package reasoning

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
	"strings"
	"time"

	"chorus/pkg/mcp"
)

const (
	defaultTimeout  = 60 * time.Second
)

var (
    availableModels []string
    modelWebhookURL string
    defaultModel    string
    ollamaEndpoint  string = "http://localhost:11434" // Default fallback
    aiProvider      string = "resetdata"              // Default provider
    resetdataConfig ResetDataConfig
    defaultSystemPrompt string
    lightragClient  *mcp.LightRAGClient // Optional LightRAG client for context enrichment
)

// AIProvider represents the AI service provider
type AIProvider string

const (
	ProviderOllama    AIProvider = "ollama"
	ProviderResetData AIProvider = "resetdata"
)

// ResetDataConfig holds resetdata API configuration
type ResetDataConfig struct {
	BaseURL string
	APIKey  string
	Model   string
	Timeout time.Duration
}

// OllamaRequest represents the request payload for the Ollama API.
type OllamaRequest struct {
	Model  string `json:"model"`
	Prompt string `json:"prompt"`
	Stream bool   `json:"stream"`
}

// OllamaResponse represents a single streamed response object from the Ollama API.
type OllamaResponse struct {
	Model     string    `json:"model"`
	CreatedAt time.Time `json:"created_at"`
	Response  string    `json:"response"`
	Done      bool      `json:"done"`
}

// OpenAIMessage represents a message in the OpenAI API format
type OpenAIMessage struct {
	Role    string `json:"role"`
	Content string `json:"content"`
}

// OpenAIRequest represents the request payload for OpenAI-compatible APIs
type OpenAIRequest struct {
	Model       string          `json:"model"`
	Messages    []OpenAIMessage `json:"messages"`
	Temperature float64         `json:"temperature"`
	TopP        float64         `json:"top_p"`
	MaxTokens   int             `json:"max_tokens"`
	Stream      bool            `json:"stream"`
}

// OpenAIChoice represents a choice in the OpenAI response
type OpenAIChoice struct {
	Message struct {
		Content string `json:"content"`
	} `json:"message"`
}

// OpenAIResponse represents the response from OpenAI-compatible APIs
type OpenAIResponse struct {
	Choices []OpenAIChoice `json:"choices"`
}

// GenerateResponse queries the configured AI provider with a given prompt and model,
// and returns the complete generated response as a single string.
func GenerateResponse(ctx context.Context, model, prompt string) (string, error) {
	// Set up a timeout for the request
	ctx, cancel := context.WithTimeout(ctx, defaultTimeout)
	defer cancel()

	switch AIProvider(aiProvider) {
	case ProviderResetData:
		return generateResetDataResponse(ctx, model, prompt)
	case ProviderOllama:
		return generateOllamaResponse(ctx, model, prompt)
	default:
		// Default to ResetData if unknown provider
		return generateResetDataResponse(ctx, model, prompt)
	}
}

// generateResetDataResponse queries the ResetData API
func generateResetDataResponse(ctx context.Context, model, prompt string) (string, error) {
	if resetdataConfig.APIKey == "" {
		return "", fmt.Errorf("resetdata API key not configured")
	}

	// Use the configured model if provided, otherwise use the one passed in
	modelToUse := model
	if resetdataConfig.Model != "" {
		modelToUse = resetdataConfig.Model
	}
	// Ensure the model has the correct format for ResetData
	if !strings.Contains(modelToUse, ":") && !strings.Contains(modelToUse, "/") {
		modelToUse = resetdataConfig.Model // Fallback to configured model
	}

	// Create the request payload in OpenAI format
    requestPayload := OpenAIRequest{
        Model: modelToUse,
        Messages: []OpenAIMessage{
            {Role: "system", Content: defaultSystemPromptOrFallback()},
            {Role: "user", Content: prompt},
        },
        Temperature: 0.2,
        TopP:        0.7,
        MaxTokens:   1024,
        Stream:      false,
    }

	payloadBytes, err := json.Marshal(requestPayload)
	if err != nil {
		return "", fmt.Errorf("failed to marshal resetdata request: %w", err)
	}

	// Create the HTTP request
	apiURL := resetdataConfig.BaseURL + "/chat/completions"
	req, err := http.NewRequestWithContext(ctx, "POST", apiURL, bytes.NewBuffer(payloadBytes))
	if err != nil {
		return "", fmt.Errorf("failed to create http request: %w", err)
	}
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Authorization", "Bearer "+resetdataConfig.APIKey)

	// Execute the request
	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		return "", fmt.Errorf("failed to execute http request to resetdata: %w", err)
	}
	defer resp.Body.Close()

	// Check for non-200 status codes
	if resp.StatusCode != http.StatusOK {
		bodyBytes, _ := io.ReadAll(resp.Body)
		return "", fmt.Errorf("resetdata api returned non-200 status: %d - %s", resp.StatusCode, string(bodyBytes))
	}

	// Decode the JSON response
	var openaiResp OpenAIResponse
	if err := json.NewDecoder(resp.Body).Decode(&openaiResp); err != nil {
		return "", fmt.Errorf("failed to decode resetdata response: %w", err)
	}

	if len(openaiResp.Choices) == 0 {
		return "", fmt.Errorf("no choices in resetdata response")
	}

	return openaiResp.Choices[0].Message.Content, nil
}

// generateOllamaResponse queries the Ollama API (legacy support)
func generateOllamaResponse(ctx context.Context, model, prompt string) (string, error) {
	// Create the request payload
	requestPayload := OllamaRequest{
		Model:  model,
		Prompt: prompt,
		Stream: false, // We will handle the full response at once for simplicity
	}

	payloadBytes, err := json.Marshal(requestPayload)
	if err != nil {
		return "", fmt.Errorf("failed to marshal ollama request: %w", err)
	}

	// Create the HTTP request
	apiURL := ollamaEndpoint + "/api/generate"
	req, err := http.NewRequestWithContext(ctx, "POST", apiURL, bytes.NewBuffer(payloadBytes))
	if err != nil {
		return "", fmt.Errorf("failed to create http request: %w", err)
	}
	req.Header.Set("Content-Type", "application/json")

	// Execute the request
	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		return "", fmt.Errorf("failed to execute http request to ollama: %w", err)
	}
	defer resp.Body.Close()

	// Check for non-200 status codes
	if resp.StatusCode != http.StatusOK {
		bodyBytes, _ := io.ReadAll(resp.Body)
		return "", fmt.Errorf("ollama api returned non-200 status: %d - %s", resp.StatusCode, string(bodyBytes))
	}

	// Decode the JSON response
	var ollamaResp OllamaResponse
	if err := json.NewDecoder(resp.Body).Decode(&ollamaResp); err != nil {
		return "", fmt.Errorf("failed to decode ollama response: %w", err)
	}

	return ollamaResp.Response, nil
}

// SetModelConfig configures the available models and webhook URL for smart model selection
func SetModelConfig(models []string, webhookURL, defaultReasoningModel string) {
	availableModels = models
	modelWebhookURL = webhookURL
	defaultModel = defaultReasoningModel
}

// SetAIProvider configures which AI provider to use
func SetAIProvider(provider string) {
	aiProvider = provider
}

// SetResetDataConfig configures the ResetData API settings
func SetResetDataConfig(config ResetDataConfig) {
	resetdataConfig = config
}

// SetOllamaEndpoint configures the Ollama API endpoint
func SetOllamaEndpoint(endpoint string) {
    ollamaEndpoint = endpoint
}

// SetDefaultSystemPrompt configures the default system message used when building prompts.
func SetDefaultSystemPrompt(systemPrompt string) {
    defaultSystemPrompt = systemPrompt
}

// SetLightRAGClient configures the optional LightRAG client for context enrichment
func SetLightRAGClient(client *mcp.LightRAGClient) {
    lightragClient = client
}

// GenerateResponseWithRAG queries LightRAG for context, then generates a response
// enriched with relevant information from the knowledge base
func GenerateResponseWithRAG(ctx context.Context, model, prompt string, queryMode mcp.QueryMode) (string, error) {
	// If LightRAG is not configured, fall back to regular generation
	if lightragClient == nil {
		return GenerateResponse(ctx, model, prompt)
	}

	// Query LightRAG for relevant context
	ragCtx, err := lightragClient.GetContext(ctx, prompt, queryMode)
	if err != nil {
		// Log the error but continue with regular generation
		// This makes LightRAG failures non-fatal
		return GenerateResponse(ctx, model, prompt)
	}

	// If we got context, enrich the prompt
	enrichedPrompt := prompt
	if strings.TrimSpace(ragCtx) != "" {
		enrichedPrompt = fmt.Sprintf("Context from knowledge base:\n%s\n\nUser query:\n%s", ragCtx, prompt)
	}

	// Generate response with enriched context
	return GenerateResponse(ctx, model, enrichedPrompt)
}

// GenerateResponseSmartWithRAG combines smart model selection with RAG context enrichment
func GenerateResponseSmartWithRAG(ctx context.Context, prompt string, queryMode mcp.QueryMode) (string, error) {
	selectedModel := selectBestModel(availableModels, prompt)
	return GenerateResponseWithRAG(ctx, selectedModel, prompt, queryMode)
}

// selectBestModel calls the model selection webhook to choose the best model for a prompt
func selectBestModel(availableModels []string, prompt string) string {
	if modelWebhookURL == "" || len(availableModels) == 0 {
		// Fallback to first available model
		if len(availableModels) > 0 {
			return availableModels[0]
		}
		return defaultModel // Last resort fallback
	}
	
	requestPayload := map[string]interface{}{
		"models": availableModels,
		"prompt": prompt,
	}
	
	payloadBytes, err := json.Marshal(requestPayload)
	if err != nil {
		// Fallback on error
		return availableModels[0]
	}
	
	resp, err := http.Post(modelWebhookURL, "application/json", bytes.NewBuffer(payloadBytes))
	if err != nil {
		// Fallback on error
		return availableModels[0]
	}
	defer resp.Body.Close()
	
	if resp.StatusCode != http.StatusOK {
		// Fallback on error
		return availableModels[0]
	}
	
	var response struct {
		Model string `json:"model"`
	}
	
	if err := json.NewDecoder(resp.Body).Decode(&response); err != nil {
		// Fallback on error
		return availableModels[0]
	}
	
	// Validate that the returned model is in our available list
	for _, model := range availableModels {
		if model == response.Model {
			return response.Model
		}
	}
	
	// Fallback if webhook returned invalid model
	return availableModels[0]
}

// GenerateResponseSmart automatically selects the best model for the prompt
func GenerateResponseSmart(ctx context.Context, prompt string) (string, error) {
    selectedModel := selectBestModel(availableModels, prompt)
    return GenerateResponse(ctx, selectedModel, prompt)
}

func defaultSystemPromptOrFallback() string {
    if strings.TrimSpace(defaultSystemPrompt) != "" {
        return defaultSystemPrompt
    }
    return "You are a helpful assistant."
}