# -*- coding: utf-8 -*-
"""
Hugging Face Spaces Dashboard for NSN Integration
Multi-panel interactive dashboard for contributor challenges
"""
import gradio as gr
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
from typing import Dict, List, Tuple
import json

from backend_telemetry_rank_adapter import BackendTelemetryRankAdapter
from edit_propagation_engine import EditPropagationEngine
from rank_feedback_generator import RankFeedbackGenerator
from ensemble_inference_manager import EnsembleInferenceManager


class NSNDashboard:
    """Hugging Face Spaces Dashboard for NSN Integration"""
    
    def __init__(self):
        self.telemetry_adapter = BackendTelemetryRankAdapter()
        self.propagation_engine = EditPropagationEngine()
        self.feedback_generator = RankFeedbackGenerator()
        self.ensemble_manager = EnsembleInferenceManager()
        
    # Panel 1: FLOPs vs Reliability (per backend)
    def create_flops_reliability_chart(self, backend_id: str) -> go.Figure:
        """Line chart of rank vs reliability across backend states"""
        ranks = [8, 16, 32, 64, 128, 256]
        
        # Simulate different backend states
        states = {
            'Optimal': {'error_rate': 0.01, 'coherence_time': 150.0, 'gate_fidelity': 0.99},
            'Good': {'error_rate': 0.03, 'coherence_time': 100.0, 'gate_fidelity': 0.96},
            'Degraded': {'error_rate': 0.06, 'coherence_time': 60.0, 'gate_fidelity': 0.92},
            'Poor': {'error_rate': 0.10, 'coherence_time': 30.0, 'gate_fidelity': 0.88}
        }
        
        fig = go.Figure()
        
        for state_name, telemetry in states.items():
            reliabilities = []
            flops = []
            
            for rank in ranks:
                result = self.telemetry_adapter.adapt_rank(
                    backend_id=backend_id,
                    telemetry=telemetry,
                    current_rank=rank
                )
                reliabilities.append(result.reliability_score)
                flops.append(rank * 1e6)  # Approximate FLOPs
            
            fig.add_trace(go.Scatter(
                x=flops,
                y=reliabilities,
                mode='lines+markers',
                name=state_name,
                line=dict(width=2),
                marker=dict(size=8)
            ))
        
        fig.update_layout(
            title=f'FLOPs vs Reliability - {backend_id}',
            xaxis_title='FLOPs',
            yaxis_title='Reliability Score',
            xaxis_type='log',
            template='plotly_white',
            height=400
        )
        
        return fig
    
    # Panel 2: Multilingual Heatmap (accuracy across ranks)
    def create_multilingual_heatmap(self, languages: List[str]) -> go.Figure:
        """Heatmap of accuracy across languages and ranks"""
        ranks = [8, 16, 32, 64, 128, 256]
        
        # Simulate accuracy data
        accuracy_matrix = []
        for lang in languages:
            lang_accuracies = []
            base_accuracy = 0.95 if lang in ['english', 'chinese', 'spanish'] else 0.75
            
            for rank in ranks:
                # Higher ranks = higher accuracy
                accuracy = base_accuracy + (rank / 256.0) * 0.1
                accuracy = min(accuracy, 0.99)
                lang_accuracies.append(accuracy)
            
            accuracy_matrix.append(lang_accuracies)
        
        fig = go.Figure(data=go.Heatmap(
            z=accuracy_matrix,
            x=[f'Rank {r}' for r in ranks],
            y=languages,
            colorscale='RdYlGn',
            text=[[f'{val:.3f}' for val in row] for row in accuracy_matrix],
            texttemplate='%{text}',
            textfont={"size": 10},
            colorbar=dict(title='Accuracy')
        ))
        
        fig.update_layout(
            title='Multilingual Edit Accuracy Across Ranks',
            xaxis_title='NSN Rank',
            yaxis_title='Language',
            template='plotly_white',
            height=400
        )
        
        return fig
    
    # Panel 3: Subspace Containment Graphs
    def create_containment_heatmap(self, languages: List[str], rank: int) -> go.Figure:
        """Heatmap of containment scores with flow arrows"""
        heatmap_data = self.propagation_engine.compute_containment_heatmap(languages, rank)
        
        fig = go.Figure(data=go.Heatmap(
            z=heatmap_data,
            x=languages,
            y=languages,
            colorscale='Blues',
            text=[[f'{val:.2f}' for val in row] for row in heatmap_data],
            texttemplate='%{text}',
            textfont={"size": 10},
            colorbar=dict(title='Containment Score')
        ))
        
        # Add flow arrows for high containment
        annotations = []
        for i, source in enumerate(languages):
            for j, target in enumerate(languages):
                if i != j and heatmap_data[i][j] > 0.75:
                    annotations.append(dict(
                        x=j,
                        y=i,
                        text='→',
                        showarrow=False,
                        font=dict(size=20, color='red')
                    ))
        
        fig.update_layout(
            title=f'Subspace Containment Matrix (Rank {rank})',
            xaxis_title='Target Language',
            yaxis_title='Source Language',
            annotations=annotations,
            template='plotly_white',
            height=500
        )
        
        return fig
    
    # Panel 4: Pareto Frontier (efficiency vs expressiveness)
    def create_pareto_frontier(self, contributor_data: List[Dict]) -> go.Figure:
        """Scatter plot showing efficiency vs accuracy trade-off"""
        fig = go.Figure()
        
        # Group by contributor
        contributors = {}
        for data in contributor_data:
            cid = data['contributor_id']
            if cid not in contributors:
                contributors[cid] = {'efficiency': [], 'accuracy': [], 'ranks': []}
            
            contributors[cid]['efficiency'].append(data['efficiency'])
            contributors[cid]['accuracy'].append(data['accuracy'])
            contributors[cid]['ranks'].append(data['rank'])
        
        # Plot each contributor
        for cid, data in contributors.items():
            fig.add_trace(go.Scatter(
                x=data['efficiency'],
                y=data['accuracy'],
                mode='markers+lines',
                name=cid,
                marker=dict(size=10),
                text=[f'Rank {r}' for r in data['ranks']],
                hovertemplate='<b>%{text}</b><br>Efficiency: %{x:.2e}<br>Accuracy: %{y:.3f}'
            ))
        
        # Add Pareto frontier
        all_efficiency = [e for d in contributors.values() for e in d['efficiency']]
        all_accuracy = [a for d in contributors.values() for a in d['accuracy']]
        
        # Find Pareto optimal points
        pareto_x, pareto_y = self._compute_pareto_frontier(all_efficiency, all_accuracy)
        
        fig.add_trace(go.Scatter(
            x=pareto_x,
            y=pareto_y,
            mode='lines',
            name='Pareto Frontier',
            line=dict(color='red', width=3, dash='dash')
        ))
        
        fig.update_layout(
            title='Efficiency vs Accuracy Pareto Frontier',
            xaxis_title='Efficiency (Accuracy/FLOPs)',
            yaxis_title='Accuracy',
            xaxis_type='log',
            template='plotly_white',
            height=400
        )
        
        return fig
    
    def _compute_pareto_frontier(self, x: List[float], y: List[float]) -> Tuple[List, List]:
        """Compute Pareto frontier points"""
        points = sorted(zip(x, y), key=lambda p: (-p[0], -p[1]))
        pareto_x, pareto_y = [], []
        max_y = -float('inf')
        
        for px, py in points:
            if py > max_y:
                pareto_x.append(px)
                pareto_y.append(py)
                max_y = py
        
        return pareto_x, pareto_y
    
    # Panel 5: Contributor Leaderboard + Feedback
    def create_leaderboard_table(self, leaderboard_data: List[Dict]) -> pd.DataFrame:
        """Create leaderboard DataFrame"""
        df = pd.DataFrame(leaderboard_data)
        df = df.sort_values('total_score', ascending=False)
        df['rank'] = range(1, len(df) + 1)
        
        return df[['rank', 'contributor_id', 'badge', 'total_score', 
                   'avg_accuracy', 'avg_efficiency', 'num_submissions']]
    
    def create_feedback_panel(self, contributor_id: str) -> Dict:
        """Generate personalized feedback panel"""
        panel = self.feedback_generator.generate_feedback_panel(contributor_id)
        
        feedback_html = f"""
        <div style="padding: 20px; background: #f0f0f0; border-radius: 10px;">
            <h3>🎯 Personalized Feedback for {contributor_id}</h3>
            <p><strong>Badge:</strong> {panel['recommendation'].personalized_badge}</p>
            <p><strong>Recommended Rank:</strong> {panel['recommendation'].recommended_rank}</p>
            <p><strong>Confidence:</strong> {panel['recommendation'].confidence:.2%}</p>
            
            <h4>📊 Your Statistics:</h4>
            <ul>
                <li>Total Submissions: {panel['stats'].get('total_submissions', 0)}</li>
                <li>Unique Languages: {panel['stats'].get('unique_languages', 0)}</li>
                <li>Avg Accuracy: {panel['stats'].get('avg_accuracy', 0):.3f}</li>
                <li>Avg Efficiency: {panel['stats'].get('avg_efficiency', 0):.2e}</li>
            </ul>
            
            <h4>💡 Suggestions:</h4>
            <ol>
                {''.join([f'<li>{s}</li>' for s in panel['suggestions']])}
            </ol>
            
            <h4>🔍 Unexplored Opportunities:</h4>
            <ul>
                {''.join([f'<li>Rank {r} with {lang}</li>' 
                         for r, lang in panel['recommendation'].unexplored_pairs[:5]])}
            </ul>
        </div>
        """
        
        return feedback_html
    
    # Ensemble Agreement Matrix
    def create_agreement_matrix(self, backend_list: List[str]) -> go.Figure:
        """Backend consensus heatmap"""
        edit_vector = np.random.randn(256) * 0.1
        result = self.ensemble_manager.run_ensemble_inference(edit_vector, backend_list)
        
        fig = go.Figure(data=go.Heatmap(
            z=result.agreement_matrix,
            x=backend_list,
            y=backend_list,
            colorscale='RdYlGn',
            text=[[f'{val:.2f}' for val in row] for row in result.agreement_matrix],
            texttemplate='%{text}',
            textfont={"size": 12},
            colorbar=dict(title='Agreement Score')
        ))
        
        fig.update_layout(
            title=f'Backend Agreement Matrix (Score: {result.agreement_score:.3f})',
            xaxis_title='Backend',
            yaxis_title='Backend',
            template='plotly_white',
            height=400
        )
        
        return fig


def create_gradio_interface():
    """Create Gradio interface for Hugging Face Spaces"""
    dashboard = NSNDashboard()
    
    with gr.Blocks(title="NSN Integration Dashboard", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🚀 Quantum LIMIT-Graph v2.4.0: NSN Integration Dashboard
        
        Interactive dashboard for contributor challenges with real-time visualization
        """)
        
        with gr.Tabs():
            # Tab 1: Backend Telemetry Rank Adaptation
            with gr.Tab("📊 Panel 1: Backend Telemetry"):
                gr.Markdown("### Real-Time Backend-Aware Rank Adaptation")
                
                with gr.Row():
                    backend_select = gr.Dropdown(
                        choices=['ibm_manila', 'ibm_washington', 'russian_simulator'],
                        value='ibm_washington',
                        label="Select Backend"
                    )
                    refresh_btn1 = gr.Button("Generate Chart")
                
                flops_plot = gr.Plot(label="FLOPs vs Reliability")
                
                gr.Markdown("""
                **Contributor Task:** Submit edits optimized for dynamic rank shifts
                
                **Leaderboard Metric:** Responsiveness vs reliability trade-off
                """)
                
                refresh_btn1.click(
                    fn=dashboard.create_flops_reliability_chart,
                    inputs=[backend_select],
                    outputs=[flops_plot]
                )
            
            # Tab 2: Multilingual Heatmap
            with gr.Tab("🌍 Panel 2: Multilingual Accuracy"):
                gr.Markdown("### Accuracy Across Languages and Ranks")
                
                language_select = gr.CheckboxGroup(
                    choices=['english', 'chinese', 'spanish', 'french', 'russian', 
                            'indonesian', 'vietnamese', 'swahili'],
                    value=['english', 'chinese', 'indonesian', 'swahili'],
                    label="Select Languages"
                )
                refresh_btn2 = gr.Button("Generate Heatmap")
                
                multilingual_plot = gr.Plot(label="Multilingual Accuracy Heatmap")
                
                refresh_btn2.click(
                    fn=dashboard.create_multilingual_heatmap,
                    inputs=[language_select],
                    outputs=[multilingual_plot]
                )
            
            # Tab 3: Subspace Containment
            with gr.Tab("🔗 Panel 3: Edit Propagation"):
                gr.Markdown("### Cross-Lingual Edit Propagation via Subspace Containment")
                
                with gr.Row():
                    prop_languages = gr.CheckboxGroup(
                        choices=['english', 'chinese', 'spanish', 'indonesian', 'swahili'],
                        value=['english', 'chinese', 'indonesian'],
                        label="Select Languages"
                    )
                    rank_slider = gr.Slider(8, 256, value=128, step=8, label="NSN Rank")
                
                refresh_btn3 = gr.Button("Generate Containment Map")
                containment_plot = gr.Plot(label="Subspace Containment Heatmap")
                
                gr.Markdown("""
                **Contributor Task:** Submit propagation strategies and containment visualizations
                
                **Leaderboard Metric:** Quality score of propagated edits
                """)
                
                refresh_btn3.click(
                    fn=dashboard.create_containment_heatmap,
                    inputs=[prop_languages, rank_slider],
                    outputs=[containment_plot]
                )
            
            # Tab 4: Pareto Frontier
            with gr.Tab("⚡ Panel 4: Efficiency Frontier"):
                gr.Markdown("### Pareto Frontier: Efficiency vs Expressiveness")
                
                # Sample data input
                sample_data_json = gr.Textbox(
                    label="Contributor Data (JSON)",
                    value=json.dumps([
                        {'contributor_id': 'user_001', 'rank': 32, 'accuracy': 0.88, 'efficiency': 8.6e-8},
                        {'contributor_id': 'user_001', 'rank': 64, 'accuracy': 0.92, 'efficiency': 2.2e-8},
                        {'contributor_id': 'user_002', 'rank': 16, 'accuracy': 0.82, 'efficiency': 3.2e-7},
                        {'contributor_id': 'user_002', 'rank': 128, 'accuracy': 0.95, 'efficiency': 5.8e-9}
                    ], indent=2),
                    lines=10
                )
                
                refresh_btn4 = gr.Button("Generate Pareto Frontier")
                pareto_plot = gr.Plot(label="Efficiency vs Accuracy")
                
                def plot_pareto(json_str):
                    data = json.loads(json_str)
                    return dashboard.create_pareto_frontier(data)
                
                refresh_btn4.click(
                    fn=plot_pareto,
                    inputs=[sample_data_json],
                    outputs=[pareto_plot]
                )
            
            # Tab 5: Leaderboard & Feedback
            with gr.Tab("🏆 Panel 5: Leaderboard"):
                gr.Markdown("### Contributor Leaderboard + Personalized Feedback")
                
                with gr.Row():
                    contributor_input = gr.Textbox(
                        label="Contributor ID",
                        value="contributor_001"
                    )
                    get_feedback_btn = gr.Button("Get Feedback")
                
                feedback_html = gr.HTML(label="Personalized Feedback")
                
                gr.Markdown("""
                **Contributor Task:** Submit edits across ranks and analyze feedback
                
                **Leaderboard Metric:** Efficiency badge (accuracy/FLOPs)
                """)
                
                get_feedback_btn.click(
                    fn=dashboard.create_feedback_panel,
                    inputs=[contributor_input],
                    outputs=[feedback_html]
                )
            
            # Tab 6: Ensemble Agreement
            with gr.Tab("🔬 Panel 6: Ensemble Inference"):
                gr.Markdown("### Backend Agreement Matrix")
                
                backend_checkboxes = gr.CheckboxGroup(
                    choices=['ibm_manila', 'ibm_washington', 'russian_simulator', 
                            'ibm_kyoto', 'google_sycamore'],
                    value=['ibm_manila', 'ibm_washington', 'russian_simulator'],
                    label="Select Backends"
                )
                
                refresh_btn6 = gr.Button("Generate Agreement Matrix")
                agreement_plot = gr.Plot(label="Backend Consensus Heatmap")
                
                gr.Markdown("""
                **Contributor Task:** Submit ensemble edits and analyze backend agreement
                
                **Leaderboard Metric:** Agreement score + reliability boost
                """)
                
                refresh_btn6.click(
                    fn=dashboard.create_agreement_matrix,
                    inputs=[backend_checkboxes],
                    outputs=[agreement_plot]
                )
        
        gr.Markdown("""
        ---
        ### 📚 Resources
        - [GitHub Repository](https://github.com/your-repo/quantum-limit-graph)
        - [Documentation](https://github.com/your-repo/quantum-limit-graph/blob/main/quantum_integration/nsn_integration/README.md)
        - [Contributor Guide](https://github.com/your-repo/quantum-limit-graph/blob/main/quantum_integration/nsn_integration/CONTRIBUTOR_GUIDE.md)
        """)
    
    return demo


if __name__ == '__main__':
    demo = create_gradio_interface()
    demo.launch()