src/cli/commands.py

"""CLI Commands - run, train, eval"""

import json
import time
from pathlib import Path
from typing import Optional

import click
from rich.console import Console
from rich.progress import Progress, SpinnerColumn, TextColumn

from src.core import CoderAgent, ResponseValidator
from src.animations import Spinner, ProgressBar
from src.knowledge import LocalKB


console = Console()


@click.command()
@click.argument("instruction")
@click.option("--model", default="gpt-4", help="AI model")
@click.option("--output", "-o", help="Output file for response")
@click.option("--validate/--no-validate", default=True, help="Validate response")
def run_command(instruction: str, model: str, output: Optional[str], validate: bool):
    """Run a single instruction and display response"""
    agent = CoderAgent(model=model)
    validator = ResponseValidator()

    console.print(f"[bold cyan]Question:[/bold cyan] {instruction}\n")

    with Spinner("Generating response..."):
        response = agent.generate_response(instruction)

    console.print("[bold green]Response:[/bold green]")
    console.print(response["response"])

    if validate:
        console.print("\n[bold yellow]Validating...[/bold yellow]")
        result = validator.validate(response["response"], instruction)
        console.print(f"Quality: [bold]{result.quality.value}[/bold]")
        console.print(f"Score: [bold]{result.score:.2f}[/bold]")

        if result.issues:
            console.print("[bold red]Issues:[/bold red]")
            for issue in result.issues:
                console.print(f"  - {issue}")

    if output:
        Path(output).write_text(response["response"], encoding="utf-8")
        console.print(f"\n[green]Response saved to {output}[/green]")


@click.command()
@click.argument("data_dir")
@click.option("--epochs", default=10, help="Training epochs")
@click.option("--batch-size", default=4, help="Batch size")
def train_command(data_dir: str, epochs: int, batch_size: int):
    """Train agent on trajectory data"""
    console.print("[bold cyan]Training Agent...[/bold cyan]\n")

    data_path = Path(data_dir)
    if not data_path.exists():
        console.print(f"[bold red]Error:[/bold red] {data_dir} not found")
        return

    trajectory_files = list(data_path.glob("*.jsonl"))
    if not trajectory_files:
        console.print(f"[bold red]Error:[/bold red] No trajectory files in {data_dir}")
        return

    console.print(f"Found {len(trajectory_files)} trajectory files\n")

    for epoch in range(epochs):
        console.print(f"[bold cyan]Epoch {epoch + 1}/{epochs}[/bold cyan]")

        with Progress(
            SpinnerColumn(),
            TextColumn("[progress.description]{task.description}"),
            console=console,
        ) as progress:
            task = progress.add_task("Processing...", total=len(trajectory_files))

            for traj_file in trajectory_files:
                progress.update(task, advance=1)

                with open(traj_file, "r", encoding="utf-8") as f:
                    for line in f:
                        data = json.loads(line)
                        # Process trajectory data
                        pass

        time.sleep(0.5)

    console.print("\n[bold green]Training completed![/bold green]")


@click.command()
@click.argument("data_dir")
@click.option("--verbose", is_flag=True, help="Show detailed results")
def eval_command(data_dir: str, verbose: bool):
    """Evaluate agent on test data"""
    console.print("[bold cyan]Evaluating Agent...[/bold cyan]\n")

    data_path = Path(data_dir)
    if not data_path.exists():
        console.print(f"[bold red]Error:[/bold red] {data_dir} not found")
        return

    kb = LocalKB()

    test_queries = [
        "Python decorator hta ya py",
        "JavaScript async/await hta ya",
        "SQL JOIN operations hta ya",
        "React useState lo useEffect hta ya",
    ]

    agent = CoderAgent()
    validator = ResponseValidator()

    results = []
    with Progress(
        SpinnerColumn(), TextColumn("[progress.description]{task.description}"), console=console
    ) as progress:
        task = progress.add_task("Testing...", total=len(test_queries))

        for query in test_queries:
            progress.update(task, description=f"Testing: {query[:30]}...")

            response = agent.generate_response(query)
            validation = validator.validate(response["response"], query)

            results.append(
                {
                    "query": query,
                    "quality": validation.quality.value,
                    "score": validation.score,
                    "issues": len(validation.issues),
                }
            )

            progress.update(task, advance=1)

    console.print("\n[bold]Evaluation Results:[/bold]\n")

    total_score = sum(r["score"] for r in results)
    avg_score = total_score / len(results) if results else 0

    console.print(f"Average Score: [bold]{avg_score:.2f}[/bold]\n")

    for r in results:
        color = "green" if r["score"] >= 0.6 else "yellow" if r["score"] >= 0.4 else "red"
        console.print(f"[{color}]{r['query']}:[/{color}] {r['score']:.2f} ({r['quality']})")

        if verbose and r["issues"] > 0:
            console.print(f"  Issues: {r['issues']}")


@click.command()
@click.argument("query")
def search_command(query: str):
    """Search knowledge base"""
    kb = LocalKB()
    results = kb.search(query)

    if not results:
        console.print("[yellow]No results found[/yellow]")
        return

    for result in results:
        console.print(f"[bold cyan]{result['source']}[/bold cyan]")
        console.print(result["content"][:200])
        console.print()


@click.command()
@click.argument("question")
def ask_command(question: str):
    """Ask a question (alias for run)"""
    run_command.callback(question, model="gpt-4", output=None, validate=True)