import { pipeline, env } from '@huggingface/transformers';

env.cacheDir = './.cache';

class TeapotAI {
  /**
  * Initializes the TeapotAI class.
  * @param {object} options - Configuration options.
  * @param {string} [options.modelId='teapotai/teapotllm'] - The Hugging Face model ID.
  * @param {boolean} [options.verbose=true] - Whether to print status messages.
  * @param {object} [options.pipelineOptions={}] - Additional pipeline options passed to the transformer.
  */
  constructor({
    modelId = 'tomasmcm/teapotai-teapotllm-onnx',
    verbose = false,
    pipelineOptions = {}
  } = {}) {
    this.modelId = modelId;
    this.verbose = verbose;
    this.pipelineOptions = pipelineOptions;
    this.generator = null;
    this.isInitialized = false;
    
    if (this.verbose) {
      console.log(`TeapotAI instance created for model: ${this.modelId}`);
    }
  }
  
  /**
  * Asynchronously initializes the text generation pipeline.
  * Must be called before using generate, query, or chat.
  */
  async initialize() {
    if (this.isInitialized) {
      if (this.verbose) console.log("Pipeline already initialized.");
      return;
    }
    try {
      if (this.verbose) console.log(`Initializing generator pipeline for model: ${this.modelId}...`);
      const pipelineOptions = {
        model: this.modelId,
        ...this.pipelineOptions
      };
      
      this.generator = await pipeline('text2text-generation', pipelineOptions.model, pipelineOptions);
      this.isInitialized = true;
      if (this.verbose) console.log("Pipeline initialized successfully.");
    } catch (error) {
      console.error("Failed to initialize pipeline:", error);
      throw error;
    }
  }
  
  /**
  * Ensures the pipeline is initialized before proceeding.
  * @private
  */
  _ensureInitialized() {
    if (!this.isInitialized || !this.generator) {
      throw new Error("Pipeline not initialized. Call initialize() before using query(), generate(), or chat().");
    }
  }
  
  /**
  * Generates text based on the input string.
  * (Internal method similar to the Python version's generate)
  * @param {string} inputText - The text prompt to generate a response for.
  * @returns {Promise<string>} The generated output from the model.
  */
  async generate(inputText) {
    this._ensureInitialized();
    try {
      if (this.verbose) console.log("Generating text...");
      
      const output = await this.generator(inputText, {
        max_new_tokens: 512,
      });
      
      const generatedText = output[0]?.generated_text?.trim() ?? "Error: Could not generate text.";
      if (this.verbose) console.log("Text generation complete.");
      return generatedText;
      
    } catch (error) {
      if (this.verbose) console.error("Error during text generation:", error);
      return "Error: Generation failed.";
    }
  }
  
  /**
  * Handles a query and context to generate a response.
  * (Focuses on the case where context is provided, skipping RAG)
  * @param {string} query - The query string to be answered.
  * @param {string} context - The context to guide the response.
  * @returns {Promise<string>} The generated response based on the query and context.
  */
  async query(query, context) {
    this._ensureInitialized();
    
    let inputText;
    if (!context) {
      if (this.verbose) console.warn("Context is empty. Proceeding without context enhancement.");
      inputText = `Query: ${query}`;
    } else {
      inputText = `Context: ${context}\nQuery: ${query}`;
      if (this.verbose) console.log("\nFormatted Input for Query:\n", inputText);
    }
    return this.generate(inputText);
  }
  
  /**
  * Engages in a chat by taking a list of previous messages and generating a response.
  * @param {Array<object>} conversationHistory - An array of message objects, each expected to have a 'content' property. E.g., [{ content: 'User: Hi' }, { content: 'Agent: Hello!' }]
  * @returns {Promise<string>} The generated agent response based on the conversation history.
  */
  async chat(conversationHistory) {
    this._ensureInitialized();
    
    if (!Array.isArray(conversationHistory)) {
      throw new Error("conversationHistory must be an array of message objects.");
    }
    
    let chatHistoryString = conversationHistory
      .map(message => message.content)
      .join("\n");
    
    const inputText = chatHistoryString + "\n" + "agent:";
    
    if (this.verbose) console.log("\nFormatted Input for Chat:\n", inputText);
    
    return this.generate(inputText);
  }
}

export default TeapotAI;