# frozen_string_literal: true

module DiscourseAi
  module Personas
    class PostRawTranslator < Persona
      def self.default_enabled
        false
      end

      def system_prompt
        <<~PROMPT.strip
          You are a highly skilled translator tasked with translating content from one language to another. Your goal is to provide accurate and contextually appropriate translations while preserving the original structure and formatting of the content. Follow these instructions carefully:

          1. Translate the content accurately while preserving any Markdown, HTML elements, or newlines.
          2. Maintain the original document structure including headings, lists, tables, code blocks, etc.
          3. Preserve all links, images, and other media references without translation.
          4. Handle code snippets appropriately:
            - Do not translate variable names, functions, or syntax within code blocks (```).
            - Translate comments within code blocks.
          5. For technical terminology:
            - Provide the accepted target language term if it exists.
            - If no equivalent exists, transliterate the term and include the original term in parentheses.
          6. For ambiguous terms or phrases, choose the most contextually appropriate translation.
          7. Do not add any content besides the translation.
          8. Ensure the translation only contains the original language and the target language.

          The text to translate will be provided in JSON format with the following structure:
          {"content": "Text to translate", "target_locale": "Target language code"}

          Output your translation in the following JSON format:
          {"translation": "Your translated text here"}

          You are being consumed via an API. Only return the translated text in the specified JSON format. Do not include any additional information or explanations.
        PROMPT
      end

      def response_format
        [{ "key" => "translation", "type" => "string" }]
      end

      def temperature
        0.3
      end

      def examples
        [
          [
            {
              content:
                "**Heathrow fechado**: paralisação de voos deve continuar nos próximos dias, diz gestora do aeroporto de *Londres*",
              target_locale: "en",
            }.to_json,
            {
              translation:
                "**Heathrow closed**: flight disruption expected to continue in coming days, says *London* airport management",
            }.to_json,
          ],
          [
            {
              content: "New Update for Minecraft Adds Underwater Temples",
              target_locale: "es",
            }.to_json,
            { translation: "Nueva actualización para Minecraft añade templos submarinos" }.to_json,
          ],
          [
            {
              content:
                "# Machine Learning 101\n\nMachine Learning (ML) is a subset of Artificial Intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience.\n\n## Key Concepts\n\n1. **Supervised Learning**: The algorithm learns from labeled training data.\n2. **Unsupervised Learning**: The algorithm finds patterns in unlabeled data.\n3. **Reinforcement Learning**: The algorithm learns through interaction with an environment.\n\n```python\n# Simple example of a machine learning model\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\n\n# Assuming X and y are your features and target variables\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n\n# Evaluate the model\naccuracy = model.score(X_test, y_test)\nprint(f'Model accuracy: {accuracy}')\n```\n\nFor more information, visit [Machine Learning on Wikipedia](https://en.wikipedia.org/wiki/Machine_learning).",
              target_locale: "fr",
            }.to_json,
            {
              translation:
                "# Machine Learning 101\n\nLe Machine Learning (ML) est un sous-ensemble de l'Intelligence Artificielle (IA) qui se concentre sur le développement d'algorithmes et de modèles statistiques permettant aux systèmes informatiques d'améliorer leurs performances sur une tâche spécifique grâce à l'expérience.\n\n## Concepts clés\n\n1. **Apprentissage supervisé** : L'algorithme apprend à partir de données d'entraînement étiquetées.\n2. **Apprentissage non supervisé** : L'algorithme trouve des motifs dans des données non étiquetées.\n3. **Apprentissage par renforcement** : L'algorithme apprend à travers l'interaction avec un environnement.\n\n```python\n# Exemple simple d'un modèle de machine learning\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\n\n# En supposant que X et y sont vos variables de caractéristiques et cibles\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n\n# Évaluer le modèle\naccuracy = model.score(X_test, y_test)\nprint(f'Model accuracy: {accuracy}')\n```\n\nPour plus d'informations, visitez [Machine Learning sur Wikipedia](https://en.wikipedia.org/wiki/Machine_learning).",
            }.to_json,
          ],
        ]
      end
    end
  end
end
