package executor

import (
	"context"
	"encoding/json"
	"fmt"
	"os/exec"
	"strings"

	"github.com/thepeterstone/claudomator/internal/llm"
)

type Classification struct {
	AgentType string `json:"agent_type"`
	Model     string `json:"model"`
	Reason    string `json:"reason"`
}

type SystemStatus struct {
	ActiveTasks map[string]int
	RateLimited map[string]bool
}

// Classifier picks a model for an incoming task. When LLM is non-nil the
// classifier routes through the local OpenAI-compatible client (cheap,
// private, fast). Otherwise it falls back to invoking the Gemini CLI
// at GeminiBinaryPath.
type Classifier struct {
	LLM              *llm.Client
	GeminiBinaryPath string
}

const classificationPrompt = `
You are a model selector for Claudomator.
The agent has already been chosen by the load balancer. Your ONLY job is to select the best model for that agent.

REQUIRED agent: %s

Available Models:
Claude:
- claude-sonnet-4-6 (default, balanced, best for most coding tasks)
- claude-opus-4-6 (most powerful, expensive, use for hardest tasks only)
- claude-haiku-4-5-20251001 (fast, cheap, use for simple tasks)

Gemini:
- gemini-2.5-flash-lite (fastest, most efficient, best for simple/trivial tasks)
- gemini-2.5-flash (fast, balanced)
- gemini-2.5-pro (most powerful, use for hardest tasks only)

Selection Criteria:
- Use powerful models (opus, pro) only for the hardest reasoning/coding tasks.
- Use lite/haiku for simple, short, or low-stakes tasks.
- Default to the balanced model (sonnet, flash) for everything else.

Task:
Name: %s
Instructions: %s

Respond with ONLY a JSON object:
{
  "agent_type": "%s",
  "model": "model-name",
  "reason": "brief reason"
}
`

func (c *Classifier) Classify(ctx context.Context, taskName, instructions string, _ SystemStatus, agentType string) (*Classification, error) {
	prompt := fmt.Sprintf(classificationPrompt,
		agentType, taskName, instructions, agentType,
	)

	if c.LLM != nil {
		return c.classifyViaLLM(ctx, prompt, agentType)
	}

	binary := c.GeminiBinaryPath
	if binary == "" {
		binary = "gemini"
	}

	// Use a minimal model for classification to be fast and cheap.
	args := []string{
		"--prompt", prompt,
		"--model", "gemini-2.5-flash-lite",
		"--output-format", "json",
	}

	cmd := exec.CommandContext(ctx, binary, args...)
	out, err := cmd.Output()
	if err != nil {
		if exitErr, ok := err.(*exec.ExitError); ok {
			return nil, fmt.Errorf("classifier failed (%v): %s", err, string(exitErr.Stderr))
		}
		return nil, fmt.Errorf("classifier failed: %w", err)
	}

	// 1. Parse the JSON envelope from the gemini CLI.
	var cliOut struct {
		Response string `json:"response"`
	}
	if err := json.Unmarshal(out, &cliOut); err != nil {
		// If it's not JSON, it might be raw text (though we requested JSON).
		// This can happen if the CLI prints "Loaded cached credentials" or other info.
		cliOut.Response = string(out)
	}

	// 2. Extract the model response from the "response" field if present.
	// If it was already raw text, cliOut.Response will have it.
	cleanOut := strings.TrimSpace(cliOut.Response)

	// 3. Clean up "Loaded cached credentials" or other noise that might be in the string
	// if we fell back to string(out).
	if strings.Contains(cleanOut, "Loaded cached credentials.") {
		lines := strings.Split(cleanOut, "\n")
		var modelLines []string
		for _, line := range lines {
			if !strings.Contains(line, "Loaded cached credentials.") {
				modelLines = append(modelLines, line)
			}
		}
		cleanOut = strings.TrimSpace(strings.Join(modelLines, "\n"))
	}

	// 4. Gemini might wrap the JSON in markdown code blocks.
	cleanOut = strings.TrimPrefix(cleanOut, "```json")
	cleanOut = strings.TrimPrefix(cleanOut, "```") // fallback
	cleanOut = strings.TrimSuffix(cleanOut, "```")
	cleanOut = strings.TrimSpace(cleanOut)

	var cls Classification
	if err := json.Unmarshal([]byte(cleanOut), &cls); err != nil {
		return nil, fmt.Errorf("failed to parse classification JSON: %w\nOriginal Output: %s\nCleaned Output: %s", err, string(out), cleanOut)
	}

	return &cls, nil
}

// classifyViaLLM routes classification through the local OpenAI-compatible
// client with response_format=json_object, so we get clean JSON without the
// markdown-fence cleanup needed for the Gemini CLI fallback.
func (c *Classifier) classifyViaLLM(ctx context.Context, prompt, agentType string) (*Classification, error) {
	resp, err := c.LLM.Chat(ctx, llm.ChatRequest{
		Messages:     []llm.Message{{Role: "user", Content: prompt}},
		ResponseJSON: true,
	})
	if err != nil {
		return nil, fmt.Errorf("classifier (local llm): %w", err)
	}
	body := strings.TrimSpace(resp.Content)
	var cls Classification
	if err := json.Unmarshal([]byte(body), &cls); err != nil {
		return nil, fmt.Errorf("classifier (local llm): parse JSON: %w\nbody: %s", err, body)
	}
	if cls.AgentType == "" {
		cls.AgentType = agentType
	}
	return &cls, nil
}