import asyncio
import os
import typer
import random
from rich.console import Console
from rich.table import Table
from rich.panel import Panel
from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn

app = typer.Typer(help="Agent Quality Hill Climber: Iteratively optimize agent quality using ADK patterns.")
console = Console()

# --- ADK GOLDEN DATASET ---
GOLDEN_DATASET = [
    {
        "query": "How do I deploy to Cloud Run?",
        "expected": "Use the 'make deploy-prod' command to deploy to Cloud Run.",
        "type": "retrieval"
    },
    {
        "query": "What is the Hive Mind?",
        "expected": "The Hive Mind is a semantic caching layer for reducing LLM costs.",
        "type": "definition"
    },
    {
        "query": "Scrub this email: test@example.com",
        "expected": "[[MASKED_EMAIL]]",
        "type": "tool_execution"
    }
]

class QualityJudge:
    """Mock Judge LLM following Google ADK Evaluation standards."""
    
    @staticmethod
    async def score_response(actual: str, expected: str, metric: str = "similarity") -> float:
        await asyncio.sleep(0.1)
        # In production, this calls Vertex AI Evaluation Service (ADK)
        # Metrics: Response Match Score, Tool Trajectory Score
        return random.uniform(0.7, 0.95)

async def run_iteration(iteration: int, prompt_variant: str) -> float:
    """Run a single evaluation pass against the golden dataset."""
    import json
    dataset = GOLDEN_DATASET
    if os.path.exists("src/agent_ops_cockpit/tests/golden_set.json"):
        try:
            with open("src/agent_ops_cockpit/tests/golden_set.json", "r") as f:
                dataset = json.load(f)
        except Exception:
            pass

    scores = []
    for item in dataset:
        # Simulate agent execution
        actual_response = f"Simulated response for: {item['query']}"
        
        # Tool Trajectory Check: If the query is tool-based, mock a trajectory score
        trajectory_score = 1.0
        if item.get("type") == "tool_execution":
             trajectory_score = random.uniform(0.8, 1.0)
             
        match_score = await QualityJudge.score_response(actual_response, item["expected"])
        
        # 70% Match Score, 30% Trajectory Score
        final_score = (match_score * 0.7) + (trajectory_score * 0.3)
        scores.append(final_score)
    
    avg = sum(scores) / len(scores)
    return avg

@app.command()
def climb(
    steps: int = typer.Option(3, help="Number of hill-climbing iterations"),
    threshold: float = typer.Option(0.9, help="Target quality score (0.0 - 1.0)")
):
    """
    Quality Hill Climbing: Iteratively optimizes agent prompts/blueprints to reach a quality peak.
    Calculates ADK-style metrics (Response Match & Tool Trajectory).
    """
    console.print(Panel.fit(
        "🧗 [bold cyan]QUALITY HILL CLIMBING: ADK EVALUATION SUITE[/bold cyan]\nIteratively optimizing for Response Match & Tool Trajectory...",
        border_style="cyan"
    ))

    current_score = 0.75 # Initial baseline
    best_score = current_score
    history = []

    with Progress(
        SpinnerColumn(),
        TextColumn("[progress.description]{task.description}"),
        BarColumn(),
        TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
        console=console
    ) as progress:
        task = progress.add_task("[yellow]Climbing the quality curve...", total=steps)
        
        for i in range(1, steps + 1):
            # Simulated 'Neighbor Generation' (Modifying prompts/instructions)
            progress.update(task, description=f"[yellow]Iteration {i}: Optimizing Prompt Variant...")
            
            # Run evaluation iteration
            new_score = asyncio.run(run_iteration(i, f"variant_{i}"))
            
            # Selection: Move to the better neighbor
            improvement = new_score - best_score
            if new_score > best_score:
                best_score = new_score
                status = "[bold green]IMPROVED[/bold green]"
            else:
                status = "[red]REGRESSION[/red]"
            
            history.append({"iter": i, "score": new_score, "status": status, "improvement": improvement})
            progress.update(task, advance=1)
            
            if best_score >= threshold:
                console.print(f"\n🎯 [bold green]Target Quality ({threshold*100}%) Reached at Iteration {i}![/bold green]")
                break

    # Summary Table
    table = Table(title="📈 Hill Climbing Optimization History")
    table.add_column("Iter", justify="center")
    table.add_column("Score", justify="right")
    table.add_column("Status", justify="center")
    table.add_column("Improvement", justify="right")

    for h in history:
        color = "green" if h["improvement"] > 0 else "red"
        table.add_row(
            str(h["iter"]), 
            f"{h['score']*100:.1f}%", 
            h["status"], 
            f"[{color}]+{h['improvement']*100:.1f}%[/{color}]" if h["improvement"] > 0 else f"[red]{h['improvement']*100:.1f}%[/red]"
        )

    console.print(table)
    
    if best_score >= threshold:
        console.print(f"\n✅ [bold green]SUCCESS:[/bold green] High-fidelity agent stabilized at {best_score*100:.1f}%.")
        console.print("🚀 Final blueprint is ready for deployment.")
    else:
        console.print(f"\n⚠️ [bold yellow]WARNING:[/bold yellow] Failed to reach global peak. Current quality: {best_score*100:.1f}%.")
        console.print("💡 Try expanding the Golden Dataset or using a stronger Judge LLM.")

if __name__ == "__main__":
    app()
