"""Gradio Space for Agent Cost Optimizer Dashboard.

This app visualizes cost-quality frontiers from ACO benchmark runs.
If no benchmark data exists, it runs the benchmark on first load.
"""

import json
import subprocess
import sys
from pathlib import Path
from typing import Dict, List, Any

import gradio as gr


def ensure_data_exists():
    """Run benchmark if data doesn't exist."""
    results_path = Path("eval_results_v2/baseline_results.json")
    report_path = Path("eval_results_v2/report.txt")
    
    if not results_path.exists() or not report_path.exists():
        print("Benchmark data not found. Running benchmark...")
        try:
            # Run the benchmark generator
            subprocess.run(
                [sys.executable, "standalone_eval_v2.py", "--tasks", "2000", "--output", "eval_results_v2"],
                capture_output=True, text=True, timeout=120
            )
            print("Benchmark complete.")
        except Exception as e:
            print(f"Benchmark failed: {e}")
    
    return results_path, report_path


def load_results(path: str) -> Dict[str, Any]:
    with open(path) as f:
        return json.load(f)


def parse_report(report_path: str) -> str:
    with open(report_path) as f:
        return f.read()


def create_frontier_plot(results: Dict[str, Any]):
    points = []
    for name, data in results.items():
        success = (data.get("num_success", 0) + data.get("num_partial", 0)) / max(data.get("num_tasks", 1), 1)
        cost = data.get("avg_cost_success", 0)
        points.append({"baseline": name, "success_rate": success, "cost_per_success": cost})
    points.sort(key=lambda p: (-p["success_rate"], p["cost_per_success"]))
    frontier = []
    min_cost = float("inf")
    for p in points:
        if p["cost_per_success"] <= min_cost:
            frontier.append(p)
            min_cost = p["cost_per_success"]
    return points, frontier


def build_dashboard():
    results_path, report_path = ensure_data_exists()
    
    if not results_path.exists() or not report_path.exists():
        with gr.Blocks(title="Agent Cost Optimizer Dashboard") as demo:
            gr.Markdown("# Agent Cost Optimizer Dashboard")
            gr.Markdown("## Benchmark data not available")
            gr.Markdown("Run `python standalone_eval_v2.py --tasks 2000 --output eval_results_v2` to generate data.")
        return demo
    
    results = load_results(str(results_path))
    report_text = parse_report(str(report_path))
    points, frontier = create_frontier_plot(results)
    
    with gr.Blocks(title="Agent Cost Optimizer Dashboard") as demo:
        gr.Markdown("# Agent Cost Optimizer - Cost-Quality Dashboard")
        gr.Markdown("Visualize cost-quality tradeoffs across routing strategies and ablations.")
        
        with gr.Row():
            with gr.Column(scale=2):
                gr.Markdown("## Cost-Quality Frontier")
                gr.Markdown("**X-axis**: Average cost per successful task | **Y-axis**: Success rate")
                
                scatter_data = [
                    [p["baseline"], f"{p['success_rate']:.1%}", f"${p['cost_per_success']:.4f}"]
                    for p in points
                ]
                gr.Dataframe(
                    headers=["Baseline", "Success Rate", "Cost per Success"],
                    value=scatter_data,
                    label="All Baselines",
                )
                
                frontier_data = [
                    [p["baseline"], f"{p['success_rate']:.1%}", f"${p['cost_per_success']:.4f}"]
                    for p in frontier
                ]
                gr.Dataframe(
                    headers=["Baseline", "Success Rate", "Cost per Success"],
                    value=frontier_data,
                    label="Pareto Frontier",
                )
            
            with gr.Column(scale=1):
                gr.Markdown("## Pareto Frontier Baselines")
                pareto_names = [p["baseline"] for p in frontier]
                for name in pareto_names:
                    gr.Markdown(f"- **{name}**")
        
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Baseline Comparison")
                comparison_data = []
                for name, data in results.items():
                    comparison_data.append([
                        name,
                        f"{(data.get('num_success',0)+data.get('num_partial',0))/max(data.get('num_tasks',1),1):.1%}",
                        f"${data.get('avg_cost_success',0):.4f}",
                        f"${data.get('total_cost',0):.2f}",
                        f"{data.get('cost_reduction_vs_frontier',0):.1%}",
                        f"{data.get('false_done_rate',0):.1%}",
                        f"{data.get('unsafe_cheap_miss_rate',0):.1%}",
                        f"{data.get('regression_rate',0):.1%}",
                    ])
                gr.Dataframe(
                    headers=["Baseline", "Success", "Cost/Success", "Total Cost", "Cost Reduction", "False-DONE", "Cheap Miss", "Regression"],
                    value=comparison_data,
                )
        
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Per-Scenario Breakdown (Full Optimizer)")
                full_data = results.get("full_optimizer", {})
                scenario_stats = full_data.get("per_scenario_stats", {})
                if scenario_stats:
                    scenario_data = []
                    for scenario, stats in scenario_stats.items():
                        count = stats.get("count", 0)
                        success = stats.get("success", 0)
                        cost = stats.get("cost", 0)
                        scenario_data.append([
                            scenario,
                            str(count),
                            f"{success/max(count,1):.1%}",
                            f"${cost:.2f}",
                        ])
                    gr.Dataframe(
                        headers=["Scenario", "Count", "Success Rate", "Total Cost"],
                        value=scenario_data,
                    )
        
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Ablation Impact")
                gr.Markdown("Cost impact when removing each module (vs full_optimizer)")
                
                full_cost = results.get("full_optimizer", {}).get("total_cost", 0)
                ablation_data = []
                for name, data in results.items():
                    if name.startswith("no_"):
                        delta = data.get("total_cost", 0) - full_cost
                        pct = (delta / max(full_cost, 0.001)) * 100
                        ablation_data.append([name, f"${delta:.2f}", f"{pct:.1f}%"])
                
                if ablation_data:
                    ablation_data.sort(key=lambda x: float(x[1].replace("$", "")), reverse=True)
                    gr.Dataframe(
                        headers=["Module Removed", "Cost Increase", "% Increase"],
                        value=ablation_data,
                    )
        
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Full Report")
                gr.Textbox(report_text, lines=40, label="Benchmark Report", interactive=False)
    
    return demo


if __name__ == "__main__":
    demo = build_dashboard()
    demo.launch(server_name="0.0.0.0", server_port=7860)