app.py

"""
Stereo Matching Methods Comparison Demo (Hugging Face Spaces with ZeroGPU)

This demo compares different stereo matching algorithms using Gradio's ImageSlider.
Optimized for Hugging Face Spaces with ZeroGPU support.

Currently supports:
- FoundationStereo (Low-cost and High-quality variants)
- CREStereo (ETH3D pre-trained model)
"""

import os
import sys
import logging
import gc
import tempfile
from pathlib import Path
from typing import Optional, Tuple, Union, Dict, List
import numpy as np
import cv2
import gradio as gr
import imageio
import argparse
import random

# Import spaces BEFORE torch to ensure proper ZeroGPU initialization
import spaces

import torch
import torch.nn.functional as F

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Get current directory
current_dir = os.path.dirname(os.path.abspath(__file__))

# Add subdemo directories to path
foundation_stereo_dir = os.path.join(current_dir, "FoundationStereo_demo")
crestereo_dir = os.path.join(current_dir, "CREStereo_demo")
sys.path.insert(0, foundation_stereo_dir)
sys.path.insert(0, crestereo_dir)

# Global variables for model caching
_cached_models = {}
_available_methods = {}

class StereoMethodBase:
    """Base class for stereo matching methods"""
    
    def __init__(self, name: str, display_name: str):
        self.name = name
        self.display_name = display_name
        self._model = None
        self._device = None
    
    def load_model(self):
        """Load the model for this method"""
        raise NotImplementedError
    
    def process_stereo_pair(self, left_img: np.ndarray, right_img: np.ndarray, progress_callback=None) -> Tuple[np.ndarray, str]:
        """Process stereo pair and return disparity visualization and status"""
        raise NotImplementedError
    
    def cleanup(self):
        """Clean up model and free memory"""
        if self._model is not None:
            del self._model
            self._model = None
            self._device = None
            torch.cuda.empty_cache()
            gc.collect()


class FoundationStereoMethod(StereoMethodBase):
    """FoundationStereo implementation"""
    
    def __init__(self, variant: str = "11-33-40"):
        display_name = f"FoundationStereo ({variant})"
        super().__init__(f"foundation_stereo_{variant}", display_name)
        self.variant = variant
        
    def load_model(self):
        """Load FoundationStereo model"""
        try:
            # Import FoundationStereo modules
            from FoundationStereo_demo.app_local import get_cached_model, get_available_models
            
            # Get available models
            available_models = get_available_models()
            
            # Find the appropriate model selection
            model_selection = None
            for model_name in available_models.keys():
                if self.variant in model_name:
                    model_selection = model_name
                    break
            
            if model_selection is None:
                # Fallback to first available model
                model_selection = list(available_models.keys())[0] if available_models else None
            
            if model_selection is None:
                raise ValueError("No FoundationStereo models available")
            
            self._model, self._device = get_cached_model(model_selection)
            logging.info(f"✅ FoundationStereo {self.variant} loaded successfully")
            return True
            
        except Exception as e:
            logging.error(f"Failed to load FoundationStereo {self.variant}: {e}")
            return False
    
    def process_stereo_pair(self, left_img: np.ndarray, right_img: np.ndarray, progress_callback=None) -> Tuple[np.ndarray, str]:
        """Process stereo pair using FoundationStereo"""
        try:
            from FoundationStereo_demo.app_local import process_stereo_pair
            
            # Save images temporarily
            with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as left_tmp:
                cv2.imwrite(left_tmp.name, cv2.cvtColor(left_img, cv2.COLOR_RGB2BGR))
                left_path = left_tmp.name
            
            with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as right_tmp:
                cv2.imwrite(right_tmp.name, cv2.cvtColor(right_img, cv2.COLOR_RGB2BGR))
                right_path = right_tmp.name
            
            try:
                # Find the model selection
                from FoundationStereo_demo.app_local import get_available_models
                available_models = get_available_models()
                model_selection = None
                for model_name in available_models.keys():
                    if self.variant in model_name:
                        model_selection = model_name
                        break
                
                if model_selection is None:
                    model_selection = list(available_models.keys())[0]
                
                # Process the stereo pair
                result_img, status = process_stereo_pair(model_selection, left_path, right_path)
                
                if result_img is not None:
                    return result_img, f"✅ {self.display_name}: {status}"
                else:
                    return None, f"❌ {self.display_name}: Processing failed"
                    
            finally:
                # Clean up temporary files
                if os.path.exists(left_path):
                    os.unlink(left_path)
                if os.path.exists(right_path):
                    os.unlink(right_path)
                    
        except Exception as e:
            logging.error(f"FoundationStereo processing failed: {e}")
            return None, f"❌ {self.display_name}: {str(e)}"


class CREStereoMethod(StereoMethodBase):
    """CREStereo implementation"""
    
    def __init__(self):
        super().__init__("crestereo", "CREStereo (ETH3D)")
        
    def load_model(self):
        """Load CREStereo model"""
        try:
            from CREStereo_demo.app_local import get_cached_model, get_available_models
            
            # Get available models
            available_models = get_available_models()
            
            if not available_models:
                raise ValueError("No CREStereo models available")
            
            # Use the first available model
            model_selection = list(available_models.keys())[0]
            self._model, self._device = get_cached_model(model_selection)
            logging.info("✅ CREStereo loaded successfully")
            return True
            
        except Exception as e:
            logging.error(f"Failed to load CREStereo: {e}")
            return False
    
    def process_stereo_pair(self, left_img: np.ndarray, right_img: np.ndarray, progress_callback=None) -> Tuple[np.ndarray, str]:
        """Process stereo pair using CREStereo"""
        try:
            from CREStereo_demo.app_local import process_stereo_pair
            
            # Save images temporarily
            with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as left_tmp:
                cv2.imwrite(left_tmp.name, cv2.cvtColor(left_img, cv2.COLOR_RGB2BGR))
                left_path = left_tmp.name
            
            with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as right_tmp:
                cv2.imwrite(right_tmp.name, cv2.cvtColor(right_img, cv2.COLOR_RGB2BGR))
                right_path = right_tmp.name
            
            try:
                # Find the model selection
                from CREStereo_demo.app_local import get_available_models
                available_models = get_available_models()
                model_selection = list(available_models.keys())[0]
                
                # Process the stereo pair
                result_img, status = process_stereo_pair(model_selection, left_path, right_path)
                
                if result_img is not None:
                    return result_img, f"✅ {self.display_name}: {status}"
                else:
                    return None, f"❌ {self.display_name}: Processing failed"
                    
            finally:
                # Clean up temporary files
                if os.path.exists(left_path):
                    os.unlink(left_path)
                if os.path.exists(right_path):
                    os.unlink(right_path)
                    
        except Exception as e:
            logging.error(f"CREStereo processing failed: {e}")
            return None, f"❌ {self.display_name}: {str(e)}"


def initialize_methods() -> Dict[str, StereoMethodBase]:
    """Initialize available stereo matching methods"""
    methods = {}
    
    # Initialize FoundationStereo variants
    for variant in ["11-33-40", "23-51-11"]:
        method = FoundationStereoMethod(variant)
        methods[method.name] = method
    
    # Initialize CREStereo
    crestereo_method = CREStereoMethod()
    methods[crestereo_method.name] = crestereo_method
    
    return methods


def load_example_images() -> List[Tuple[str, str, str]]:
    """Load example stereo pairs"""
    examples = []
    assets_dir = os.path.join(current_dir, "assets")
    
    if os.path.exists(assets_dir):
        for example_dir in os.listdir(assets_dir):
            example_path = os.path.join(assets_dir, example_dir)
            if os.path.isdir(example_path):
                left_path = os.path.join(example_path, "left.png")
                right_path = os.path.join(example_path, "right.png")
                
                if os.path.exists(left_path) and os.path.exists(right_path):
                    examples.append((left_path, right_path, example_dir))
    
    return examples


@spaces.GPU(duration=120)  # 2 minutes for comparison processing
def compare_methods(left_image: np.ndarray, right_image: np.ndarray, 
                   method1_name: str, method2_name: str,
                   progress: gr.Progress = gr.Progress()) -> Tuple[Optional[np.ndarray], str]:
    """Compare two stereo matching methods"""
    
    if left_image is None or right_image is None:
        return None, "❌ Please upload both left and right images."
    
    if method1_name == method2_name:
        return None, "❌ Please select two different methods for comparison."
    
    # Get methods
    methods = initialize_methods()
    method1 = methods.get(method1_name)
    method2 = methods.get(method2_name)
    
    if method1 is None or method2 is None:
        return None, "❌ Selected methods not available."
    
    progress(0.1, desc=f"Loading {method1.display_name}...")
    
    # Load method 1
    if not method1.load_model():
        return None, f"❌ Failed to load {method1.display_name}"
    
    progress(0.2, desc=f"Processing with {method1.display_name}...")
    
    # Process with method 1
    result1, status1 = method1.process_stereo_pair(left_image, right_image)
    
    progress(0.5, desc=f"Loading {method2.display_name}...")
    
    # Load method 2
    if not method2.load_model():
        method1.cleanup()
        return None, f"❌ Failed to load {method2.display_name}"
    
    progress(0.7, desc=f"Processing with {method2.display_name}...")
    
    # Process with method 2
    result2, status2 = method2.process_stereo_pair(left_image, right_image)
    
    progress(0.9, desc="Creating comparison...")
    
    if result1 is None or result2 is None:
        method1.cleanup()
        method2.cleanup()
        return None, "❌ One or both methods failed to process the images."
    
    # Create side-by-side comparison
    comparison_img = create_comparison_image(result1, result2, method1.display_name, method2.display_name)
    
    # Clean up
    method1.cleanup()
    method2.cleanup()
    
    progress(1.0, desc="Complete!")
    
    status = f"""🔍 **Comparison Results**

**{method1.display_name}:**
{status1}

**{method2.display_name}:**
{status2}

💡 **Tip:** Use the slider in the comparison image to switch between results."""
    
    return comparison_img, status


def create_comparison_image(img1: np.ndarray, img2: np.ndarray, label1: str, label2: str) -> np.ndarray:
    """Create a side-by-side comparison image with labels"""
    h, w = img1.shape[:2]
    
    # Create comparison canvas
    comparison = np.zeros((h + 60, w * 2 + 20, 3), dtype=np.uint8)
    comparison.fill(255)  # White background
    
    # Place images
    comparison[50:50+h, 10:10+w] = img1
    comparison[50:50+h, w+20:w*2+20] = img2
    
    # Add labels
    font = cv2.FONT_HERSHEY_SIMPLEX
    font_scale = 0.8
    font_thickness = 2
    
    # Method 1 label
    text_size1 = cv2.getTextSize(label1, font, font_scale, font_thickness)[0]
    text_x1 = 10 + (w - text_size1[0]) // 2
    cv2.putText(comparison, label1, (text_x1, 30), font, font_scale, (0, 0, 0), font_thickness)
    
    # Method 2 label
    text_size2 = cv2.getTextSize(label2, font, font_scale, font_thickness)[0]
    text_x2 = w + 20 + (w - text_size2[0]) // 2
    cv2.putText(comparison, label2, (text_x2, 30), font, font_scale, (0, 0, 0), font_thickness)
    
    return comparison


@spaces.GPU(duration=90)  # 1.5 minutes for single method processing
def single_method_inference(left_image: np.ndarray, right_image: np.ndarray, 
                           method_name: str,
                           progress: gr.Progress = gr.Progress()) -> Tuple[Optional[np.ndarray], str]:
    """Run inference with a single method"""
    
    if left_image is None or right_image is None:
        return None, "❌ Please upload both left and right images."
    
    methods = initialize_methods()
    method = methods.get(method_name)
    
    if method is None:
        return None, "❌ Selected method not available."
    
    progress(0.2, desc=f"Loading {method.display_name}...")
    
    if not method.load_model():
        return None, f"❌ Failed to load {method.display_name}"
    
    progress(0.5, desc=f"Processing with {method.display_name}...")
    
    result, status = method.process_stereo_pair(left_image, right_image)
    
    method.cleanup()
    
    progress(1.0, desc="Complete!")
    
    return result, status


@spaces.GPU(duration=120)  # 2 minutes for slider comparison
def create_slider_comparison(left_img, right_img, method1, method2, progress=gr.Progress()):
    """Create comparison for image slider"""
    if left_img is None or right_img is None:
        return None, "❌ Please upload both images."
    
    if method1 == method2:
        return None, "❌ Please select different methods."
    
    methods = initialize_methods()
    m1 = methods.get(method1)
    m2 = methods.get(method2)
    
    if m1 is None or m2 is None:
        return None, "❌ Methods not available."
    
    progress(0.1, desc=f"Processing with {m1.display_name}...")
    
    # Process with method 1
    if not m1.load_model():
        return None, f"❌ Failed to load {m1.display_name}"
    
    result1, status1 = m1.process_stereo_pair(left_img, right_img)
    
    progress(0.5, desc=f"Processing with {m2.display_name}...")
    
    # Process with method 2
    if not m2.load_model():
        m1.cleanup()
        return None, f"❌ Failed to load {m2.display_name}"
    
    result2, status2 = m2.process_stereo_pair(left_img, right_img)
    
    # Clean up
    m1.cleanup()
    m2.cleanup()
    
    progress(1.0, desc="Complete!")
    
    if result1 is None or result2 is None:
        return None, "❌ Processing failed."
    
    # Add method names to the top of the images for slider comparison
    def add_method_label(img: np.ndarray, method_name: str) -> np.ndarray:
        """Add method name label to the top of the image"""
        h, w = img.shape[:2]
        
        # Create new image with space for label
        labeled_img = np.zeros((h + 40, w, 3), dtype=np.uint8)
        labeled_img.fill(255)  # White background for label area
        
        # Place original image below the label area
        labeled_img[40:, :] = img
        
        # Add method name label
        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 0.7
        font_thickness = 2
        
        # Calculate text size and position
        text_size = cv2.getTextSize(method_name, font, font_scale, font_thickness)[0]
        text_x = (w - text_size[0]) // 2
        text_y = 28  # Position in the label area
        
        cv2.putText(labeled_img, method_name, (text_x, text_y), font, font_scale, (0, 0, 0), font_thickness)
        
        return labeled_img
    
    # Add labels to both images
    labeled_result1 = add_method_label(result1, m1.display_name)
    labeled_result2 = add_method_label(result2, m2.display_name)
    
    status = f"""🎚️ **Interactive Comparison Ready**
                    
**{m1.display_name}:** {status1.split(':')[-1].strip() if ':' in status1 else status1}
**{m2.display_name}:** {status2.split(':')[-1].strip() if ':' in status2 else status2}

💡 **Tip:** Drag the slider to compare the two methods interactively!"""
    
    return (labeled_result1, labeled_result2), status


def create_app() -> gr.Blocks:
    """Create the Gradio application"""
    
    # Load examples
    examples = load_example_images()
    
    # Get available methods
    methods = initialize_methods()
    method_choices = [(method.display_name, method.name) for method in methods.values()]
    
    with gr.Blocks(
        title="Stereo Matching Methods Comparison",
        theme=gr.themes.Soft(),
        css="footer {visibility: hidden}"
    ) as app:
        
        gr.Markdown("""
        # 🏆 Stereo Matching Methods Comparison
        
        Compare different stereo matching algorithms side-by-side using advanced deep learning models.
        
        **Available Methods:**
        - 🎯 **FoundationStereo** (Low-cost & High-quality variants) - Zero-shot stereo matching
        - ⚡ **CREStereo** - Practical stereo matching with high efficiency
        
        ⚠️ **Important**: Upload **rectified** stereo image pairs for best results.
        🚀 **Powered by ZeroGPU**: Automatic GPU allocation for fast processing!
        """)
        
        # Instructions section
        with gr.Accordion("📋 How to Use", open=False):
            gr.Markdown("""
            ### 🖼️ Input Requirements
            1. **Rectified stereo pairs**: Images should be epipolar-aligned (horizontal epipolar lines)
            2. **Same resolution**: Left and right images must have identical dimensions
            3. **Good quality**: Clear, well-lit images work best
            
            ### 🔍 Comparison Modes
            1. **Method Comparison**: Compare two different methods side-by-side
            2. **Single Method**: Test individual methods
            3. **Interactive Slider**: Use ImageSlider for easy comparison
            
            ### 📊 Example Images
            Try the provided example stereo pairs to see the differences between methods.
            
            ### 🚀 ZeroGPU Integration
            - Automatic GPU allocation when processing starts
            - Optimized memory management
            - Fast model loading and cleanup
            """)
        
        with gr.Tabs():
            # Tab 1: Method Comparison
            with gr.Tab("🔍 Method Comparison"):
                gr.Markdown("### Compare Two Stereo Matching Methods")
                
                with gr.Row():
                    with gr.Column():
                        left_img_comp = gr.Image(label="Left Image", type="numpy")
                        right_img_comp = gr.Image(label="Right Image", type="numpy")
                    
                    with gr.Column():
                        method1_dropdown = gr.Dropdown(
                            choices=method_choices,
                            label="Method 1",
                            value=method_choices[0][1] if method_choices else None
                        )
                        method2_dropdown = gr.Dropdown(
                            choices=method_choices,
                            label="Method 2", 
                            value=method_choices[1][1] if len(method_choices) > 1 else None
                        )
                        compare_btn = gr.Button("🚀 Compare Methods", variant="primary", size="lg")
                
                comparison_result = gr.Image(label="Comparison Result")
                comparison_status = gr.Markdown()
                
                compare_btn.click(
                    fn=compare_methods,
                    inputs=[left_img_comp, right_img_comp, method1_dropdown, method2_dropdown],
                    outputs=[comparison_result, comparison_status],
                    show_progress=True
                )
                
                # Examples for method comparison
                if examples:
                    example_inputs = []
                    for left_path, right_path, name in examples[:3]:
                        # Load images as numpy arrays
                        left_img = cv2.imread(left_path)
                        right_img = cv2.imread(right_path)
                        if left_img is not None:
                            left_img = cv2.cvtColor(left_img, cv2.COLOR_BGR2RGB)
                        if right_img is not None:
                            right_img = cv2.cvtColor(right_img, cv2.COLOR_BGR2RGB)
                        example_inputs.append([left_img, right_img])
                    
                    gr.Examples(
                        examples=example_inputs,
                        inputs=[left_img_comp, right_img_comp],
                        label="📸 Example Stereo Pairs",
                        examples_per_page=3
                    )
            
            # Tab 2: Interactive Slider Comparison
            with gr.Tab("🎚️ Interactive Comparison"):
                gr.Markdown("### Interactive Method Comparison with Slider")
                
                with gr.Row():
                    with gr.Column():
                        left_img_slider = gr.Image(label="Left Image", type="numpy")
                        right_img_slider = gr.Image(label="Right Image", type="numpy")
                    
                    with gr.Column():
                        method1_slider = gr.Dropdown(
                            choices=method_choices,
                            label="Method A",
                            value=method_choices[0][1] if method_choices else None
                        )
                        method2_slider = gr.Dropdown(
                            choices=method_choices,
                            label="Method B",
                            value=method_choices[1][1] if len(method_choices) > 1 else None
                        )
                        slider_compare_btn = gr.Button("🎚️ Generate Slider Comparison", variant="primary", size="lg")
                
                # Image slider for comparison
                comparison_slider = gr.ImageSlider(
                    label="Method Comparison (Drag slider to compare)",
                    show_label=True
                )
                slider_status = gr.Markdown()
                
                slider_compare_btn.click(
                    fn=create_slider_comparison,
                    inputs=[left_img_slider, right_img_slider, method1_slider, method2_slider],
                    outputs=[comparison_slider, slider_status],
                    show_progress=True
                )
                
                # Examples for interactive slider
                if examples:
                    example_inputs_slider = []
                    for left_path, right_path, name in examples[:3]:
                        # Load images as numpy arrays
                        left_img = cv2.imread(left_path)
                        right_img = cv2.imread(right_path)
                        if left_img is not None:
                            left_img = cv2.cvtColor(left_img, cv2.COLOR_BGR2RGB)
                        if right_img is not None:
                            right_img = cv2.cvtColor(right_img, cv2.COLOR_BGR2RGB)
                        example_inputs_slider.append([left_img, right_img])
                    
                    gr.Examples(
                        examples=example_inputs_slider,
                        inputs=[left_img_slider, right_img_slider],
                        label="📸 Example Stereo Pairs",
                        examples_per_page=3
                    )
            
            # Tab 3: Single Method Testing
            with gr.Tab("🎯 Single Method"):
                gr.Markdown("### Test Individual Methods")
                
                with gr.Row():
                    with gr.Column():
                        left_img_single = gr.Image(label="Left Image", type="numpy")
                        right_img_single = gr.Image(label="Right Image", type="numpy")
                    
                    with gr.Column():
                        method_single = gr.Dropdown(
                            choices=method_choices,
                            label="Select Method",
                            value=method_choices[0][1] if method_choices else None
                        )
                        single_btn = gr.Button("🚀 Process", variant="primary", size="lg")
                
                single_result = gr.Image(label="Disparity Result")
                single_status = gr.Markdown()
                
                single_btn.click(
                    fn=single_method_inference,
                    inputs=[left_img_single, right_img_single, method_single],
                    outputs=[single_result, single_status],
                    show_progress=True
                )
                
                # Examples for single method
                if examples:
                    example_inputs_single = []
                    for left_path, right_path, name in examples[:3]:
                        # Load images as numpy arrays
                        left_img = cv2.imread(left_path)
                        right_img = cv2.imread(right_path)
                        if left_img is not None:
                            left_img = cv2.cvtColor(left_img, cv2.COLOR_BGR2RGB)
                        if right_img is not None:
                            right_img = cv2.cvtColor(right_img, cv2.COLOR_BGR2RGB)
                        example_inputs_single.append([left_img, right_img])
                    
                    gr.Examples(
                        examples=example_inputs_single,
                        inputs=[left_img_single, right_img_single],
                        label="📸 Example Stereo Pairs",
                        examples_per_page=3
                    )
        
        # Information section
        with gr.Accordion("ℹ️ Method Information", open=False):
            gr.Markdown("""
            ### 🎯 FoundationStereo
            - **Type**: Zero-shot stereo matching using foundation models
            - **Variants**: Low-cost (11-33-40) and High-quality (23-51-11)
            - **Strengths**: Generalizes well to different domains without training
            - **Paper**: [FoundationStereo: Zero-Shot Stereo Matching via Foundation Model](https://arxiv.org/abs/2501.09898)
            
            ### ⚡ CREStereo
            - **Type**: Practical stereo matching with iterative refinement
            - **Model**: ETH3D pre-trained weights
            - **Strengths**: Fast inference with good accuracy
            - **Paper**: [Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation](https://arxiv.org/abs/2203.11483)
            
            ### 🎚️ Interactive Comparison Tips
            - Use the **ImageSlider** to quickly compare methods
            - Drag the slider to see differences in detail preservation
            - Look for differences in depth boundaries and texture regions
            - Different methods may perform better on different scene types
            
            ### 🚀 ZeroGPU Features
            - **Automatic GPU allocation**: GPU resources allocated on-demand
            - **Optimized timeouts**: Different durations for different operations
            - **Memory management**: Automatic cleanup after processing
            - **Queue management**: Fair resource sharing among users
            """)
        
        # Footer
        gr.Markdown("""
        ---
        ### 📝 Notes
        - **🚀 ZeroGPU Powered**: Automatic GPU allocation for optimal performance
        - **⏱️ Processing Times**: Method comparison ~2min, Single method ~1.5min
        - **🧠 Memory Management**: Automatic cleanup between comparisons
        - **📊 Best Results**: Use high-quality, well-rectified stereo pairs
        
        ### 🔗 References
        - [FoundationStereo Repository](https://github.com/NVlabs/FoundationStereo)
        - [CREStereo Repository](https://github.com/megvii-research/CREStereo)
        - [Gradio ImageSlider Documentation](https://gradio.app/docs/#imageslider)
        - [Hugging Face ZeroGPU](https://huggingface.co/zero-gpu-explorers)
        """)
    
    return app


def main():
    """Main function to launch the comparison app"""
    
    logging.info("🚀 Starting Stereo Matching Comparison App (ZeroGPU)...")
    
    # Check if we're in Hugging Face Spaces
    if 'SPACE_ID' in os.environ:
        logging.info("Running in Hugging Face Spaces environment")
    
    try:
        # Check if subdemo directories exist
        foundation_exists = os.path.exists(foundation_stereo_dir)
        crestereo_exists = os.path.exists(crestereo_dir)
        
        if not foundation_exists and not crestereo_exists:
            logging.error("No stereo matching demo directories found!")
            return
        
        logging.info(f"FoundationStereo demo: {'✅' if foundation_exists else '❌'}")
        logging.info(f"CREStereo demo: {'✅' if crestereo_exists else '❌'}")
        
        # Create and launch app
        logging.info("Creating comparison app...")
        app = create_app()
        logging.info("✅ Comparison app created successfully")
        
        # Launch with Spaces-optimized settings
        app.launch(
            share=False,  # Spaces handles sharing
            show_error=True,
            favicon_path=None,
            ssr_mode=False,
            allowed_paths=["./"]
        )
        
    except Exception as e:
        logging.error(f"Failed to launch app: {e}")
        raise


if __name__ == "__main__":
    main()