import gradio as gr

# Sample data for demonstration
perception_papers = [
    {
        "title": "CoSDH: Communication-Efficient Collaborative Perception",
        "venue": "CVPR 2025",
        "description": "Novel approach for efficient collaborative perception using supply-demand awareness.",
        "link": "https://arxiv.org/abs/2503.03430"
    },
    {
        "title": "V2X-R: Cooperative LiDAR-4D Radar Fusion",
        "venue": "CVPR 2025", 
        "description": "Cooperative fusion of LiDAR and 4D radar sensors for enhanced 3D object detection.",
        "link": "https://arxiv.org/abs/2411.08402"
    },
    {
        "title": "Where2comm: Efficient Collaborative Perception via Spatial Confidence Maps",
        "venue": "NeurIPS 2022",
        "description": "Groundbreaking work on efficient collaborative perception using spatial confidence maps.",
        "link": "https://openreview.net/forum?id=dLL4KXzKUpS"
    },
    {
        "title": "STAMP: Scalable Task-Agnostic Collaborative Perception",
        "venue": "ICLR 2025",
        "description": "Framework for scalable collaborative perception that is both task and model agnostic.",
        "link": "https://openreview.net/forum?id=8NdNniulYE"
    },
    {
        "title": "CoBEVFlow: Robust Asynchronous Collaborative 3D Detection",
        "venue": "NeurIPS 2023",
        "description": "Handles temporal asynchrony in collaborative perception using bird's eye view flow.",
        "link": "https://openreview.net/forum?id=UHIDdtxmVS"
    }
]

datasets_data = [
    ["DAIR-V2X", "2022", "Real-world", "V2I", "71K frames", "3D boxes, Infrastructure"],
    ["V2V4Real", "2023", "Real-world", "V2V", "20K frames", "Real V2V, Highway"],
    ["TUMTraf-V2X", "2024", "Real-world", "V2X", "2K sequences", "Dense labels, Urban"],
    ["OPV2V", "2022", "Simulation", "V2V", "Large-scale", "CARLA, Multi-agent"],
    ["V2X-Sim", "2021", "Simulation", "Multi", "Scalable", "Multi-agent, Collaborative"],
    ["DOLPHINS", "2024", "Simulation", "UAV", "UAV swarms", "AirSim, Multi-UAV"]
]

def create_paper_card(paper):
    return f"""
    <div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; margin: 10px 0; background: white; box-shadow: 0 2px 5px rgba(0,0,0,0.1);">
        <div style="background: #667eea; color: white; padding: 5px 10px; border-radius: 15px; display: inline-block; font-size: 0.8em; margin-bottom: 10px;">
            {paper['venue']}
        </div>
        <h3 style="color: #333; margin: 10px 0;">{paper['title']}</h3>
        <p style="color: #666; line-height: 1.5; margin-bottom: 15px;">{paper['description']}</p>
        <a href="{paper['link']}" target="_blank" style="background: #667eea; color: white; padding: 8px 15px; border-radius: 5px; text-decoration: none; font-size: 0.9em;">
            📄 Read Paper
        </a>
    </div>
    """

# Custom CSS
custom_css = """
.gradio-container {
    max-width: 1200px !important;
}
.main-header {
    text-align: center;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    color: white;
    padding: 40px 20px;
    border-radius: 15px;
    margin-bottom: 30px;
}
"""

# Create the interface
with gr.Blocks(
    title="🤖 Awesome Multi-Agent Collaborative Perception",
    theme=gr.themes.Soft(),
    css=custom_css
) as demo:
    
    # Header
    gr.HTML("""
    <div class="main-header">
        <h1 style="font-size: 2.5rem; margin-bottom: 10px;">🤖 Awesome Multi-Agent Collaborative Perception</h1>
        <p style="font-size: 1.2rem; opacity: 0.9;">Explore cutting-edge resources for Multi-Agent Collaborative Perception, Prediction, and Planning</p>
        <div style="display: flex; justify-content: center; gap: 30px; margin-top: 20px; flex-wrap: wrap;">
            <div style="background: rgba(255,255,255,0.2); padding: 10px 20px; border-radius: 25px;">
                <div style="font-size: 1.5rem; font-weight: bold;">200+</div>
                <div>Papers</div>
            </div>
            <div style="background: rgba(255,255,255,0.2); padding: 10px 20px; border-radius: 25px;">
                <div style="font-size: 1.5rem; font-weight: bold;">25+</div>
                <div>Datasets</div>
            </div>
            <div style="background: rgba(255,255,255,0.2); padding: 10px 20px; border-radius: 25px;">
                <div style="font-size: 1.5rem; font-weight: bold;">50+</div>
                <div>Code Repos</div>
            </div>
            <div style="background: rgba(255,255,255,0.2); padding: 10px 20px; border-radius: 25px;">
                <div style="font-size: 1.5rem; font-weight: bold;">2025</div>
                <div>Updated</div>
            </div>
        </div>
    </div>
    """)
    
    # Main navigation tabs
    with gr.Tabs():
        
        with gr.Tab("🔍 Perception"):
            gr.Markdown("## Multi-Agent Collaborative Perception Papers")
            gr.Markdown("*Latest research in collaborative sensing, 3D object detection, and V2X communication*")
            
            # Create paper cards
            papers_html = "".join([create_paper_card(paper) for paper in perception_papers])
            gr.HTML(papers_html)
            
            gr.Markdown("""
            ### 🔄 Key Communication Strategies:
            - **Early Fusion**: Raw sensor data sharing
            - **Late Fusion**: Detection-level information exchange  
            - **Intermediate Fusion**: Feature-level collaboration
            - **Selective Communication**: Confidence-based data sharing
            """)
            
        with gr.Tab("📊 Datasets"):
            gr.Markdown("## Datasets & Benchmarks")
            gr.Markdown("*Comprehensive collection of real-world and simulation datasets*")
            
            gr.Dataframe(
                value=datasets_data,
                headers=["Dataset", "Year", "Type", "Agents", "Size", "Features"],
                datatype=["str", "str", "str", "str", "str", "str"],
                interactive=False
            )
            
            gr.Markdown("""
            ### 🌟 Notable Features:
            - **DAIR-V2X**: First real-world V2I collaborative perception dataset with infrastructure sensors
            - **V2V4Real**: Real vehicle-to-vehicle communication dataset collected on highways
            - **TUMTraf-V2X**: Dense annotations for urban collaborative perception scenarios
            - **OPV2V**: Large-scale simulation benchmark built on CARLA platform
            - **V2X-Sim**: Comprehensive multi-agent simulation with customizable scenarios
            """)
            
        with gr.Tab("📍 Tracking"):
            gr.Markdown("## Multi-Object Tracking & State Estimation")
            gr.Markdown("*Collaborative tracking across distributed agents with uncertainty quantification*")
            
            gr.HTML("""
            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 20px; margin: 20px 0;">
                <div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; background: white; box-shadow: 0 2px 5px rgba(0,0,0,0.1);">
                    <h3 style="color: #4ECDC4;">MOT-CUP</h3>
                    <p>Multi-Object Tracking with Conformal Uncertainty Propagation</p>
                    <a href="https://arxiv.org/abs/2303.14346" target="_blank" style="color: #667eea;">📄 Paper</a>
                </div>
                <div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; background: white; box-shadow: 0 2px 5px rgba(0,0,0,0.1);">
                    <h3 style="color: #4ECDC4;">DMSTrack</h3>
                    <p>Probabilistic 3D Multi-Object Cooperative Tracking (ICRA 2024)</p>
                    <a href="https://arxiv.org/abs/2309.14655" target="_blank" style="color: #667eea;">📄 Paper</a>
                </div>
                <div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; background: white; box-shadow: 0 2px 5px rgba(0,0,0,0.1);">
                    <h3 style="color: #4ECDC4;">CoDynTrust</h3>
                    <p>Dynamic Feature Trust for Robust Asynchronous Collaborative Perception (ICRA 2025)</p>
                    <a href="https://arxiv.org/abs/2502.08169" target="_blank" style="color: #667eea;">📄 Paper</a>
                </div>
            </div>
            """)
            
            gr.Markdown("""
            ### 🎯 Key Challenges:
            - **Temporal Asynchrony**: Handling different sensor timestamps and communication delays
            - **Uncertainty Quantification**: Reliable confidence estimation across multiple agents
            - **Data Association**: Multi-agent correspondence and track management
            - **Scalability**: Maintaining performance with increasing number of agents
            """)
            
        with gr.Tab("🔮 Prediction"):
            gr.Markdown("## Trajectory Forecasting & Motion Prediction")
            gr.Markdown("*Cooperative prediction for autonomous systems and multi-agent coordination*")
            
            gr.HTML("""
            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 20px; margin: 20px 0;">
                <div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; background: white; box-shadow: 0 2px 5px rgba(0,0,0,0.1);">
                    <h3 style="color: #45B7D1;">V2X-Graph</h3>
                    <p>Learning Cooperative Trajectory Representations (NeurIPS 2024)</p>
                    <a href="https://arxiv.org/abs/2311.00371" target="_blank" style="color: #667eea;">📄 Paper</a>
                </div>
                <div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; background: white; box-shadow: 0 2px 5px rgba(0,0,0,0.1);">
                    <h3 style="color: #45B7D1;">Co-MTP</h3>
                    <p>Cooperative Multi-Temporal Prediction Framework (ICRA 2025)</p>
                    <a href="https://arxiv.org/abs/2502.16589" target="_blank" style="color: #667eea;">📄 Paper</a>
                </div>
            </div>
            """)
            
            gr.HTML("""
            <div style="background: #f8f9fa; border-radius: 10px; padding: 20px; margin: 20px 0;">
                <h3>🧠 Key Approaches:</h3>
                <ul style="line-height: 1.8;">
                    <li><strong>Graph Neural Networks</strong>: Modeling agent interactions and social behaviors</li>
                    <li><strong>Transformer Architectures</strong>: Attention-based prediction with long-range dependencies</li>
                    <li><strong>Multi-Modal Fusion</strong>: Combining LiDAR, camera, and communication data</li>
                    <li><strong>Uncertainty Quantification</strong>: Reliable confidence estimation for safety-critical applications</li>
                </ul>
            </div>
            """)
            
        with gr.Tab("⚙️ Methods"):
            gr.Markdown("## Methods & Techniques")
            gr.Markdown("*Core methodologies for communication, robustness, and learning in collaborative systems*")
            
            with gr.Row():
                with gr.Column():
                    gr.Markdown("""
                    ### 📡 Communication Strategies
                    - **Bandwidth Optimization**: Compression and selective sharing
                    - **Protocol Design**: V2V, V2I, V2X communication standards
                    - **Network Topology**: Centralized vs. distributed architectures
                    - **Quality of Service**: Latency and reliability management
                    """)
                
                with gr.Column():
                    gr.Markdown("""
                    ### 🛡️ Robustness Approaches
                    - **Byzantine Fault Tolerance**: Handling adversarial agents
                    - **Uncertainty Handling**: Robust fusion under noise
                    - **Privacy Preservation**: Secure multi-party computation
                    - **Malicious Agent Detection**: CP-Guard framework (AAAI 2025)
                    """)
            
            gr.HTML("""
            <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; border-radius: 10px; padding: 20px; margin: 20px 0;">
                <h3>🧠 Learning Paradigms</h3>
                <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 15px; margin-top: 15px;">
                    <div>• <strong>Federated Learning</strong>: Distributed model training</div>
                    <div>• <strong>Transfer Learning</strong>: Cross-domain adaptation</div>
                    <div>• <strong>Meta-Learning</strong>: Quick adaptation to new scenarios</div>
                    <div>• <strong>Heterogeneous Learning</strong>: HEAL framework (ICLR 2024)</div>
                </div>
            </div>
            """)
            
        with gr.Tab("🏛️ Conferences"):
            gr.Markdown("## Top Venues & Publication Trends")
            gr.Markdown("*Premier conferences and emerging research directions in collaborative perception*")
            
            conference_data = [
                ["CVPR 2025", "5+", "End-to-end systems, robustness"],
                ["ICLR 2025", "3+", "Learning representations, scalability"], 
                ["AAAI 2025", "4+", "AI applications, defense mechanisms"],
                ["ICRA 2025", "6+", "Robotics applications, real-world deployment"],
                ["NeurIPS 2024", "2+", "Theoretical foundations, novel architectures"]
            ]
            
            gr.Dataframe(
                value=conference_data,
                headers=["Conference", "Papers", "Focus Areas"],
                datatype=["str", "str", "str"],
                interactive=False
            )
            
            gr.Markdown("""
            ### 📊 Research Trends (2024-2025):
            - **Communication Efficiency**: 40% increase in bandwidth-aware methods
            - **Robustness & Security**: Emerging focus on adversarial robustness (15+ papers)
            - **Real-World Deployment**: Growing emphasis on practical systems and field tests
            - **Heterogeneous Systems**: Multi-modal and multi-agent diversity becoming standard
            - **End-to-End Learning**: Integration of perception, prediction, and planning
            """)
    
    # Footer
    gr.HTML("""
    <div style="text-align: center; margin-top: 40px; padding: 30px; background: #f8f9fa; border-radius: 10px;">
        <h3>🤝 Contributing</h3>
        <p>We welcome contributions! Please submit papers, datasets, and code repositories via GitHub.</p>
        <div style="margin-top: 20px;">
            <a href="https://github.com/your-username/awesome-multi-agent-collaborative-perception" target="_blank" 
               style="background: #667eea; color: white; padding: 10px 20px; border-radius: 5px; text-decoration: none; margin: 5px;">
                📚 GitHub Repository
            </a>
            <a href="https://huggingface.co/spaces/your-username/awesome-multi-agent-collaborative-perception" target="_blank"
               style="background: #ff6b6b; color: white; padding: 10px 20px; border-radius: 5px; text-decoration: none; margin: 5px;">
                🤗 Hugging Face Space
            </a>
        </div>
        <p style="margin-top: 20px; color: #666;">Made with ❤️ for the Collaborative Perception Community</p>
    </div>
    """)

if __name__ == "__main__":
    demo.launch()