package openai

import (
	"bufio"
	"bytes"
	"encoding/json"
	"fmt"
	"strings"
	"time"

	"github.com/gofiber/fiber/v2"
	"github.com/google/uuid"
	"github.com/mudler/LocalAI/core/backend"
	"github.com/mudler/LocalAI/core/config"
	"github.com/mudler/LocalAI/core/schema"
	"github.com/mudler/LocalAI/pkg/functions"
	model "github.com/mudler/LocalAI/pkg/model"
	"github.com/rs/zerolog/log"
	"github.com/valyala/fasthttp"
)

// ChatEndpoint is the OpenAI Completion API endpoint https://platform.openai.com/docs/api-reference/chat/create
// @Summary Generate a chat completions for a given prompt and model.
// @Param request body schema.OpenAIRequest true "query params"
// @Success 200 {object} schema.OpenAIResponse "Response"
// @Router /v1/chat/completions [post]
func ChatEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, startupOptions *config.ApplicationConfig) func(c *fiber.Ctx) error {
	textContentToReturn := ""
	id := uuid.New().String()
	created := int(time.Now().Unix())

	process := func(s string, req *schema.OpenAIRequest, config *config.BackendConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse) {
		initialMessage := schema.OpenAIResponse{
			ID:      id,
			Created: created,
			Model:   req.Model, // we have to return what the user sent here, due to OpenAI spec.
			Choices: []schema.Choice{{Delta: &schema.Message{Role: "assistant", Content: &textContentToReturn}}},
			Object:  "chat.completion.chunk",
		}
		responses <- initialMessage

		ComputeChoices(req, s, config, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
			resp := schema.OpenAIResponse{
				ID:      id,
				Created: created,
				Model:   req.Model, // we have to return what the user sent here, due to OpenAI spec.
				Choices: []schema.Choice{{Delta: &schema.Message{Content: &s}, Index: 0}},
				Object:  "chat.completion.chunk",
				Usage: schema.OpenAIUsage{
					PromptTokens:     usage.Prompt,
					CompletionTokens: usage.Completion,
					TotalTokens:      usage.Prompt + usage.Completion,
				},
			}

			responses <- resp
			return true
		})
		close(responses)
	}
	processTools := func(noAction string, prompt string, req *schema.OpenAIRequest, config *config.BackendConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse) {
		result := ""
		_, tokenUsage, _ := ComputeChoices(req, prompt, config, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
			result += s
			// TODO: Change generated BNF grammar to be compliant with the schema so we can
			// stream the result token by token here.
			return true
		})

		textContentToReturn = functions.ParseTextContent(result, config.FunctionsConfig)
		result = functions.CleanupLLMResult(result, config.FunctionsConfig)
		results := functions.ParseFunctionCall(result, config.FunctionsConfig)
		log.Debug().Msgf("Text content to return: %s", textContentToReturn)
		noActionToRun := len(results) > 0 && results[0].Name == noAction || len(results) == 0

		switch {
		case noActionToRun:
			initialMessage := schema.OpenAIResponse{
				ID:      id,
				Created: created,
				Model:   req.Model, // we have to return what the user sent here, due to OpenAI spec.
				Choices: []schema.Choice{{Delta: &schema.Message{Role: "assistant", Content: &textContentToReturn}}},
				Object:  "chat.completion.chunk",
			}
			responses <- initialMessage

			result, err := handleQuestion(config, req, ml, startupOptions, results, result, prompt)
			if err != nil {
				log.Error().Err(err).Msg("error handling question")
				return
			}

			resp := schema.OpenAIResponse{
				ID:      id,
				Created: created,
				Model:   req.Model, // we have to return what the user sent here, due to OpenAI spec.
				Choices: []schema.Choice{{Delta: &schema.Message{Content: &result}, Index: 0}},
				Object:  "chat.completion.chunk",
				Usage: schema.OpenAIUsage{
					PromptTokens:     tokenUsage.Prompt,
					CompletionTokens: tokenUsage.Completion,
					TotalTokens:      tokenUsage.Prompt + tokenUsage.Completion,
				},
			}

			responses <- resp

		default:
			for i, ss := range results {
				name, args := ss.Name, ss.Arguments

				initialMessage := schema.OpenAIResponse{
					ID:      id,
					Created: created,
					Model:   req.Model, // we have to return what the user sent here, due to OpenAI spec.
					Choices: []schema.Choice{{
						Delta: &schema.Message{
							Role: "assistant",
							ToolCalls: []schema.ToolCall{
								{
									Index: i,
									ID:    id,
									Type:  "function",
									FunctionCall: schema.FunctionCall{
										Name: name,
									},
								},
							},
						}}},
					Object: "chat.completion.chunk",
				}
				responses <- initialMessage

				responses <- schema.OpenAIResponse{
					ID:      id,
					Created: created,
					Model:   req.Model, // we have to return what the user sent here, due to OpenAI spec.
					Choices: []schema.Choice{{
						Delta: &schema.Message{
							Role:    "assistant",
							Content: &textContentToReturn,
							ToolCalls: []schema.ToolCall{
								{
									Index: i,
									ID:    id,
									Type:  "function",
									FunctionCall: schema.FunctionCall{
										Arguments: args,
									},
								},
							},
						}}},
					Object: "chat.completion.chunk",
				}
			}
		}

		close(responses)
	}

	return func(c *fiber.Ctx) error {
		modelFile, input, err := readRequest(c, cl, ml, startupOptions, true)
		if err != nil {
			return fmt.Errorf("failed reading parameters from request:%w", err)
		}

		config, input, err := mergeRequestWithConfig(modelFile, input, cl, ml, startupOptions.Debug, startupOptions.Threads, startupOptions.ContextSize, startupOptions.F16)
		if err != nil {
			return fmt.Errorf("failed reading parameters from request:%w", err)
		}
		log.Debug().Msgf("Configuration read: %+v", config)

		funcs := input.Functions
		shouldUseFn := len(input.Functions) > 0 && config.ShouldUseFunctions()
		strictMode := false

		for _, f := range input.Functions {
			if f.Strict {
				strictMode = true
				break
			}
		}

		// Allow the user to set custom actions via config file
		// to be "embedded" in each model
		noActionName := "answer"
		noActionDescription := "use this action to answer without performing any action"

		if config.FunctionsConfig.NoActionFunctionName != "" {
			noActionName = config.FunctionsConfig.NoActionFunctionName
		}
		if config.FunctionsConfig.NoActionDescriptionName != "" {
			noActionDescription = config.FunctionsConfig.NoActionDescriptionName
		}

		if config.ResponseFormatMap != nil {
			d := schema.ChatCompletionResponseFormat{}
			dat, err := json.Marshal(config.ResponseFormatMap)
			if err != nil {
				return err
			}
			err = json.Unmarshal(dat, &d)
			if err != nil {
				return err
			}
			if d.Type == "json_object" {
				input.Grammar = functions.JSONBNF
			} else if d.Type == "json_schema" {
				d := schema.JsonSchemaRequest{}
				dat, err := json.Marshal(config.ResponseFormatMap)
				if err != nil {
					return err
				}
				err = json.Unmarshal(dat, &d)
				if err != nil {
					return err
				}
				fs := &functions.JSONFunctionStructure{
					AnyOf: []functions.Item{d.JsonSchema.Schema},
				}
				g, err := fs.Grammar(config.FunctionsConfig.GrammarOptions()...)
				if err == nil {
					input.Grammar = g
				}
			}
		}

		config.Grammar = input.Grammar

		if shouldUseFn {
			log.Debug().Msgf("Response needs to process functions")
		}

		switch {
		case (!config.FunctionsConfig.GrammarConfig.NoGrammar || strictMode) && shouldUseFn:
			noActionGrammar := functions.Function{
				Name:        noActionName,
				Description: noActionDescription,
				Parameters: map[string]interface{}{
					"properties": map[string]interface{}{
						"message": map[string]interface{}{
							"type":        "string",
							"description": "The message to reply the user with",
						}},
				},
			}

			// Append the no action function
			if !config.FunctionsConfig.DisableNoAction {
				funcs = append(funcs, noActionGrammar)
			}

			// Force picking one of the functions by the request
			if config.FunctionToCall() != "" {
				funcs = funcs.Select(config.FunctionToCall())
			}

			// Update input grammar
			jsStruct := funcs.ToJSONStructure(config.FunctionsConfig.FunctionNameKey, config.FunctionsConfig.FunctionNameKey)
			g, err := jsStruct.Grammar(config.FunctionsConfig.GrammarOptions()...)
			if err == nil {
				config.Grammar = g
			}
		case input.JSONFunctionGrammarObject != nil:
			g, err := input.JSONFunctionGrammarObject.Grammar(config.FunctionsConfig.GrammarOptions()...)
			if err == nil {
				config.Grammar = g
			}
		default:
			// Force picking one of the functions by the request
			if config.FunctionToCall() != "" {
				funcs = funcs.Select(config.FunctionToCall())
			}
		}

		// process functions if we have any defined or if we have a function call string

		// functions are not supported in stream mode (yet?)
		toStream := input.Stream

		log.Debug().Msgf("Parameters: %+v", config)

		var predInput string

		// If we are using the tokenizer template, we don't need to process the messages
		// unless we are processing functions
		if !config.TemplateConfig.UseTokenizerTemplate || shouldUseFn {
			suppressConfigSystemPrompt := false
			mess := []string{}
			for messageIndex, i := range input.Messages {
				var content string
				role := i.Role

				// if function call, we might want to customize the role so we can display better that the "assistant called a json action"
				// if an "assistant_function_call" role is defined, we use it, otherwise we use the role that is passed by in the request
				if (i.FunctionCall != nil || i.ToolCalls != nil) && i.Role == "assistant" {
					roleFn := "assistant_function_call"
					r := config.Roles[roleFn]
					if r != "" {
						role = roleFn
					}
				}
				r := config.Roles[role]
				contentExists := i.Content != nil && i.StringContent != ""

				fcall := i.FunctionCall
				if len(i.ToolCalls) > 0 {
					fcall = i.ToolCalls
				}

				// First attempt to populate content via a chat message specific template
				if config.TemplateConfig.ChatMessage != "" {
					chatMessageData := model.ChatMessageTemplateData{
						SystemPrompt: config.SystemPrompt,
						Role:         r,
						RoleName:     role,
						Content:      i.StringContent,
						FunctionCall: fcall,
						FunctionName: i.Name,
						LastMessage:  messageIndex == (len(input.Messages) - 1),
						Function:     config.Grammar != "" && (messageIndex == (len(input.Messages) - 1)),
						MessageIndex: messageIndex,
					}
					templatedChatMessage, err := ml.EvaluateTemplateForChatMessage(config.TemplateConfig.ChatMessage, chatMessageData)
					if err != nil {
						log.Error().Err(err).Interface("message", chatMessageData).Str("template", config.TemplateConfig.ChatMessage).Msg("error processing message with template, skipping")
					} else {
						if templatedChatMessage == "" {
							log.Warn().Msgf("template \"%s\" produced blank output for %+v. Skipping!", config.TemplateConfig.ChatMessage, chatMessageData)
							continue // TODO: This continue is here intentionally to skip over the line `mess = append(mess, content)` below, and to prevent the sprintf
						}
						log.Debug().Msgf("templated message for chat: %s", templatedChatMessage)
						content = templatedChatMessage
					}
				}

				marshalAnyRole := func(f any) {
					j, err := json.Marshal(f)
					if err == nil {
						if contentExists {
							content += "\n" + fmt.Sprint(r, " ", string(j))
						} else {
							content = fmt.Sprint(r, " ", string(j))
						}
					}
				}
				marshalAny := func(f any) {
					j, err := json.Marshal(f)
					if err == nil {
						if contentExists {
							content += "\n" + string(j)
						} else {
							content = string(j)
						}
					}
				}
				// If this model doesn't have such a template, or if that template fails to return a value, template at the message level.
				if content == "" {
					if r != "" {
						if contentExists {
							content = fmt.Sprint(r, i.StringContent)
						}

						if i.FunctionCall != nil {
							marshalAnyRole(i.FunctionCall)
						}
						if i.ToolCalls != nil {
							marshalAnyRole(i.ToolCalls)
						}
					} else {
						if contentExists {
							content = fmt.Sprint(i.StringContent)
						}
						if i.FunctionCall != nil {
							marshalAny(i.FunctionCall)
						}
						if i.ToolCalls != nil {
							marshalAny(i.ToolCalls)
						}
					}
					// Special Handling: System. We care if it was printed at all, not the r branch, so check seperately
					if contentExists && role == "system" {
						suppressConfigSystemPrompt = true
					}
				}

				mess = append(mess, content)
			}

			joinCharacter := "\n"
			if config.TemplateConfig.JoinChatMessagesByCharacter != nil {
				joinCharacter = *config.TemplateConfig.JoinChatMessagesByCharacter
			}

			predInput = strings.Join(mess, joinCharacter)
			log.Debug().Msgf("Prompt (before templating): %s", predInput)

			templateFile := ""

			// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
			if ml.ExistsInModelPath(fmt.Sprintf("%s.tmpl", config.Model)) {
				templateFile = config.Model
			}

			if config.TemplateConfig.Chat != "" && !shouldUseFn {
				templateFile = config.TemplateConfig.Chat
			}

			if config.TemplateConfig.Functions != "" && shouldUseFn {
				templateFile = config.TemplateConfig.Functions
			}

			if templateFile != "" {
				templatedInput, err := ml.EvaluateTemplateForPrompt(model.ChatPromptTemplate, templateFile, model.PromptTemplateData{
					SystemPrompt:         config.SystemPrompt,
					SuppressSystemPrompt: suppressConfigSystemPrompt,
					Input:                predInput,
					Functions:            funcs,
				})
				if err == nil {
					predInput = templatedInput
					log.Debug().Msgf("Template found, input modified to: %s", predInput)
				} else {
					log.Debug().Msgf("Template failed loading: %s", err.Error())
				}
			}

			log.Debug().Msgf("Prompt (after templating): %s", predInput)
			if shouldUseFn && config.Grammar != "" {
				log.Debug().Msgf("Grammar: %+v", config.Grammar)
			}
		}

		switch {
		case toStream:

			log.Debug().Msgf("Stream request received")
			c.Context().SetContentType("text/event-stream")
			//c.Response().Header.SetContentType(fiber.MIMETextHTMLCharsetUTF8)
			//	c.Set("Content-Type", "text/event-stream")
			c.Set("Cache-Control", "no-cache")
			c.Set("Connection", "keep-alive")
			c.Set("Transfer-Encoding", "chunked")

			responses := make(chan schema.OpenAIResponse)

			if !shouldUseFn {
				go process(predInput, input, config, ml, responses)
			} else {
				go processTools(noActionName, predInput, input, config, ml, responses)
			}

			c.Context().SetBodyStreamWriter(fasthttp.StreamWriter(func(w *bufio.Writer) {
				usage := &schema.OpenAIUsage{}
				toolsCalled := false
				for ev := range responses {
					usage = &ev.Usage // Copy a pointer to the latest usage chunk so that the stop message can reference it
					if len(ev.Choices[0].Delta.ToolCalls) > 0 {
						toolsCalled = true
					}
					var buf bytes.Buffer
					enc := json.NewEncoder(&buf)
					enc.Encode(ev)
					log.Debug().Msgf("Sending chunk: %s", buf.String())
					_, err := fmt.Fprintf(w, "data: %v\n", buf.String())
					if err != nil {
						log.Debug().Msgf("Sending chunk failed: %v", err)
						input.Cancel()
					}
					w.Flush()
				}

				finishReason := "stop"
				if toolsCalled {
					finishReason = "tool_calls"
				} else if toolsCalled && len(input.Tools) == 0 {
					finishReason = "function_call"
				}

				resp := &schema.OpenAIResponse{
					ID:      id,
					Created: created,
					Model:   input.Model, // we have to return what the user sent here, due to OpenAI spec.
					Choices: []schema.Choice{
						{
							FinishReason: finishReason,
							Index:        0,
							Delta:        &schema.Message{Content: &textContentToReturn},
						}},
					Object: "chat.completion.chunk",
					Usage:  *usage,
				}
				respData, _ := json.Marshal(resp)

				w.WriteString(fmt.Sprintf("data: %s\n\n", respData))
				w.WriteString("data: [DONE]\n\n")
				w.Flush()
			}))
			return nil

		// no streaming mode
		default:
			result, tokenUsage, err := ComputeChoices(input, predInput, config, startupOptions, ml, func(s string, c *[]schema.Choice) {
				if !shouldUseFn {
					// no function is called, just reply and use stop as finish reason
					*c = append(*c, schema.Choice{FinishReason: "stop", Index: 0, Message: &schema.Message{Role: "assistant", Content: &s}})
					return
				}

				textContentToReturn = functions.ParseTextContent(s, config.FunctionsConfig)
				s = functions.CleanupLLMResult(s, config.FunctionsConfig)
				results := functions.ParseFunctionCall(s, config.FunctionsConfig)
				log.Debug().Msgf("Text content to return: %s", textContentToReturn)
				noActionsToRun := len(results) > 0 && results[0].Name == noActionName || len(results) == 0

				switch {
				case noActionsToRun:
					result, err := handleQuestion(config, input, ml, startupOptions, results, s, predInput)
					if err != nil {
						log.Error().Err(err).Msg("error handling question")
						return
					}
					*c = append(*c, schema.Choice{
						Message: &schema.Message{Role: "assistant", Content: &result}})
				default:
					toolChoice := schema.Choice{
						Message: &schema.Message{
							Role: "assistant",
						},
					}

					if len(input.Tools) > 0 {
						toolChoice.FinishReason = "tool_calls"
					}

					for _, ss := range results {
						name, args := ss.Name, ss.Arguments
						if len(input.Tools) > 0 {
							// If we are using tools, we condense the function calls into
							// a single response choice with all the tools
							toolChoice.Message.Content = textContentToReturn
							toolChoice.Message.ToolCalls = append(toolChoice.Message.ToolCalls,
								schema.ToolCall{
									ID:   id,
									Type: "function",
									FunctionCall: schema.FunctionCall{
										Name:      name,
										Arguments: args,
									},
								},
							)
						} else {
							// otherwise we return more choices directly
							*c = append(*c, schema.Choice{
								FinishReason: "function_call",
								Message: &schema.Message{
									Role:    "assistant",
									Content: &textContentToReturn,
									FunctionCall: map[string]interface{}{
										"name":      name,
										"arguments": args,
									},
								},
							})
						}
					}

					if len(input.Tools) > 0 {
						// we need to append our result if we are using tools
						*c = append(*c, toolChoice)
					}
				}

			}, nil)
			if err != nil {
				return err
			}

			resp := &schema.OpenAIResponse{
				ID:      id,
				Created: created,
				Model:   input.Model, // we have to return what the user sent here, due to OpenAI spec.
				Choices: result,
				Object:  "chat.completion",
				Usage: schema.OpenAIUsage{
					PromptTokens:     tokenUsage.Prompt,
					CompletionTokens: tokenUsage.Completion,
					TotalTokens:      tokenUsage.Prompt + tokenUsage.Completion,
				},
			}
			respData, _ := json.Marshal(resp)
			log.Debug().Msgf("Response: %s", respData)

			// Return the prediction in the response body
			return c.JSON(resp)
		}
	}
}

func handleQuestion(config *config.BackendConfig, input *schema.OpenAIRequest, ml *model.ModelLoader, o *config.ApplicationConfig, funcResults []functions.FuncCallResults, result, prompt string) (string, error) {

	if len(funcResults) == 0 && result != "" {
		log.Debug().Msgf("nothing function results but we had a message from the LLM")

		return result, nil
	}

	log.Debug().Msgf("nothing to do, computing a reply")
	arg := ""
	if len(funcResults) > 0 {
		arg = funcResults[0].Arguments
	}
	// If there is a message that the LLM already sends as part of the JSON reply, use it
	arguments := map[string]interface{}{}
	if err := json.Unmarshal([]byte(arg), &arguments); err != nil {
		log.Debug().Msg("handleQuestion: function result did not contain a valid JSON object")
	}
	m, exists := arguments["message"]
	if exists {
		switch message := m.(type) {
		case string:
			if message != "" {
				log.Debug().Msgf("Reply received from LLM: %s", message)
				message = backend.Finetune(*config, prompt, message)
				log.Debug().Msgf("Reply received from LLM(finetuned): %s", message)

				return message, nil
			}
		}
	}

	log.Debug().Msgf("No action received from LLM, without a message, computing a reply")
	// Otherwise ask the LLM to understand the JSON output and the context, and return a message
	// Note: This costs (in term of CPU/GPU) another computation
	config.Grammar = ""
	images := []string{}
	for _, m := range input.Messages {
		images = append(images, m.StringImages...)
	}

	predFunc, err := backend.ModelInference(input.Context, prompt, input.Messages, images, ml, *config, o, nil)
	if err != nil {
		log.Error().Err(err).Msg("model inference failed")
		return "", err
	}

	prediction, err := predFunc()
	if err != nil {
		log.Error().Err(err).Msg("prediction failed")
		return "", err
	}
	return backend.Finetune(*config, prompt, prediction.Response), nil
}