Simple Chatbot

{"name":"Chatbot Adventure","description":"Join the 'Chatbot Adventure' where you'll build an interactive chatbot that can engage users in fun conversations! With the power of transformers.js, you'll create a friendly AI that learns and responds to user input creatively.","photo":null,"view_count":2,"fork_count":0,"expires_at":null,"created_at":"2024-08-17T16:11:13.848838Z","updated_at":"2024-08-17T16:11:13.848838Z","user_id":70,"status":"PRIVATE","parent_id":null,"id":2765,"user":{"photo":null,"email":"kristen@jippity.pro","recess_id":"0c22a63e-e8fa-453f-a493-d113532a13c2","created_at":"2024-03-23T19:20:56.974481Z","reset_token":"GD2dODEjNUbAH88Yx-1HMTa4uc_OUtvTa0c5D-VKUhw","stripe_customer_id":"cus_RPbovGjLueUufv","seats":200,"username":"kris10","id":70,"password_hash":"$2b$12$abwA8Crfrc22ANEFpZErHeQDLvhr9RJOD0lMNLh7PJGrAn7M//qEC","updated_at":"2024-03-23T19:20:56.974485Z","reset_token_expiry":"2025-01-14T23:32:05.455609","subscription_status":"active"},"code":{"script.js":"console.log(\"script.js loaded\"); // Confirm script is loaded\n\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n console.log(\"DOM fully loaded and parsed\");\n console.log(\"webllm object:\", webllm); // Check if webllm is loaded\n\n if (!webllm || !webllm.prebuiltAppConfig || !webllm.prebuiltAppConfig.model_list) {\n console.error(\"webllm or prebuiltAppConfig is not loaded correctly.\");\n } else {\n console.log(\"webllm and prebuiltAppConfig loaded correctly.\");\n\n const availableModels = webllm.prebuiltAppConfig.model_list.map(\n (m) => m.model_id,\n );\n\n console.log(\"Available Models:\", availableModels); // Check if models are populated\n\n availableModels.forEach((modelId) => {\n const option = document.createElement(\"option\");\n option.value = modelId;\n option.textContent = modelId;\n document.getElementById(\"model-selection\").appendChild(option);\n });\n document.getElementById(\"model-selection\").value = selectedModel;\n }\n\n /*************** WebLLM logic ***************/\n const messages = [\n {\n content: \"You are a helpful AI agent helping users.\",\n role: \"system\",\n },\n ];\n\n let selectedModel = \"Llama-3-8B-Instruct-q4f32_1-MLC-1k\";\n\n // Callback function for initializing progress\n function updateEngineInitProgressCallback(report) {\n console.log(\"initialize\", report.progress);\n document.getElementById(\"download-status\").textContent = report.text;\n }\n\n // Create engine instance\n const engine = new webllm.MLCEngine();\n engine.setInitProgressCallback(updateEngineInitProgressCallback);\n\n async function initializeWebLLMEngine() {\n document.getElementById(\"download-status\").classList.remove(\"hidden\");\n selectedModel = document.getElementById(\"model-selection\").value;\n const config = {\n temperature: 1.0,\n top_p: 1,\n };\n await engine.reload(selectedModel, config);\n }\n\n async function streamingGenerating(messages, onUpdate, onFinish, onError) {\n try {\n let curMessage = \"\";\n const completion = await engine.chat.completions.create({\n stream: true,\n messages,\n });\n for await (const chunk of completion) {\n const curDelta = chunk.choices[0].delta.content;\n if (curDelta) {\n curMessage += curDelta;\n }\n onUpdate(curMessage);\n }\n const finalMessage = await engine.getMessage();\n onFinish(finalMessage);\n } catch (err) {\n onError(err);\n }\n }\n\n /*************** UI logic ***************/\n function onMessageSend() {\n const input = document.getElementById(\"user-input\").value.trim();\n const message = {\n content: input,\n role: \"user\",\n };\n if (input.length === 0) {\n return;\n }\n document.getElementById(\"send\").disabled = true;\n\n messages.push(message);\n appendMessage(message);\n\n document.getElementById(\"user-input\").value = \"\";\n document\n .getElementById(\"user-input\")\n .setAttribute(\"placeholder\", \"Generating...\");\n\n const aiMessage = {\n content: \"typing...\",\n role: \"assistant\",\n };\n appendMessage(aiMessage);\n\n const onFinishGenerating = (finalMessage) => {\n updateLastMessage(finalMessage);\n document.getElementById(\"send\").disabled = false;\n engine.runtimeStatsText().then(statsText => {\n document.getElementById('chat-stats').classList.remove('hidden');\n document.getElementById('chat-stats').textContent = statsText;\n });\n };\n\n streamingGenerating(\n messages,\n updateLastMessage,\n onFinishGenerating,\n console.error,\n );\n }\n\n function appendMessage(message) {\n const chatBox = document.getElementById(\"chat-box\");\n const container = document.createElement(\"div\");\n container.classList.add(\"message-container\");\n const newMessage = document.createElement(\"div\");\n newMessage.classList.add(\"message\");\n newMessage.textContent = message.content;\n\n if (message.role === \"user\") {\n container.classList.add(\"user\");\n } else {\n container.classList.add(\"assistant\");\n }\n\n container.appendChild(newMessage);\n chatBox.appendChild(container);\n chatBox.scrollTop = chatBox.scrollHeight; // Scroll to the latest message\n }\n\n function updateLastMessage(content) {\n const messageDoms = document\n .getElementById(\"chat-box\")\n .querySelectorAll(\".message\");\n const lastMessageDom = messageDoms[messageDoms.length - 1];\n lastMessageDom.textContent = content;\n }\n\n /*************** UI binding ***************/\n document.getElementById(\"download\").addEventListener(\"click\", function () {\n initializeWebLLMEngine().then(() => {\n document.getElementById(\"send\").disabled = false;\n });\n });\n document.getElementById(\"send\").addEventListener(\"click\", function () {\n onMessageSend();\n });\n});","style.css":"body,\nhtml {\n font-family: Arial, sans-serif;\n padding: 10px 20px;\n}\n\n.download-container {\n display: flex;\n justify-content: space-between;\n margin-bottom: 20px;\n}\n\n#download-status {\n border: solid 1px black;\n box-shadow: 0 10px 15px -3px rgba(0, 0, 0, .1), 0 4px 6px -2px rgba(0, 0, 0, .05);\n padding: 10px;\n}\n\n.chat-container {\n height: 400px;\n width: 100%;\n border: 2px solid black;\n display: flex;\n flex-direction: column;\n}\n\n.chat-box {\n overflow-y: scroll;\n background-color: #c3c3c3;\n border: 1px solid #ccc;\n padding: 5px;\n flex: 1 1;\n}\n\n.chat-stats {\n background-color: #D3ECEB;\n flex: 0 0;\n padding: 10px;\n font-size: 0.75rem;\n}\n\n.message-container {\n width: 100%;\n display: flex;\n}\n\n.message {\n padding: 10px;\n margin: 10px 0;\n border-radius: 10px;\n width: fit-content;\n}\n\n.message-container.user {\n justify-content: end;\n}\n\n.message-container.assistant {\n justify-content: start;\n}\n\n.message-container.user .message {\n background: #007bff;\n color: #fff;\n}\n\n.message-container.assistant .message {\n background: #f1f0f0;\n color: #333;\n}\n\n.chat-input-container {\n min-height: 40px;\n flex: 0 0;\n display: flex;\n}\n\n#user-input {\n width: 70%;\n padding: 10px;\n border: 1px solid #ccc;\n}\n\nbutton {\n width: 25%;\n padding: 10px;\n border: none;\n background-color: #007bff;\n color: white;\n cursor: pointer;\n}\n\nbutton:disabled {\n background-color: lightgray;\n cursor: not-allowed;\n}\n\nbutton:hover:not(:disabled) {\n background-color: #0056b3;\n}\n\n.hidden {\n display: none;\n}\n","index.html":"\n\n\n Simple Chatbot\n \n \n \n\n\n

\n Step 1: Initialize WebLLM and Download Model\n

\n \n \n

\n\n

\n Step 2: Chat\n

\n \n \n

\n\n \n\n"},"chat_history":[{"message":"we are trying to copy this example but there should be a model in the options dropdown but its not working https://github.com/mlc-ai/web-llm?tab=readme-ov-file#get-started","project_id":2765,"created_at":"2024-08-17T16:38:00.856208Z","output_tokens":null,"code_id":134255,"sender":"human","deleted":0,"input_tokens":null,"id":65289},{"message":"Got it! Let's make sure that the models are properly loaded into the dropdown. We will debug and ensure that the models appear in the dropdown list. \n\nFirst, let's double-check the part of the code where the models are being fetched and added to the dropdown. \n\nHere's the relevant part from your `script.js`:\n\n```javascript\nconst availableModels = webllm.prebuiltAppConfig.model_list.map(\n (m) => m.model_id,\n);\n\navailableModels.forEach((modelId) => {\n const option = document.createElement(\"option\");\n option.value = modelId;\n option.textContent = modelId;\n document.getElementById(\"model-selection\").appendChild(option);\n});\ndocument.getElementById(\"model-selection\").value = selectedModel;\n```\n\nTo debug, let's add some console logs to verify if `availableModels` is being populated correctly.\n\n```javascript\nconst availableModels = webllm.prebuiltAppConfig.model_list.map(\n (m) => m.model_id,\n);\n\nconsole.log(\"Available Models:\", availableModels); // Add this line\n\navailableModels.forEach((modelId) => {\n const option = document.createElement(\"option\");\n option.value = modelId;\n option.textContent = modelId;\n document.getElementById(\"model-selection\").appendChild(option);\n});\ndocument.getElementById(\"model-selection\").value = selectedModel;\n```\n\nAdd the above `console.log` line and check the browser's console to see if the `availableModels` array is being populated.\n\nIf it still doesn't work, there could be an issue with how `webllm.prebuiltAppConfig.model_list` is being accessed. \n\nTry running this code and let me know what you see in the console.","project_id":2765,"created_at":"2024-08-17T16:38:00.864950Z","output_tokens":null,"code_id":134255,"sender":"ai","deleted":0,"input_tokens":null,"id":65290},{"message":"no logs","project_id":2765,"created_at":"2024-08-17T16:42:24.245210Z","output_tokens":null,"code_id":134275,"sender":"human","deleted":0,"input_tokens":null,"id":65295},{"message":"Alright, it seems like the `availableModels` array is not being populated correctly. Let's take a closer look at how `webllm.prebuiltAppConfig.model_list` is accessed and ensure that the `prebuiltAppConfig` is properly initialized. \n\nFirst, let's verify that `webllm` is correctly loaded. We'll add some debug logs to check the `webllm` object itself.\n\n```javascript\nimport * as webllm from \"https://esm.run/@mlc-ai/web-llm\";\nconsole.log(\"webllm object:\", webllm); // Add this line to check if webllm is loaded\n\n/*************** WebLLM logic ***************/\nconst messages = [\n {\n content: \"You are a helpful AI agent helping users.\",\n role: \"system\",\n },\n];\n\nconsole.log(\"webllm prebuiltAppConfig:\", webllm.prebuiltAppConfig); // Add this line to check if prebuiltAppConfig is accessible\n\nconst availableModels = webllm.prebuiltAppConfig.model_list.map(\n (m) => m.model_id,\n);\n\nconsole.log(\"Available Models:\", availableModels); // Check if models are populated\n\nlet selectedModel = \"Llama-3-8B-Instruct-q4f32_1-MLC-1k\";\n\n// Callback function for initializing progress\nfunction updateEngineInitProgressCallback(report) {\n console.log(\"initialize\", report.progress);\n document.getElementById(\"download-status\").textContent = report.text;\n}\n\n// Create engine instance\nconst engine = new webllm.MLCEngine();\nengine.setInitProgressCallback(updateEngineInitProgressCallback);\n\nasync function initializeWebLLMEngine() {\n document.getElementById(\"download-status\").classList.remove(\"hidden\");\n selectedModel = document.getElementById(\"model-selection\").value;\n const config = {\n temperature: 1.0,\n top_p: 1,\n };\n await engine.reload(selectedModel, config);\n}\n\nasync function streamingGenerating(messages, onUpdate, onFinish, onError) {\n try {\n let curMessage = \"\";\n const completion = await engine.chat.completions.create({\n stream: true,\n messages,\n });\n for await (const chunk of completion) {\n const curDelta = chunk.choices[0].delta.content;\n if (curDelta) {\n curMessage += curDelta;\n }\n onUpdate(curMessage);\n }\n const finalMessage = await engine.getMessage();\n onFinish(finalMessage);\n } catch (err) {\n onError(err);\n }\n}\n\n/*************** UI logic ***************/\nfunction onMessageSend() {\n const input = document.getElementById(\"user-input\").value.trim();\n const message = {\n content: input,\n role: \"user\",\n };\n if (input.length === 0) {\n return;\n }\n document.getElementById(\"send\").disabled = true;\n\n messages.push(message);\n appendMessage(message);\n\n document.getElementById(\"user-input\").value = \"\";\n document\n .getElementById(\"user-input\")\n .setAttribute(\"placeholder\", \"Generating...\");\n\n const aiMessage = {\n content: \"typing...\",\n role: \"assistant\",\n };\n appendMessage(aiMessage);\n\n const onFinishGenerating = (finalMessage) => {\n updateLastMessage(finalMessage);\n document.getElementById(\"send\").disabled = false;\n engine.runtimeStatsText().then(statsText => {\n document.getElementById('chat-stats').classList.remove('hidden');\n document.getElementById('chat-stats').textContent = statsText;\n })\n };\n\n streamingGenerating(\n messages,\n updateLastMessage,\n onFinishGenerating,\n console.error,\n );\n}\n\nfunction appendMessage(message) {\n const chatBox = document.getElementById(\"chat-box\");\n const container = document.createElement(\"div\");\n container.classList.add(\"message-container\");\n const newMessage = document.createElement(\"div\");\n newMessage.classList.add(\"message\");\n newMessage.textContent = message.content;\n\n if (message.role === \"user\") {\n container.classList.add(\"user\");\n } else {\n container.classList.add(\"assistant\");\n }\n\n container.appendChild(newMessage);\n chatBox.appendChild(container);\n chatBox.scrollTop = chatBox.scrollHeight; // Scroll to the latest message\n}\n\nfunction updateLastMessage(content) {\n const messageDoms = document\n .getElementById(\"chat-box\")\n .querySelectorAll(\".message\");\n const lastMessageDom = messageDoms[messageDoms.length - 1];\n lastMessageDom.textContent = content;\n}\n\n/*************** UI binding ***************/\nconsole.log(\"webllm object:\", webllm); // Add this line to check if webllm is loaded\n\nconst availableModels = webllm.prebuiltAppConfig.model_list.map(\n (m) => m.model_id,\n);\n\nconsole.log(\"webllm prebuiltAppConfig:\", webllm.prebuiltAppConfig); // Add this line to check if prebuiltAppConfig is accessible\n\navailableModels.forEach((modelId) => {\n const option = document.createElement(\"option\");\n option.value = modelId;\n option.textContent = modelId;\n document.getElementById(\"model-selection\").appendChild(option);\n});\ndocument.getElementById(\"model-selection\").value = selectedModel;\ndocument.getElementById(\"download\").addEventListener(\"click\", function () {\n initializeWebLLMEngine().then(() => {\n document.getElementById(\"send\").disabled = false;\n });\n});\ndocument.getElementById(\"send\").addEventListener(\"click\", function () {\n onMessageSend();\n});\n```\n\nPlease add these logs and check the console again. This will help us understand if `webllm` and `prebuiltAppConfig` are being loaded correctly. Let me know what you see in the console output.","project_id":2765,"created_at":"2024-08-17T16:42:24.253030Z","output_tokens":null,"code_id":134275,"sender":"ai","deleted":0,"input_tokens":null,"id":65296},{"message":"still nothing in the console","project_id":2765,"created_at":"2024-08-17T16:45:57.183308Z","output_tokens":null,"code_id":134291,"sender":"human","deleted":0,"input_tokens":null,"id":65299},{"message":"Alright, it seems like the issue may be with how the `webllm` library is being imported or initialized. Let's ensure we're loading the library correctly, and we'll add some additional debugging steps.\n\nFirst, let's verify that the `webllm` object is being loaded. We can add a simple check at the very beginning of your `script.js` file to ensure the library is loaded properly.\n\n```javascript\nimport * as webllm from \"https://esm.run/@mlc-ai/web-llm\";\nconsole.log(\"webllm object:\", webllm); // Check if webllm is loaded\n\nif (!webllm || !webllm.prebuiltAppConfig || !webllm.prebuiltAppConfig.model_list) {\n console.error(\"webllm or prebuiltAppConfig is not loaded correctly.\");\n} else {\n console.log(\"webllm and prebuiltAppConfig loaded correctly.\");\n}\n\n/*************** WebLLM logic ***************/\nconst messages = [\n {\n content: \"You are a helpful AI agent helping users.\",\n role: \"system\",\n },\n];\n\nconsole.log(\"webllm prebuiltAppConfig:\", webllm.prebuiltAppConfig); // Check if prebuiltAppConfig is accessible\n\nconst availableModels = webllm.prebuiltAppConfig.model_list.map(\n (m) => m.model_id,\n);\n\nconsole.log(\"Available Models:\", availableModels); // Check if models are populated\n\nlet selectedModel = \"Llama-3-8B-Instruct-q4f32_1-MLC-1k\";\n\n// Callback function for initializing progress\nfunction updateEngineInitProgressCallback(report) {\n console.log(\"initialize\", report.progress);\n document.getElementById(\"download-status\").textContent = report.text;\n}\n\n// Create engine instance\nconst engine = new webllm.MLCEngine();\nengine.setInitProgressCallback(updateEngineInitProgressCallback);\n\nasync function initializeWebLLMEngine() {\n document.getElementById(\"download-status\").classList.remove(\"hidden\");\n selectedModel = document.getElementById(\"model-selection\").value;\n const config = {\n temperature: 1.0,\n top_p: 1,\n };\n await engine.reload(selectedModel, config);\n}\n\nasync function streamingGenerating(messages, onUpdate, onFinish, onError) {\n try {\n let curMessage = \"\";\n const completion = await engine.chat.completions.create({\n stream: true,\n messages,\n });\n for await (const chunk of completion) {\n const curDelta = chunk.choices[0].delta.content;\n if (curDelta) {\n curMessage += curDelta;\n }\n onUpdate(curMessage);\n }\n const finalMessage = await engine.getMessage();\n onFinish(finalMessage);\n } catch (err) {\n onError(err);\n }\n}\n\n/*************** UI logic ***************/\nfunction onMessageSend() {\n const input = document.getElementById(\"user-input\").value.trim();\n const message = {\n content: input,\n role: \"user\",\n };\n if (input.length === 0) {\n return;\n }\n document.getElementById(\"send\").disabled = true;\n\n messages.push(message);\n appendMessage(message);\n\n document.getElementById(\"user-input\").value = \"\";\n document\n .getElementById(\"user-input\")\n .setAttribute(\"placeholder\", \"Generating...\");\n\n const aiMessage = {\n content: \"typing...\",\n role: \"assistant\",\n };\n appendMessage(aiMessage);\n\n const onFinishGenerating = (finalMessage) => {\n updateLastMessage(finalMessage);\n document.getElementById(\"send\").disabled = false;\n engine.runtimeStatsText().then(statsText => {\n document.getElementById('chat-stats').classList.remove('hidden');\n document.getElementById('chat-stats').textContent = statsText;\n });\n };\n\n streamingGenerating(\n messages,\n updateLastMessage,\n onFinishGenerating,\n console.error,\n );\n}\n\nfunction appendMessage(message) {\n const chatBox = document.getElementById(\"chat-box\");\n const container = document.createElement(\"div\");\n container.classList.add(\"message-container\");\n const newMessage = document.createElement(\"div\");\n newMessage.classList.add(\"message\");\n newMessage.textContent = message.content;\n\n if (message.role === \"user\") {\n container.classList.add(\"user\");\n } else {\n container.classList.add(\"assistant\");\n }\n\n container.appendChild(newMessage);\n chatBox.appendChild(container);\n chatBox.scrollTop = chatBox.scrollHeight; // Scroll to the latest message\n}\n\nfunction updateLastMessage(content) {\n const messageDoms = document\n .getElementById(\"chat-box\")\n .querySelectorAll(\".message\");\n const lastMessageDom = messageDoms[messageDoms.length - 1];\n lastMessageDom.textContent = content;\n}\n\n/*************** UI binding ***************/\ndocument.getElementById(\"download\").addEventListener(\"click\", function () {\n initializeWebLLMEngine().then(() => {\n document.getElementById(\"send\").disabled = false;\n });\n});\ndocument.getElementById(\"send\").addEventListener(\"click\", function () {\n onMessageSend();\n});\n```\n\n### Steps to Follow:\n1. Add the above code to your `script.js` file.\n2. Open the browser console and reload the page.\n3. Check the console for any messages.\n\nThis will help us determine if the `webllm` library is being loaded and if the `prebuiltAppConfig` is accessible. Let me know what you see in the console output.","project_id":2765,"created_at":"2024-08-17T16:45:57.190380Z","output_tokens":null,"code_id":134291,"sender":"ai","deleted":0,"input_tokens":null,"id":65300},{"message":"i think scriptjs is not loading from index","project_id":2765,"created_at":"2024-08-17T16:50:17.143304Z","output_tokens":null,"code_id":134314,"sender":"human","deleted":0,"input_tokens":null,"id":65305},{"message":"Let's make sure that `script.js` is correctly referenced and loaded in your `index.html`. Here are a few steps to ensure everything is set up properly:\n\n1. Verify the path to `script.js` in your `index.html`.\n2. Add a `console.log` statement at the very beginning of your `script.js` to see if it outputs anything in the console.\n3. Check for any console errors in the browser that might indicate issues with loading the script.\n\nFirst, let's ensure the script reference in your `index.html` is correct:\n\n```html\n\n\n\n Simple Chatbot\n \n \n\n\n

\n Step 1: Initialize WebLLM and Download Model\n

\n \n \n

\n\n

\n Step 2: Chat\n

\n \n \n

\n\n \n\n\n```\n\nNext, add a `console.log` statement at the very beginning of your `script.js` to confirm it is loading:\n\n```javascript\nconsole.log(\"script.js loaded\"); // Add this line at the very top of script.js\n\nimport * as webllm from \"https://esm.run/@mlc-ai/web-llm\";\nconsole.log(\"webllm object:\", webllm); // Check if webllm is loaded\n\nif (!webllm || !webllm.prebuiltAppConfig || !webllm.prebuiltAppConfig.model_list) {\n console.error(\"webllm or prebuiltAppConfig is not loaded correctly.\");\n} else {\n console.log(\"webllm and prebuiltAppConfig loaded correctly.\");\n}\n\n/*************** WebLLM logic ***************/\nconst messages = [\n {\n content: \"You are a helpful AI agent helping users.\",\n role: \"system\",\n },\n];\n\nconsole.log(\"webllm prebuiltAppConfig:\", webllm.prebuiltAppConfig); // Check if prebuiltAppConfig is accessible\n\nconst availableModels = webllm.prebuiltAppConfig.model_list.map(\n (m) => m.model_id,\n);\n\nconsole.log(\"Available Models:\", availableModels); // Check if models are populated\n\nlet selectedModel = \"Llama-3-8B-Instruct-q4f32_1-MLC-1k\";\n\n// Callback function for initializing progress\nfunction updateEngineInitProgressCallback(report) {\n console.log(\"initialize\", report.progress);\n document.getElementById(\"download-status\").textContent = report.text;\n}\n\n// Create engine instance\nconst engine = new webllm.MLCEngine();\nengine.setInitProgressCallback(updateEngineInitProgressCallback);\n\nasync function initializeWebLLMEngine() {\n document.getElementById(\"download-status\").classList.remove(\"hidden\");\n selectedModel = document.getElementById(\"model-selection\").value;\n const config = {\n temperature: 1.0,\n top_p: 1,\n };\n await engine.reload(selectedModel, config);\n}\n\nasync function streamingGenerating(messages, onUpdate, onFinish, onError) {\n try {\n let curMessage = \"\";\n const completion = await engine.chat.completions.create({\n stream: true,\n messages,\n });\n for await (const chunk of completion) {\n const curDelta = chunk.choices[0].delta.content;\n if (curDelta) {\n curMessage += curDelta;\n }\n onUpdate(curMessage);\n }\n const finalMessage = await engine.getMessage();\n onFinish(finalMessage);\n } catch (err) {\n onError(err);\n }\n}\n\n/*************** UI logic ***************/\nfunction onMessageSend() {\n const input = document.getElementById(\"user-input\").value.trim();\n const message = {\n content: input,\n role: \"user\",\n };\n if (input.length === 0) {\n return;\n }\n document.getElementById(\"send\").disabled = true;\n\n messages.push(message);\n appendMessage(message);\n\n document.getElementById(\"user-input\").value = \"\";\n document\n .getElementById(\"user-input\")\n .setAttribute(\"placeholder\", \"Generating...\");\n\n const aiMessage = {\n content: \"typing...\",\n role: \"assistant\",\n };\n appendMessage(aiMessage);\n\n const onFinishGenerating = (finalMessage) => {\n updateLastMessage(finalMessage);\n document.getElementById(\"send\").disabled = false;\n engine.runtimeStatsText().then(statsText => {\n document.getElementById('chat-stats').classList.remove('hidden');\n document.getElementById('chat-stats').textContent = statsText;\n });\n };\n\n streamingGenerating(\n messages,\n updateLastMessage,\n onFinishGenerating,\n console.error,\n );\n}\n\nfunction appendMessage(message) {\n const chatBox = document.getElementById(\"chat-box\");\n const container = document.createElement(\"div\");\n container.classList.add(\"message-container\");\n const newMessage = document.createElement(\"div\");\n newMessage.classList.add(\"message\");\n newMessage.textContent = message.content;\n\n if (message.role === \"user\") {\n container.classList.add(\"user\");\n } else {\n container.classList.add(\"assistant\");\n }\n\n container.appendChild(newMessage);\n chatBox.appendChild(container);\n chatBox.scrollTop = chatBox.scrollHeight; // Scroll to the latest message\n}\n\nfunction updateLastMessage(content) {\n const messageDoms = document\n .getElementById(\"chat-box\")\n .querySelectorAll(\".message\");\n const lastMessageDom = messageDoms[messageDoms.length - 1];\n lastMessageDom.textContent = content;\n}\n\n/*************** UI binding ***************/\ndocument.getElementById(\"download\").addEventListener(\"click\", function () {\n initializeWebLLMEngine().then(() => {\n document.getElementById(\"send\").disabled = false;\n });\n});\ndocument.getElementById(\"send\").addEventListener(\"click\", function () {\n onMessageSend();\n});\n```\n\n### Steps to Follow:\n1. Ensure the script reference in `index.html` is correct.\n2. Add the `console.log(\"script.js loaded\");` statement at the very top of `script.js`.\n3. Open the browser console and reload the page to see if you get the \"script.js loaded\" message.\n\nLet me know what you see in the console output after these steps.","project_id":2765,"created_at":"2024-08-17T16:50:17.170609Z","output_tokens":null,"code_id":134314,"sender":"ai","deleted":0,"input_tokens":null,"id":65306},{"message":"ERROR: Uncaught SyntaxError: Cannot use import statement outside a module at 1:1 in script.js","project_id":2765,"created_at":"2024-08-17T16:51:01.808208Z","output_tokens":null,"code_id":134322,"sender":"human","deleted":0,"input_tokens":null,"id":65307},{"message":"The error indicates that the `import` statement is not being recognized because the script is not being treated as a module. To fix this, we need to ensure that the script is treated as a module by adding the `type=\"module\"` attribute to the ` \n\n\n```\n\n### Steps to Follow:\n1. Update the `","project_id":2765,"created_at":"2024-08-17T16:56:04.883748Z","output_tokens":null,"code_id":134346,"sender":"human","deleted":0,"input_tokens":null,"id":65325},{"message":"Got it! Let's switch to using the CDN for loading the `webllm` library. \n\n### Steps:\n1. Add the CDN script to your `index.html`.\n2. Remove the `import` statement from `script.js`.\n3. Ensure that `webllm` is accessible globally.\n\nHere is how you can update your files:\n\n### Updated `index.html`:\n```html\n\n\n\n Simple Chatbot\n \n \n \n\n\n

\n Step 1: Initialize WebLLM and Download Model\n

\n \n \n

\n\n

\n Step 2: Chat\n

\n \n \n

\n\n \n\n\n```\n\n### Updated `script.js`:\nRemove the `import` statement and directly access `webllm`:\n\n```javascript\nconsole.log(\"script.js loaded\"); // Confirm script is loaded\n\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n console.log(\"DOM fully loaded and parsed\");\n console.log(\"webllm object:\", webllm); // Check if webllm is loaded\n\n if (!webllm || !webllm.prebuiltAppConfig || !webllm.prebuiltAppConfig.model_list) {\n console.error(\"webllm or prebuiltAppConfig is not loaded correctly.\");\n } else {\n console.log(\"webllm and prebuiltAppConfig loaded correctly.\");\n\n const availableModels = webllm.prebuiltAppConfig.model_list.map(\n (m) => m.model_id,\n );\n\n console.log(\"Available Models:\", availableModels); // Check if models are populated\n\n availableModels.forEach((modelId) => {\n const option = document.createElement(\"option\");\n option.value = modelId;\n option.textContent = modelId;\n document.getElementById(\"model-selection\").appendChild(option);\n });\n document.getElementById(\"model-selection\").value = selectedModel;\n }\n\n /*************** WebLLM logic ***************/\n const messages = [\n {\n content: \"You are a helpful AI agent helping users.\",\n role: \"system\",\n },\n ];\n\n let selectedModel = \"Llama-3-8B-Instruct-q4f32_1-MLC-1k\";\n\n // Callback function for initializing progress\n function updateEngineInitProgressCallback(report) {\n console.log(\"initialize\", report.progress);\n document.getElementById(\"download-status\").textContent = report.text;\n }\n\n // Create engine instance\n const engine = new webllm.MLCEngine();\n engine.setInitProgressCallback(updateEngineInitProgressCallback);\n\n async function initializeWebLLMEngine() {\n document.getElementById(\"download-status\").classList.remove(\"hidden\");\n selectedModel = document.getElementById(\"model-selection\").value;\n const config = {\n temperature: 1.0,\n top_p: 1,\n };\n await engine.reload(selectedModel, config);\n }\n\n async function streamingGenerating(messages, onUpdate, onFinish, onError) {\n try {\n let curMessage = \"\";\n const completion = await engine.chat.completions.create({\n stream: true,\n messages,\n });\n for await (const chunk of completion) {\n const curDelta = chunk.choices[0].delta.content;\n if (curDelta) {\n curMessage += curDelta;\n }\n onUpdate(curMessage);\n }\n const finalMessage = await engine.getMessage();\n onFinish(finalMessage);\n } catch (err) {\n onError(err);\n }\n }\n\n /*************** UI logic ***************/\n function onMessageSend() {\n const input = document.getElementById(\"user-input\").value.trim();\n const message = {\n content: input,\n role: \"user\",\n };\n if (input.length === 0) {\n return;\n }\n document.getElementById(\"send\").disabled = true;\n\n messages.push(message);\n appendMessage(message);\n\n document.getElementById(\"user-input\").value = \"\";\n document\n .getElementById(\"user-input\")\n .setAttribute(\"placeholder\", \"Generating...\");\n\n const aiMessage = {\n content: \"typing...\",\n role: \"assistant\",\n };\n appendMessage(aiMessage);\n\n const onFinishGenerating = (finalMessage) => {\n updateLastMessage(finalMessage);\n document.getElementById(\"send\").disabled = false;\n engine.runtimeStatsText().then(statsText => {\n document.getElementById('chat-stats').classList.remove('hidden');\n document.getElementById('chat-stats').textContent = statsText;\n });\n };\n\n streamingGenerating(\n messages,\n updateLastMessage,\n onFinishGenerating,\n console.error,\n );\n }\n\n function appendMessage(message) {\n const chatBox = document.getElementById(\"chat-box\");\n const container = document.createElement(\"div\");\n container.classList.add(\"message-container\");\n const newMessage = document.createElement(\"div\");\n newMessage.classList.add(\"message\");\n newMessage.textContent = message.content;\n\n if (message.role === \"user\") {\n container.classList.add(\"user\");\n } else {\n container.classList.add(\"assistant\");\n }\n\n container.appendChild(newMessage);\n chatBox.appendChild(container);\n chatBox.scrollTop = chatBox.scrollHeight; // Scroll to the latest message\n }\n\n function updateLastMessage(content) {\n const messageDoms = document\n .getElementById(\"chat-box\")\n .querySelectorAll(\".message\");\n const lastMessageDom = messageDoms[messageDoms.length - 1];\n lastMessageDom.textContent = content;\n }\n\n /*************** UI binding ***************/\n document.getElementById(\"download\").addEventListener(\"click\", function () {\n initializeWebLLMEngine().then(() => {\n document.getElementById(\"send\").disabled = false;\n });\n });\n document.getElementById(\"send\").addEventListener(\"click\", function () {\n onMessageSend();\n });\n});\n```\n\n### Steps to Follow:\n1. Update your `index.html` to include the CDN script tag.\n2. Update your `script.js` to remove the `import` statement and ensure `webllm` is accessed globally.\n3. Reload the page and check the browser console for any messages or errors.\n\nThis should help us get `webllm` loaded correctly. Let me know what you see in the console output.","project_id":2765,"created_at":"2024-08-17T16:56:04.891044Z","output_tokens":null,"code_id":134346,"sender":"ai","deleted":0,"input_tokens":null,"id":65326}],"assets":[],"messages_sent":15,"comments":[],"steps":[]}