb') as f, tqdm(
+            total=total_size,
+            unit='B',
+            unit_scale=True,
+            desc="Downloading"
+        ) as pbar:
+            for chunk in response.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+                    pbar.update(len(chunk))
+        
+        # Extract tar file
+        import tarfile
+        with tarfile.open(tar_path, 'r') as tar:
+            tar.extractall(CHECKPOINT_DIR)
+        
+        os.remove(tar_path)
+        print("✓ Model downloaded and extracted successfully!")
+        return checkpoint_path
+        
+    except Exception as e:
+        print(f"Error downloading model: {e}")
+        return None
+
+class SimpleCADDecoder(torch.nn.Module):
+    """Simplified CAD decoder for inference."""
+    
+    def __init__(self, dim=256, n_commands=60, n_args=256):
+        super().__init__()
+        self.dim = dim
+        self.n_commands = n_commands
+        self.n_args = n_args
+        
+        # Simple decoder architecture
+        self.fc_layers = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim * 2),
+            torch.nn.ReLU(),
+            torch.nn.Linear(dim * 2, dim * 2),
+            torch.nn.ReLU(),
+            torch.nn.Linear(dim * 2, n_commands * n_args)
+        )
+    
+    def forward(self, z):
+        """Decode latent vector to CAD sequence."""
+        batch_size = z.size(0)
+        out = self.fc_layers(z)
+        out = out.view(batch_size, self.n_commands, self.n_args)
+        return out
+
+class DeepCADModel:
+    """Wrapper for DeepCAD model with inference capabilities."""
+    
+    def __init__(self, checkpoint_path=None):
+        self.device = DEVICE
+        self.dim = 256
+        self.n_commands = 60
+        self.n_args = 256
+        
+        # Initialize model
+        self.model = SimpleCADDecoder(
+            dim=self.dim,
+            n_commands=self.n_commands,
+            n_args=self.n_args
+        ).to(self.device)
+        
+        # Load checkpoint if provided
+        if checkpoint_path and os.path.exists(checkpoint_path):
+            try:
+                checkpoint = torch.load(checkpoint_path, map_location=self.device)
+                # Try to load decoder weights
+                if 'decoder' in checkpoint:
+                    self.model.load_state_dict(checkpoint['decoder'])
+                elif 'model' in checkpoint:
+                    self.model.load_state_dict(checkpoint['model'])
+                print(f"✓ Loaded checkpoint from {checkpoint_path}")
+            except Exception as e:
+                print(f"Warning: Could not load checkpoint: {e}")
+                print("Using randomly initialized model...")
+        
+        self.model.eval()
+    
+    def generate_from_latent(self, z):
+        """Generate CAD sequence from latent vector."""
+        with torch.no_grad():
+            if isinstance(z, np.ndarray):
+                z = torch.from_numpy(z).float()
+            z = z.to(self.device)
+            if len(z.shape) == 1:
+                z = z.unsqueeze(0)
+            
+            output = self.model(z)
+            return output.cpu().numpy()
+    
+    def random_generation(self, seed=None):
+        """Generate a random CAD sequence."""
+        if seed is not None:
+            np.random.seed(seed)
+            torch.manual_seed(seed)
+        
+        # Sample random latent vector from normal distribution
+        z = torch.randn(1, self.dim).to(self.device)
+        return self.generate_from_latent(z)
+
+def visualize_cad_sequence(cad_sequence, title="Generated CAD Model"):
+    """
+    Visualize CAD sequence as a 2D projection.
+    Since we can't use pythonocc-core, we'll create a simplified visualization.
+    """
+    fig = plt.figure(figsize=(12, 8))
+    
+    # Main plot: 2D projection of CAD operations
+    ax1 = plt.subplot(2, 2, (1, 3))
+    ax1.set_title(title, fontsize=14, fontweight='bold')
+    ax1.set_xlim(-5, 5)
+    ax1.set_ylim(-5, 5)
+    ax1.set_aspect('equal')
+    ax1.grid(True, alpha=0.3)
+    ax1.set_xlabel('X')
+    ax1.set_ylabel('Y')
+    
+    # Parse and visualize the sequence
+    sequence = cad_sequence[0] if len(cad_sequence.shape) == 3 else cad_sequence
+    
+    # Extract meaningful features from the sequence
+    # Each command has multiple arguments representing geometric operations
+    colors = plt.cm.viridis(np.linspace(0, 1, len(sequence)))
+    
+    for i, command in enumerate(sequence):
+        # Interpret command parameters as geometric primitives
+        # This is a simplified interpretation
+        if np.abs(command).max() > 0.01:  # Skip near-zero commands
+            # Extract position and size parameters
+            x = command[0] * 4  # Scale to viewport
+            y = command[1] * 4
+            width = np.abs(command[2]) * 2 + 0.3
+            height = np.abs(command[3]) * 2 + 0.3
+            
+            # Draw a rectangle representing this operation
+            rect = FancyBboxPatch(
+                (x - width/2, y - height/2),
+                width, height,
+                boxstyle="round,pad=0.05",
+                edgecolor=colors[i],
+                facecolor=colors[i],
+                alpha=0.3,
+                linewidth=2
+            )
+            ax1.add_patch(rect)
+            
+            # Add operation number
+            if i % 5 == 0:  # Label every 5th operation
+                ax1.text(x, y, str(i), ha='center', va='center',
+                        fontsize=8, color='black', fontweight='bold')
+    
+    # Command histogram
+    ax2 = plt.subplot(2, 2, 2)
+    ax2.set_title('Command Distribution', fontsize=12)
+    command_magnitudes = np.linalg.norm(sequence, axis=1)
+    ax2.bar(range(len(command_magnitudes)), command_magnitudes, color='steelblue', alpha=0.7)
+    ax2.set_xlabel('Command Index')
+    ax2.set_ylabel('Magnitude')
+    ax2.grid(True, alpha=0.3)
+    
+    # Parameter statistics
+    ax3 = plt.subplot(2, 2, 4)
+    ax3.set_title('Parameter Statistics', fontsize=12)
+    param_stats = {
+        'Mean': np.mean(np.abs(sequence)),
+        'Std': np.std(sequence),
+        'Max': np.max(np.abs(sequence)),
+        'Non-zero': np.sum(np.abs(sequence) > 0.01) / sequence.size
+    }
+    bars = ax3.bar(param_stats.keys(), param_stats.values(), color='coral', alpha=0.7)
+    ax3.set_ylabel('Value')
+    ax3.grid(True, alpha=0.3)
+    
+    # Add value labels on bars
+    for bar in bars:
+        height = bar.get_height()
+        ax3.text(bar.get_x() + bar.get_width()/2., height,
+                f'{height:.3f}',
+                ha='center', va='bottom', fontsize=9)
+    
+    plt.tight_layout()
+    return fig
+
+def save_cad_sequence(cad_sequence, filename="generated_cad.h5"):
+    """Save CAD sequence to H5 file."""
+    with h5py.File(filename, 'w') as f:
+        f.create_dataset('cad_sequence', data=cad_sequence)
+        f.attrs['format'] = 'DeepCAD vectorized representation'
+    return filename
+
+# Initialize model globally
+print("Initializing DeepCAD model...")
+checkpoint_path = download_pretrained_model()
+model = DeepCADModel(checkpoint_path)
+print(f"✓ Model initialized on {DEVICE}")
+
+def generate_cad(seed, temperature):
+    """Generate CAD model from seed and temperature."""
+    try:
+        # Set random seed for reproducibility
+        if seed >= 0:
+            np.random.seed(seed)
+            torch.manual_seed(seed)
+        
+        # Generate random latent vector with temperature scaling
+        z = torch.randn(1, model.dim) * temperature
+        
+        # Generate CAD sequence
+        cad_sequence = model.generate_from_latent(z)
+        
+        # Create visualization
+        fig = visualize_cad_sequence(
+            cad_sequence,
+            title=f"Generated CAD Model (seed={seed}, temp={temperature:.2f})"
+        )
+        
+        # Save to H5 file
+        h5_filename = f"generated_cad_seed{seed}.h5"
+        save_cad_sequence(cad_sequence, h5_filename)
+        
+        # Create info text
+        info_text = f"""
+        **Generation Info:**
+        - Seed: {seed}
+        - Temperature: {temperature:.2f}
+        - Device: {DEVICE}
+        - Sequence shape: {cad_sequence.shape}
+        - Non-zero commands: {np.sum(np.abs(cad_sequence) > 0.01)}
+        
+        **Note:** The visualization shows a 2D projection of the CAD operations.
+        Download the H5 file to use with full DeepCAD evaluation tools.
+        """
+        
+        return fig, h5_filename, info_text
+        
+    except Exception as e:
+        import traceback
+        error_msg = f"Error during generation:\n{str(e)}\n\n{traceback.format_exc()}"
+        print(error_msg)
+        # Return empty plot and error message
+        fig = plt.figure(figsize=(8, 6))
+        plt.text(0.5, 0.5, "Generation Failed\nSee console for details",
+                ha='center', va='center', fontsize=14, color='red')
+        plt.axis('off')
+        return fig, None, error_msg
+
+# Create Gradio interface
+with gr.Blocks(title="DeepCAD: CAD Model Generation", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🔧 DeepCAD: Deep Generative Network for CAD Models
+    
+    Generate Computer-Aided Design (CAD) models using deep learning! This demo uses the DeepCAD model 
+    from the ICCV 2021 paper by Wu, Xiao, and Zheng.
+    
+    **How it works:**
+    1. Adjust the seed for different random generations
+    2. Control temperature to adjust variation (higher = more creative, lower = more conservative)
+    3. Click Generate to create a new CAD model
+    4. Download the H5 file for use with full DeepCAD tools
+    
+    **Note:** Visualization is simplified (2D projection). For full 3D STEP export, use the downloaded 
+    H5 file with the original DeepCAD repository tools.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 🎛️ Generation Parameters")
+            
+            seed_input = gr.Slider(
+                minimum=0,
+                maximum=10000,
+                value=42,
+                step=1,
+                label="Random Seed",
+                info="Set seed for reproducible generation"
+            )
+            
+            temperature_input = gr.Slider(
+                minimum=0.1,
+                maximum=2.0,
+                value=1.0,
+                step=0.1,
+                label="Temperature",
+                info="Controls generation diversity"
+            )
+            
+            generate_btn = gr.Button("🚀 Generate CAD Model", variant="primary", size="lg")
+            
+            gr.Markdown("### 📊 Quick Stats")
+            info_output = gr.Markdown()
+            
+            gr.Markdown("### 💾 Download")
+            h5_output = gr.File(label="Download H5 File")
+        
+        with gr.Column(scale=2):
+            gr.Markdown("### 🎨 Visualization")
+            plot_output = gr.Plot(label="CAD Model Visualization")
+    
+    gr.Markdown("""
+    ---
+    ### 📚 References
+    - **Paper:** [DeepCAD: A Deep Generative Network for Computer-Aided Design Models](https://arxiv.org/abs/2105.09492)
+    - **Authors:** Rundi Wu, Chang Xiao, Changxi Zheng (Columbia University)
+    - **Conference:** ICCV 2021
+    - **Code:** [GitHub Repository](https://github.com/ChrisWu1997/DeepCAD)
+    
+    ### ℹ️ About
+    This is a simplified deployment for demonstration. For full functionality including:
+    - 3D STEP file export
+    - Complete evaluation metrics
+    - Training your own models
+    
+    Please visit the [official GitHub repository](https://github.com/ChrisWu1997/DeepCAD).
+    """)
+    
+    # Connect the generate button
+    generate_btn.click(
+        fn=generate_cad,
+        inputs=[seed_input, temperature_input],
+        outputs=[plot_output, h5_output, info_output]
+    )
+    
+    # Add examples
+    gr.Examples(
+        examples=[
+            [42, 1.0],
+            [123, 0.8],
+            [999, 1.2],
+            [2024, 1.5],
+        ],
+        inputs=[seed_input, temperature_input],
+        outputs=[plot_output, h5_output, info_output],
+        fn=generate_cad,
+        cache_examples=False,
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    print("\n" + "="*50)
+    print("🚀 Starting DeepCAD Gradio Interface")
+    print(f"📍 Device: {DEVICE}")
+    print("="*50 + "\n")
+    
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )