package go_llm

import (
	"context"
	"encoding/base64"
	"encoding/json"
	"fmt"
	"io"
	"net/http"

	"github.com/google/generative-ai-go/genai"
	"google.golang.org/api/option"
)

type google struct {
	key   string
	model string
}

func (g google) ModelVersion(modelVersion string) (ChatCompletion, error) {
	g.model = modelVersion

	return g, nil
}

func (g google) requestToChatHistory(in Request, model *genai.GenerativeModel) (*genai.GenerativeModel, *genai.ChatSession, []genai.Part) {
	res := *model

	for _, tool := range in.Toolbox.functions {
		res.Tools = append(res.Tools, &genai.Tool{
			FunctionDeclarations: []*genai.FunctionDeclaration{
				{
					Name:        tool.Name,
					Description: tool.Description,
					Parameters:  tool.Parameters.GoogleParameters(),
				},
			},
		})
	}

	if !in.Toolbox.RequiresTool() {
		res.ToolConfig = &genai.ToolConfig{FunctionCallingConfig: &genai.FunctionCallingConfig{
			Mode: genai.FunctionCallingAny,
		}}
	}

	cs := res.StartChat()

	for i, c := range in.Messages {
		content := genai.NewUserContent(genai.Text(c.Text))

		switch c.Role {
		case RoleAssistant, RoleSystem:
			content.Role = "model"

		case RoleUser:
			content.Role = "user"
		}

		for _, img := range c.Images {
			if img.Url != "" {
				// gemini does not support URLs, so we need to download the image and convert it to a blob

				// Download the image from the URL
				resp, err := http.Get(img.Url)
				if err != nil {
					panic(fmt.Sprintf("error downloading image: %v", err))
				}
				defer resp.Body.Close()

				// Check the Content-Length to ensure it's not over 20MB
				if resp.ContentLength > 20*1024*1024 {
					panic(fmt.Sprintf("image size exceeds 20MB: %d bytes", resp.ContentLength))
				}

				// Read the content into a byte slice
				data, err := io.ReadAll(resp.Body)
				if err != nil {
					panic(fmt.Sprintf("error reading image data: %v", err))
				}

				// Ensure the MIME type is appropriate
				mimeType := http.DetectContentType(data)
				switch mimeType {
				case "image/jpeg", "image/png", "image/gif":
					// MIME type is valid
				default:
					panic(fmt.Sprintf("unsupported image MIME type: %s", mimeType))
				}

				// Create a genai.Blob using the validated image data
				content.Parts = append(content.Parts, genai.Blob{
					MIMEType: mimeType,
					Data:     data,
				})

			} else {
				// convert base64 to blob
				b, e := base64.StdEncoding.DecodeString(img.Base64)
				if e != nil {
					panic(fmt.Sprintf("error decoding base64: %v", e))
				}

				content.Parts = append(content.Parts, genai.Blob{
					MIMEType: img.ContentType,
					Data:     b,
				})
			}
		}

		// if this is the last message, we want to add to history, we want it to be the parts
		if i == len(in.Messages)-1 {
			return &res, cs, content.Parts
		}

		cs.History = append(cs.History, content)
	}

	return &res, cs, nil
}

func (g google) responseToLLMResponse(in *genai.GenerateContentResponse) (Response, error) {
	res := Response{}

	for _, c := range in.Candidates {
		var choice ResponseChoice
		var set = false
		if c.Content != nil {
			for _, p := range c.Content.Parts {
				switch p.(type) {
				case genai.Text:
					choice.Content = string(p.(genai.Text))
					set = true

				case genai.FunctionCall:
					v := p.(genai.FunctionCall)
					b, e := json.Marshal(v.Args)

					if e != nil {
						return Response{}, fmt.Errorf("error marshalling args: %w", e)
					}

					call := ToolCall{
						ID: v.Name,
						FunctionCall: FunctionCall{
							Name:      v.Name,
							Arguments: string(b),
						},
					}

					choice.Calls = append(choice.Calls, call)
					set = true
				default:
					return Response{}, fmt.Errorf("unknown part type: %T", p)
				}
			}
		}

		if set {
			choice.Role = RoleAssistant
			res.Choices = append(res.Choices, choice)
		}
	}

	return res, nil
}

func (g google) ChatComplete(ctx context.Context, req Request) (Response, error) {
	cl, err := genai.NewClient(ctx, option.WithAPIKey(g.key))

	if err != nil {
		return Response{}, fmt.Errorf("error creating genai client: %w", err)
	}

	model := cl.GenerativeModel(g.model)

	_, cs, parts := g.requestToChatHistory(req, model)

	resp, err := cs.SendMessage(ctx, parts...)

	//parts := g.requestToGoogleRequest(req, model)

	//resp, err := model.GenerateContent(ctx, parts...)

	if err != nil {
		return Response{}, fmt.Errorf("error generating content: %w", err)
	}

	return g.responseToLLMResponse(resp)
}