static/js/model-tooltips.js

document.addEventListener('DOMContentLoaded', function() {
  // Fix model tooltips in all tabs
  function fixAllModelTooltips() {
    console.log("Fixing model tooltips in all tabs");
    
    // Find all model name cells (first column in all tables)
    const modelCells = document.querySelectorAll('td:first-child');
    
    // Process each model cell
    modelCells.forEach(cell => {
      // Skip cells that already have tooltips
      if (cell.classList.contains('tooltip-trigger')) {
        return;
      }
      
      // Get the model name
      const modelName = cell.textContent.trim();
      
      // Add tooltip-trigger class and position style
      cell.classList.add('tooltip-trigger');
      cell.style.position = 'relative';
      
      // Add data-title attribute with the model name
      cell.setAttribute('data-title', modelName);
      
      // Add descriptive tooltip based on model
      let tooltipText = "";
      
      // Set descriptive tooltip based on model name - exact descriptions from cost analysis tab
      if (modelName.includes("GPT-4o") || modelName.includes("gpt-4o")) {
        tooltipText = "OpenAI's flagship multimodal model optimized for a balance of quality and speed. Features strong performance across diverse tasks with capabilities for complex financial reasoning and instruction following.";
      } else if (modelName.includes("o1-mini")) {
        tooltipText = "OpenAI's smaller advanced model balancing efficiency and performance. Demonstrates surprisingly strong results on financial tasks despite its reduced parameter count.";
      } else if (modelName.includes("Claude 3.5 Sonnet")) {
        tooltipText = "Anthropic's advanced proprietary language model optimized for complex reasoning and instruction-following. Features enhanced performance on financial tasks with strong text processing capabilities.";
      } else if (modelName.includes("Claude 3 Haiku")) {
        tooltipText = "Anthropic's smaller efficiency-focused model in the Claude family. Designed for speed and lower computational requirements while maintaining reasonable performance on financial tasks.";
      } else if (modelName.includes("Gemini 1.5")) {
        tooltipText = "Google's advanced proprietary multimodal model designed for complex reasoning and instruction-following tasks. Features strong performance across financial domains with advanced reasoning capabilities.";
      } else if (modelName.includes("Command R 7B")) {
        tooltipText = "Cohere's 7-billion parameter model focused on instruction-following. An efficient model with reasonable financial domain capabilities for its size.";
      } else if (modelName.includes("Command R +")) {
        tooltipText = "Cohere's enhanced command model with improved instruction-following capabilities. Features advanced reasoning for financial domains with stronger performance than its smaller counterpart.";
      } else if (modelName.includes("DeepSeek R1")) {
        tooltipText = "DeepSeek's premium 671 billion parameter Mixture of Experts (MoE) model representing their most advanced offering. Designed for state-of-the-art performance across complex reasoning and financial tasks.";
      } else if (modelName.includes("DeepSeek-V3") || modelName.includes("DeepSeek V3")) {
        tooltipText = "DeepSeek's 685 billion parameter Mixture of Experts (MoE) model optimized for advanced reasoning. Strong performance on financial tasks with robust instruction-following capabilities.";
      } else if (modelName.includes("DeepSeek LLM")) {
        tooltipText = "DeepSeek's 67 billion parameter model optimized for chat applications. Balances performance and efficiency across financial tasks with solid reasoning capabilities.";
      } else if (modelName.includes("Llama 3 70B")) {
        tooltipText = "Meta's advanced 70 billion parameter dense language model optimized for instruction-following tasks. Available through Together AI and notable for complex reasoning capabilities.";
      } else if (modelName.includes("Llama 3 8B")) {
        tooltipText = "Meta's efficient 8 billion parameter language model optimized for instruction-following. Balances performance and efficiency for financial tasks with reasonable reasoning capabilities.";
      } else if (modelName.includes("DBRX")) {
        tooltipText = "Databricks' 132 billion parameter Mixture of Experts (MoE) model focused on advanced reasoning. Demonstrates competitive performance on financial tasks with strong text processing capabilities.";
      } else if (modelName.includes("Mixtral-8x22B")) {
        tooltipText = "Mistral AI's 141 billion parameter MoE model with eight 22B expert networks. Features robust reasoning capabilities for financial tasks with strong instruction-following performance.";
      } else if (modelName.includes("Mixtral-8x7B")) {
        tooltipText = "Mistral AI's 47 billion parameter MoE model with eight 7B expert networks. Balances efficiency and performance with reasonable financial reasoning capabilities.";
      } else if (modelName.includes("Mistral")) {
        tooltipText = "Mistral AI's 7 billion parameter instruction-tuned model. Demonstrates impressive efficiency with reasonable performance on financial tasks despite its smaller size.";
      } else if (modelName.includes("Qwen 2")) {
        tooltipText = "Alibaba's 72 billion parameter instruction-following model optimized for reasoning tasks. Features strong performance on financial domains with advanced text processing capabilities.";
      } else if (modelName.includes("WizardLM")) {
        tooltipText = "A 176 billion parameter MoE model focused on complex reasoning. Designed for advanced instruction-following with strong capabilities across financial tasks.";
      } else if (modelName.includes("Gemma 2 27B")) {
        tooltipText = "Google's open-weight 27 billion parameter model optimized for reasoning tasks. Balances performance and efficiency across financial domains with strong instruction-following.";
      } else if (modelName.includes("Gemma 2 9B")) {
        tooltipText = "Google's efficient open-weight 9 billion parameter model. Demonstrates good performance on financial tasks relative to its smaller size.";
      } else if (modelName.includes("QwQ-32B")) {
        tooltipText = "Qwen's experimental 32 billion parameter MoE model focused on efficient computation. Features interesting performance characteristics on certain financial tasks.";
      } else if (modelName.includes("Jamba 1.5 Mini")) {
        tooltipText = "A compact variant in the Jamba model series focused on efficiency. Balances performance and computational requirements for financial tasks.";
      } else if (modelName.includes("Jamba 1.5 Large")) {
        tooltipText = "An expanded variant in the Jamba model series with enhanced capabilities. Features stronger reasoning for financial tasks than its smaller counterpart.";
      } else {
        tooltipText = "A language model evaluated in the FLaME financial benchmark. Assessed across multiple financial NLP tasks including classification, summarization, QA, and more.";
      }
      
      // Set the tooltip
      cell.setAttribute('data-tooltip', tooltipText);
    });
    
    // After adding attributes, run the tooltip fix
    if (window.fixProblemTooltips) {
      window.fixProblemTooltips();
    }
  }
  
  // Run on page load
  setTimeout(fixAllModelTooltips, 500);
  
  // Run when tabs are clicked
  const tabs = document.querySelectorAll('.tabs li');
  tabs.forEach(tab => {
    tab.addEventListener('click', () => {
      // Give time for content to be displayed
      setTimeout(fixAllModelTooltips, 200);
    });
  });
});