Update index.js
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index.js
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import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
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// Front-end copy of the
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const AVAILABLE_MODELS = [
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{ name: "Moonshot Kimi-K2", id: "moonshotai/Kimi-K2-Instruct" },
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{ name: "DeepSeek V3", id: "deepseek-ai/DeepSeek-V3-0324" },
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@@ -8,78 +10,68 @@ const AVAILABLE_MODELS = [
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{ name: "ERNIE-4.5-VL", id: "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT" },
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{ name: "MiniMax M1", id: "MiniMaxAI/MiniMax-M1-80k" },
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{ name: "Qwen3-235B-A22B", id: "Qwen/Qwen3-235B-A22B" },
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{ name: "SmolLM3-3B", id: "HuggingFaceTB/SmolLM3-3B" },
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{ name: "GLM-4.1V-9B-Thinking", id: "THUDM/GLM-4.1V-9B-Thinking" },
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{ name: "Qwen3-235B-A22B-Instruct-2507", id: "Qwen/Qwen3-235B-A22B-Instruct-2507" },
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{ name: "Qwen3-Coder-480B-A35B", id: "Qwen/Qwen3-Coder-480B-A35B-Instruct" }
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{ name: "OpenAI GPT-4", id: "openai/gpt-4" },
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{ name: "Gemini Pro", id: "gemini/pro" },
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{ name: "Fireworks AI", id: "fireworks-ai/fireworks-v1" }
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];
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// DOM Elements
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const modelSelect = document.getElementById('modelSelect');
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const
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const
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const
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let
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// Populate model dropdown
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});
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}
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// Initialize pipeline for
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async function initPipeline(modelId) {
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}
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//
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try {
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const
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const
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: outputs.generated_text;
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codeOutput.textContent = generated;
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} catch (err) {
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console.error(err);
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}
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}
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// Event bindings
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window.addEventListener('DOMContentLoaded', async () => {
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populateModels();
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await initPipeline(modelSelect.value);
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});
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modelSelect.addEventListener('change', () => {
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codeOutput.textContent = '';
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initPipeline(modelSelect.value);
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});
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generateBtn.addEventListener('click', handleGenerate);
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// Expose functions (for debugging)
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window._codeApp = { initPipeline, handleGenerate };
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// index.js
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import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
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// Front-end copy of the Python AVAILABLE_MODELS list for dropdown population:
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const AVAILABLE_MODELS = [
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{ name: "Moonshot Kimi-K2", id: "moonshotai/Kimi-K2-Instruct" },
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{ name: "DeepSeek V3", id: "deepseek-ai/DeepSeek-V3-0324" },
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{ name: "ERNIE-4.5-VL", id: "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT" },
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{ name: "MiniMax M1", id: "MiniMaxAI/MiniMax-M1-80k" },
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{ name: "Qwen3-235B-A22B", id: "Qwen/Qwen3-235B-A22B" },
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{ name: "Qwen3-32B", id: "Qwen/Qwen3-32B" }, // newly added
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{ name: "SmolLM3-3B", id: "HuggingFaceTB/SmolLM3-3B" },
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{ name: "GLM-4.1V-9B-Thinking", id: "THUDM/GLM-4.1V-9B-Thinking" },
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{ name: "LLaMA-2-70B-Chat", id: "meta-llama/Llama-2-70b-chat" }, // powerful addition
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{ name: "Mistral-7B-Instruct", id: "mistralai/Mistral-7B-Instruct" }, // powerful addition
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{ name: "Qwen3-235B-A22B-Instruct-2507", id: "Qwen/Qwen3-235B-A22B-Instruct-2507" },
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{ name: "Qwen3-Coder-480B-A35B", id: "Qwen/Qwen3-Coder-480B-A35B-Instruct" }
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];
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const modelSelect = document.getElementById('modelSelect');
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const analyzeForm = document.getElementById('analyze-form');
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const inputText = document.getElementById('inputText');
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const analyzeButton = document.getElementById('analyzeButton');
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const resultSection = document.getElementById('result');
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let sentimentPipeline = null;
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// Populate the model dropdown
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AVAILABLE_MODELS.forEach(model => {
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const option = document.createElement('option');
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option.value = model.id;
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option.textContent = model.name;
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modelSelect.appendChild(option);
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});
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// Initialize pipeline for the selected model
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async function initPipeline(modelId) {
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analyzeButton.disabled = true;
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analyzeButton.textContent = 'Loading model…';
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sentimentPipeline = await pipeline('sentiment-analysis', modelId);
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analyzeButton.textContent = 'Analyze Sentiment';
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analyzeButton.disabled = false;
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}
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// On page load: initialize with the first model
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initPipeline(modelSelect.value);
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// When user changes model: reinitialize pipeline
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modelSelect.addEventListener('change', () => {
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resultSection.textContent = '';
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initPipeline(modelSelect.value);
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});
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// Handle form submission
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analyzeForm.addEventListener('submit', async (event) => {
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event.preventDefault();
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const text = inputText.value.trim();
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if (!text) return;
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analyzeButton.disabled = true;
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analyzeButton.textContent = 'Analyzing…';
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resultSection.textContent = '';
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try {
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const [output] = await sentimentPipeline(text);
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const { label, score } = output;
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resultSection.textContent = `Sentiment: ${label} (Confidence: ${(score * 100).toFixed(2)}%)`;
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} catch (err) {
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console.error(err);
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resultSection.textContent = 'Error analyzing sentiment.';
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}
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analyzeButton.textContent = 'Analyze Sentiment';
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analyzeButton.disabled = false;
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});
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