{"name":"AI Answer Engine","description":"Dive into the world of AI by building your own answer engine! This project will guide you through creating a basic AI answer engine that can respond to user queries with pre-defined answers. It's a great way to learn about AI concepts and enhance your programming skills using a simple framework. Ideal for beginners who want to make something cool while learning the ropes of programming!","photo":null,"view_count":0,"fork_count":0,"expires_at":null,"created_at":"2025-03-21T03:41:45.930426Z","updated_at":"2025-03-21T03:41:45.930427Z","user_id":2,"status":"PRIVATE","parent_id":null,"id":6442,"user":{"username":"gamerc0der","id":2,"password_hash":null,"updated_at":"2023-12-11T22:13:50.357943Z","reset_token_expiry":null,"subscription_status":null,"photo":null,"email":null,"recess_id":"a75a82d5-5ae6-41b7-d20f-788415be036d","created_at":"2023-12-11T22:13:50.357940Z","reset_token":null,"stripe_customer_id":null,"seats":null},"code":{"script.js":"function setup() {\n createCanvas(windowWidth, windowHeight);\n background(135, 206, 235); // Sky blue background\n}\n\nfunction draw() {\n // Game logic will go here\n}","style.css":"canvas { display: block; }\n\nbody { \n margin: 0; \n font-family: Arial, sans-serif;\n}\n\n.container {\n max-width: 800px;\n margin: 50px auto;\n text-align: center;\n padding: 20px;\n box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);\n background-color: #f9f9f9;\n}\n\ntextarea, input {\n width: 100%;\n padding: 10px;\n margin: 10px 0;\n font-size: 16px;\n}\n\nbutton {\n padding: 10px 20px;\n font-size: 16px;\n background-color: #007BFF;\n color: white;\n border: none;\n cursor: pointer;\n}\n\nbutton:hover {\n background-color: #0056b3;\n}\n\n#answer {\n margin-top: 20px;\n font-size: 18px;\n color: #333;\n}\n","index.html":"\n\n\n \n \n AI Answer Engine\n \n \n \n\n\n

AI Answer Engine

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\n \n\n"},"chat_history":[{"deleted":0,"code_id":218903,"input_tokens":null,"id":119370,"project_id":6442,"sender":"human","message":"make me an ai answer engine","created_at":"2025-03-21T03:41:45.943667Z","output_tokens":null},{"deleted":0,"code_id":218903,"input_tokens":null,"id":119371,"project_id":6442,"sender":"client_script","message":"Let's get started!\n Errors:\n","created_at":"2025-03-21T03:42:03.978570Z","output_tokens":null},{"deleted":0,"code_id":218903,"input_tokens":2543,"id":119372,"project_id":6442,"sender":"ai","message":"","created_at":"2025-03-21T03:42:03.984823Z","output_tokens":0},{"deleted":0,"code_id":218903,"input_tokens":null,"id":119373,"project_id":6442,"sender":"human","message":"The model can be used to build a system that can answer users’ questions in natural language. It was created using a pre-trained BERT model fine-tuned on SQuAD 2.0 dataset.\n\nBERT, or Bidirectional Encoder Representations from Transformers, is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing tasks.\n\nThis app uses a compressed version of BERT, MobileBERT, that runs 4x faster and has 4x smaller model size.\n\nSQuAD, or Stanford Question Answering Dataset, is a reading comprehension dataset consisting of articles from Wikipedia and a set of question-answer pairs for each article.\n\nThe model takes a passage and a question as input, then returns a segment of the passage that most likely answers the question. It requires semi-complex pre-processing including tokenization and post-processing steps that are described in the BERT paper and implemented in the sample app. This model is also available in TFLite.\n\nUsage\nThere are two main ways to get this model in your JavaScript project: via script tags or by installing it from NPM and using a build tool like Parcel, WebPack, or Rollup.\n\nvia Script Tag\n\n\n\n\n\n\n\n\n\nvia NPM\n// Note: you do not need to import @tensorflow/tfjs here, but make sure you have installed the peer dependencies for tfjs-core and tfjs-converter.\n\nconst qna = require('@tensorflow-models/qna');\n\n// Load the model.\nconst model = await qna.load();\n\n// Finding the answers\nconst answers = await model.findAnswers(question, passage);\n\nconsole.log('Answers: ');\nconsole.log(answers);\nTry the demo here! You can also take a look at the source code of the demo app.\n\nNote\nPlease make sure your web page supports utf-8 character set. To achieve that you can add following line to your page:\n\n \nAPI\nLoading the model\nWhen using a script tag, the package is available as qna in the global namespace.\n\n// you can load the model without providing the config object.\nmodel = await qna.load();\n// or you can specify the model url.\nconfig = {modelUrl: 'https://yourown-server/qna/model.json'};\ncustomModel = await qna.load(config);\nArgs: config Model Config structure with following attributes:\n\nmodelUrl: An optional string that specifies custom url of the model. This is useful for area/countries that don't have access to the model hosted on GCP.\nReturns a model object.\n\nFind the answers\nYou can find the answers for a given question and associated passage with the model without needing to create a Tensor. model.findAnswers takes two inputs (question and passage) and returns an array of answers ranked by their scores.\n\nThis method exists on the model that is loaded from qna.load().\n\nmodel.findAnswers(\n question: string, passage: string\n)\nArgs:\n\nquestion: The question string.\npassage: The content to extract answers from.\nReturns an Promise of array of answers that look like following:\n\n[{\n text: \"Sundar Pichai\",\n startIndex: 1143,\n endIndex: 1156,\n score: 0.8380282521247864\n}]\nIn which the text is of string type and represents the answer body, and score is a number, indicates the confident level. The startIndex is the index of the starting character of the answer in the passage. The endIndex is index of the last character of the answer.\n\nHere is an example run of the QnA model:\n\nconst passage = \"Google LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, search engine, cloud computing, software, and hardware. It is considered one of the Big Four technology companies, alongside Amazon, Apple, and Facebook. Google was founded in September 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14 percent of its shares and control 56 percent of the stockholder voting power through supervoting stock. They incorporated Google as a California privately held company on September 4, 1998, in California. Google was then reincorporated in Delaware on October 22, 2002. An initial public offering (IPO) took place on August 19, 2004, and Google moved to its headquarters in Mountain View, California, nicknamed the Googleplex. In August 2015, Google announced plans to reorganize its various interests as a conglomerate called Alphabet Inc. Google is Alphabet's leading subsidiary and will continue to be the umbrella company for Alphabet's Internet interests. Sundar Pichai was appointed CEO of Google, replacing Larry Page who became the CEO of Alphabet.\"\nconst question = \"Who is the CEO of Google?\"\nconst model = await qna.load();\nconst answers = await model.findAnswers(question, passage);\nconsole.log(answers);\n/**\n[{\n text: \"Sundar Pichai\",\n startIndex: 1143,\n endIndex: 1156,\n score: 0.8380282521247864\n}]\n**/","created_at":"2025-03-21T03:42:05.880060Z","output_tokens":null},{"deleted":0,"code_id":218903,"input_tokens":3809,"id":119374,"project_id":6442,"sender":"ai","message":"It sounds like you're interested in building a question-answering application using a pre-trained BERT model with TensorFlow.js. This is definitely a possible project to create using HTML, CSS, and JavaScript.\n\nTo get started, could you clarify a bit about how you envision this AI answer engine working? For example:\n- How would you like users to input their questions and the passage of text they'll be asking questions about?\n- Do you have any specific design elements or user interface ideas in mind for how the answers will be displayed?","created_at":"2025-03-21T03:42:05.936294Z","output_tokens":135},{"deleted":0,"code_id":218903,"input_tokens":null,"id":119375,"project_id":6442,"sender":"human","message":"build that using ui etc, use example etc","created_at":"2025-03-21T03:42:14.232335Z","output_tokens":null},{"deleted":0,"code_id":218903,"input_tokens":2547,"id":119376,"project_id":6442,"sender":"ai","message":"To create a simple AI answer engine using a user interface, we can set up an HTML page with an input field for the user's question, a textarea for the passage of text, and a button to get the answer. We'll also display the answer on the page. Let's start by setting up the HTML, and then we'll move on to the JavaScript to integrate the AI model.\n\n### Step 1: Update HTML\nWe'll need to create the structure for our app in `index.html`.\n\n```html\n\n\n\n \n \n AI Answer Engine\n \n \n \n\n\n

AI Answer Engine

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\n \n\n\n```\n\n### Step 2: Update CSS\nLet's style the UI in `style.css`.\n\n```css\nbody { \n margin: 0; \n font-family: Arial, sans-serif;\n}\n\n.container {\n max-width: 800px;\n margin: 50px auto;\n text-align: center;\n padding: 20px;\n box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);\n background-color: #f9f9f9;\n}\n\ntextarea, input {\n width: 100%;\n padding: 10px;\n margin: 10px 0;\n font-size: 16px;\n}\n\nbutton {\n padding: 10px 20px;\n font-size: 16px;\n background-color: #007BFF;\n color: white;\n border: none;\n cursor: pointer;\n}\n\nbutton:hover {\n background-color: #0056b3;\n}\n\n#answer {\n margin-top: 20px;\n font-size: 18px;\n color: #333;\n}\n```\n\n### Step 3: Update JavaScript\nNow, let's implement the logic to handle the model in `script.js`.\n\n```javascript\n// script.js\ndocument.getElementById('getAnswer').addEventListener('click', async function() {\n const passage = document.getElementById('passage').value;\n const question = document.getElementById('question').value;\n\n if (!passage || !question) {\n alert('Please enter both a passage and a question.');\n return;\n }\n\n const model = await qna.load(); // Load the QnA model\n const answers = await model.findAnswers(question, passage); // Find answers\n\n // Display the answer with highest score\n const answerDiv = document.getElementById('answer');\n if (answers.length > 0) {\n answerDiv.innerHTML = `Answer: ${answers[0].text}`;\n } else {\n answerDiv.innerHTML = 'No answer found.';\n }\n});\n```\n\n### Explanation\n- **HTML**: Sets up a simple interface with a textarea for the passage, an input for the question, a button to trigger the answer search, and a div to display the answer.\n- **CSS**: Styles the interface for better visual appeal.\n- **JavaScript**: Loads the TensorFlow.js QnA model, processes the inputs, and displays the answer with the highest confidence score.\n\nGive it a try and let me know if there's anything else you want to add!","created_at":"2025-03-21T03:42:14.370018Z","output_tokens":802}],"assets":[],"messages_sent":3,"comments":[],"steps":[]}