const { fetchOpenRouterModels } = require("../AiProviders/openRouter");
const { perplexityModels } = require("../AiProviders/perplexity");
const { togetherAiModels } = require("../AiProviders/togetherAi");
const { ElevenLabsTTS } = require("../TextToSpeech/elevenLabs");
const SUPPORT_CUSTOM_MODELS = [
  "openai",
  "localai",
  "ollama",
  "native-llm",
  "togetherai",
  "mistral",
  "perplexity",
  "openrouter",
  "lmstudio",
  "koboldcpp",
  "litellm",
  "elevenlabs-tts",
  "groq",
];

async function getCustomModels(provider = "", apiKey = null, basePath = null) {
  if (!SUPPORT_CUSTOM_MODELS.includes(provider))
    return { models: [], error: "Invalid provider for custom models" };

  switch (provider) {
    case "openai":
      return await openAiModels(apiKey);
    case "localai":
      return await localAIModels(basePath, apiKey);
    case "ollama":
      return await ollamaAIModels(basePath);
    case "togetherai":
      return await getTogetherAiModels();
    case "mistral":
      return await getMistralModels(apiKey);
    case "native-llm":
      return nativeLLMModels();
    case "perplexity":
      return await getPerplexityModels();
    case "openrouter":
      return await getOpenRouterModels();
    case "lmstudio":
      return await getLMStudioModels(basePath);
    case "koboldcpp":
      return await getKoboldCPPModels(basePath);
    case "litellm":
      return await liteLLMModels(basePath, apiKey);
    case "elevenlabs-tts":
      return await getElevenLabsModels(apiKey);
    case "groq":
      return await getGroqAiModels(apiKey);
    default:
      return { models: [], error: "Invalid provider for custom models" };
  }
}

async function openAiModels(apiKey = null) {
  const { OpenAI: OpenAIApi } = require("openai");
  const openai = new OpenAIApi({
    apiKey: apiKey || process.env.OPEN_AI_KEY,
  });
  const allModels = await openai.models
    .list()
    .then((results) => results.data)
    .catch((e) => {
      console.error(`OpenAI:listModels`, e.message);
      return [
        {
          name: "gpt-3.5-turbo",
          id: "gpt-3.5-turbo",
          object: "model",
          created: 1677610602,
          owned_by: "openai",
          organization: "OpenAi",
        },
        {
          name: "gpt-4o",
          id: "gpt-4o",
          object: "model",
          created: 1677610602,
          owned_by: "openai",
          organization: "OpenAi",
        },
        {
          name: "gpt-4",
          id: "gpt-4",
          object: "model",
          created: 1687882411,
          owned_by: "openai",
          organization: "OpenAi",
        },
        {
          name: "gpt-4-turbo",
          id: "gpt-4-turbo",
          object: "model",
          created: 1712361441,
          owned_by: "system",
          organization: "OpenAi",
        },
        {
          name: "gpt-4-32k",
          id: "gpt-4-32k",
          object: "model",
          created: 1687979321,
          owned_by: "openai",
          organization: "OpenAi",
        },
        {
          name: "gpt-3.5-turbo-16k",
          id: "gpt-3.5-turbo-16k",
          object: "model",
          created: 1683758102,
          owned_by: "openai-internal",
          organization: "OpenAi",
        },
      ];
    });

  const gpts = allModels
    .filter((model) => model.id.startsWith("gpt"))
    .filter(
      (model) => !model.id.includes("vision") && !model.id.includes("instruct")
    )
    .map((model) => {
      return {
        ...model,
        name: model.id,
        organization: "OpenAi",
      };
    });

  const customModels = allModels
    .filter(
      (model) =>
        !model.owned_by.includes("openai") && model.owned_by !== "system"
    )
    .map((model) => {
      return {
        ...model,
        name: model.id,
        organization: "Your Fine-Tunes",
      };
    });

  // Api Key was successful so lets save it for future uses
  if ((gpts.length > 0 || customModels.length > 0) && !!apiKey)
    process.env.OPEN_AI_KEY = apiKey;
  return { models: [...gpts, ...customModels], error: null };
}

async function localAIModels(basePath = null, apiKey = null) {
  const { OpenAI: OpenAIApi } = require("openai");
  const openai = new OpenAIApi({
    baseURL: basePath || process.env.LOCAL_AI_BASE_PATH,
    apiKey: apiKey || process.env.LOCAL_AI_API_KEY || null,
  });
  const models = await openai.models
    .list()
    .then((results) => results.data)
    .catch((e) => {
      console.error(`LocalAI:listModels`, e.message);
      return [];
    });

  // Api Key was successful so lets save it for future uses
  if (models.length > 0 && !!apiKey) process.env.LOCAL_AI_API_KEY = apiKey;
  return { models, error: null };
}

async function getGroqAiModels(_apiKey = null) {
  const { OpenAI: OpenAIApi } = require("openai");
  const apiKey =
    _apiKey === true
      ? process.env.GROQ_API_KEY
      : _apiKey || process.env.GROQ_API_KEY || null;
  const openai = new OpenAIApi({
    baseURL: "https://api.groq.com/openai/v1",
    apiKey,
  });
  const models = (
    await openai.models
      .list()
      .then((results) => results.data)
      .catch((e) => {
        console.error(`GroqAi:listModels`, e.message);
        return [];
      })
  ).filter(
    (model) => !model.id.includes("whisper") && !model.id.includes("tool-use")
  );

  // Api Key was successful so lets save it for future uses
  if (models.length > 0 && !!apiKey) process.env.GROQ_API_KEY = apiKey;
  return { models, error: null };
}

async function liteLLMModels(basePath = null, apiKey = null) {
  const { OpenAI: OpenAIApi } = require("openai");
  const openai = new OpenAIApi({
    baseURL: basePath || process.env.LITE_LLM_BASE_PATH,
    apiKey: apiKey || process.env.LITE_LLM_API_KEY || null,
  });
  const models = await openai.models
    .list()
    .then((results) => results.data)
    .catch((e) => {
      console.error(`LiteLLM:listModels`, e.message);
      return [];
    });

  // Api Key was successful so lets save it for future uses
  if (models.length > 0 && !!apiKey) process.env.LITE_LLM_API_KEY = apiKey;
  return { models, error: null };
}

async function getLMStudioModels(basePath = null) {
  try {
    const { OpenAI: OpenAIApi } = require("openai");
    const openai = new OpenAIApi({
      baseURL: basePath || process.env.LMSTUDIO_BASE_PATH,
      apiKey: null,
    });
    const models = await openai.models
      .list()
      .then((results) => results.data)
      .catch((e) => {
        console.error(`LMStudio:listModels`, e.message);
        return [];
      });

    return { models, error: null };
  } catch (e) {
    console.error(`LMStudio:getLMStudioModels`, e.message);
    return { models: [], error: "Could not fetch LMStudio Models" };
  }
}

async function getKoboldCPPModels(basePath = null) {
  try {
    const { OpenAI: OpenAIApi } = require("openai");
    const openai = new OpenAIApi({
      baseURL: basePath || process.env.KOBOLD_CPP_BASE_PATH,
      apiKey: null,
    });
    const models = await openai.models
      .list()
      .then((results) => results.data)
      .catch((e) => {
        console.error(`KoboldCPP:listModels`, e.message);
        return [];
      });

    return { models, error: null };
  } catch (e) {
    console.error(`KoboldCPP:getKoboldCPPModels`, e.message);
    return { models: [], error: "Could not fetch KoboldCPP Models" };
  }
}

async function ollamaAIModels(basePath = null) {
  let url;
  try {
    let urlPath = basePath ?? process.env.OLLAMA_BASE_PATH;
    new URL(urlPath);
    if (urlPath.split("").slice(-1)?.[0] === "/")
      throw new Error("BasePath Cannot end in /!");
    url = urlPath;
  } catch {
    return { models: [], error: "Not a valid URL." };
  }

  const models = await fetch(`${url}/api/tags`)
    .then((res) => {
      if (!res.ok)
        throw new Error(`Could not reach Ollama server! ${res.status}`);
      return res.json();
    })
    .then((data) => data?.models || [])
    .then((models) =>
      models.map((model) => {
        return { id: model.name };
      })
    )
    .catch((e) => {
      console.error(e);
      return [];
    });

  return { models, error: null };
}

async function getTogetherAiModels() {
  const knownModels = togetherAiModels();
  if (!Object.keys(knownModels).length === 0)
    return { models: [], error: null };

  const models = Object.values(knownModels).map((model) => {
    return {
      id: model.id,
      organization: model.organization,
      name: model.name,
    };
  });
  return { models, error: null };
}

async function getPerplexityModels() {
  const knownModels = perplexityModels();
  if (!Object.keys(knownModels).length === 0)
    return { models: [], error: null };

  const models = Object.values(knownModels).map((model) => {
    return {
      id: model.id,
      name: model.name,
    };
  });
  return { models, error: null };
}

async function getOpenRouterModels() {
  const knownModels = await fetchOpenRouterModels();
  if (!Object.keys(knownModels).length === 0)
    return { models: [], error: null };

  const models = Object.values(knownModels).map((model) => {
    return {
      id: model.id,
      organization: model.organization,
      name: model.name,
    };
  });
  return { models, error: null };
}

async function getMistralModels(apiKey = null) {
  const { OpenAI: OpenAIApi } = require("openai");
  const openai = new OpenAIApi({
    apiKey: apiKey || process.env.MISTRAL_API_KEY || null,
    baseURL: "https://api.mistral.ai/v1",
  });
  const models = await openai.models
    .list()
    .then((results) =>
      results.data.filter((model) => !model.id.includes("embed"))
    )
    .catch((e) => {
      console.error(`Mistral:listModels`, e.message);
      return [];
    });

  // Api Key was successful so lets save it for future uses
  if (models.length > 0 && !!apiKey) process.env.MISTRAL_API_KEY = apiKey;
  return { models, error: null };
}

function nativeLLMModels() {
  const fs = require("fs");
  const path = require("path");
  const storageDir = path.resolve(
    process.env.STORAGE_DIR
      ? path.resolve(process.env.STORAGE_DIR, "models", "downloaded")
      : path.resolve(__dirname, `../../storage/models/downloaded`)
  );
  if (!fs.existsSync(storageDir))
    return { models: [], error: "No model/downloaded storage folder found." };

  const files = fs
    .readdirSync(storageDir)
    .filter((file) => file.toLowerCase().includes(".gguf"))
    .map((file) => {
      return { id: file, name: file };
    });
  return { models: files, error: null };
}

async function getElevenLabsModels(apiKey = null) {
  const models = (await ElevenLabsTTS.voices(apiKey)).map((model) => {
    return {
      id: model.voice_id,
      organization: model.category,
      name: model.name,
    };
  });

  if (models.length === 0) {
    return {
      models: [
        {
          id: "21m00Tcm4TlvDq8ikWAM",
          organization: "premade",
          name: "Rachel (default)",
        },
      ],
      error: null,
    };
  }

  if (models.length > 0 && !!apiKey) process.env.TTS_ELEVEN_LABS_KEY = apiKey;
  return { models, error: null };
}

module.exports = {
  getCustomModels,
};