import asyncio
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
from typing import List, Dict, Any

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(0.3)
        # 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."""
    scores = []
    for item in GOLDEN_DATASET:
        # Simulate agent execution
        actual_response = f"Simulated response for: {item['query']}"
        score = await QualityJudge.score_response(actual_response, item["expected"])
        scores.append(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()
