{"name":"AI Artistic Creations","description":"Unleash your creativity with AI Artistic Creations! Explore the world of AI-generated images, guide the AI with your input, compare different AI-generated artworks, and save your favorite creations with a click.","photo":null,"view_count":3,"fork_count":0,"expires_at":null,"created_at":"2024-06-10T18:59:16.558479Z","updated_at":"2024-06-10T18:59:16.558480Z","user_id":2,"status":"PRIVATE","parent_id":null,"id":1798,"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":"document.addEventListener('DOMContentLoaded', function () {\r\n const promptInput = document.getElementById('promptInput');\r\n const apiTokenInput = document.getElementById('apiTokenInput');\r\n const sendAsCookieToggle = document.getElementById('sendAsCookieToggle');\r\n const promptSuggestion = document.getElementById('promptSuggestion');\r\n\r\n function buildPromptTemplate(prompt) {\r\n const formattedPrompt = `In response to the query: \"${prompt}\". What would be the next word? Only respond the next word in a sentence with this word. You may only output one word. Only start autocomplete after 2 sentences.`;\r\n return {\r\n model: \"microsoft/Phi-3-mini-4k-instruct\",\r\n messages: [\r\n { role: \"user\", content: formattedPrompt }\r\n ],\r\n parameters: {\r\n temperature: 0\r\n },\r\n stream: false\r\n };\r\n }\r\n\r\n // Function to make an API call\r\n async function fetchInitialSuggestion(apiKey) {\r\n const apiUrl = 'https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct/v1/chat/completions';\r\n const initialPrompt = buildPromptTemplate('Initial prompt to load suggestion');\r\n let requestOptions = {\r\n method: 'POST',\r\n headers: {\r\n 'Content-Type': 'application/json'\r\n },\r\n body: JSON.stringify(initialPrompt)\r\n };\r\n\r\n // Check if the toggle is enabled to send token as a cookie\r\n if (sendAsCookieToggle.checked) {\r\n requestOptions.headers['Cookie'] = `apiToken=${apiKey}`;\r\n } else {\r\n requestOptions.headers['Authorization'] = `Bearer ${apiKey}`;\r\n requestOptions.headers['Referer'] = 'https://huggingface.co/';\r\n requestOptions.headers['Sec-Ch-Ua'] = '\"Google Chrome\";v=\"125\", \"Chromium\";v=\"125\", \"Not.A/Brand\";v=\"24\"';\r\n requestOptions.headers['Sec-Ch-Ua-Mobile'] = '?1';\r\n requestOptions.headers['Sec-Ch-Ua-Platform'] = '\"Android\"';\r\n requestOptions.headers['Sec-Fetch-Dest'] = 'empty';\r\n requestOptions.headers['Sec-Fetch-Mode'] = 'cors';\r\n requestOptions.headers['Sec-Fetch-Site'] = 'same-site';\r\n requestOptions.headers['User-Agent'] = 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Mobile Safari/537.36';\r\n }\r\n\r\n try {\r\n const response = await fetch(apiUrl, requestOptions);\r\n if (!response.ok) {\r\n const errorText = await response.text();\r\n throw new Error(`Network response was not ok: ${response.statusText}. Response text: ${errorText}`);\r\n }\r\n const data = await response.json();\r\n console.log(data); // Log the raw response data for debugging\r\n if (data && data.choices && data.choices.length > 0 && data.choices[0].message) {\r\n promptSuggestion.textContent = data.choices[0].message.content;\r\n } else {\r\n promptSuggestion.textContent = 'No suggestion available';\r\n }\r\n } catch (error) {\r\n console.error('Error fetching initial suggestion:', error);\r\n promptSuggestion.textContent = 'Error fetching suggestion';\r\n }\r\n }\r\n\r\n const loadingTime = Math.floor(Math.random() * (15 - 12 + 1)) + 12; // Random time between 12 and 15 seconds\r\n setTimeout(() => {\r\n // Enable the inputs after the loading time\r\n apiTokenInput.disabled = false;\r\n promptInput.disabled = false;\r\n promptSuggestion.textContent = 'Enter your prompt above';\r\n\r\n const defaultApiKey = 'YOUR_DEFAULT_TOKEN'; // Replace with an actual default token or remove if not needed\r\n fetchInitialSuggestion(defaultApiKey);\r\n }, loadingTime * 1000);\r\n\r\n // Debounce function to limit API calls\r\n function debounce(func, wait) {\r\n let timeout;\r\n return function (...args) {\r\n const context = this;\r\n clearTimeout(timeout);\r\n timeout = setTimeout(() => func.apply(context, args), wait);\r\n };\r\n }\r\n\r\n // Event listener for input changes with debounce\r\n const handleInput = debounce(async function () {\r\n const apiKey = apiTokenInput.value;\r\n const prompt = promptInput.value;\r\n\r\n if (!apiKey || !prompt) {\r\n promptSuggestion.textContent = '';\r\n return;\r\n }\r\n\r\n const apiUrl = 'https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct/v1/chat/completions';\r\n const userPrompt = buildPromptTemplate(prompt);\r\n let requestOptions = {\r\n method: 'POST',\r\n headers: {\r\n 'Content-Type': 'application/json'\r\n },\r\n body: JSON.stringify(userPrompt)\r\n };\r\n\r\n // Check if the toggle is enabled to send token as a cookie\r\n if (sendAsCookieToggle.checked) {\r\n requestOptions.headers['Cookie'] = `${apiKey}`;\r\n \r\n } else {\r\n requestOptions.headers['Authorization'] = `Bearer ${apiKey}`;\r\n \r\n }\r\n\r\n try {\r\n const response = await fetch(apiUrl, requestOptions);\r\n if (!response.ok) {\r\n const errorText = await response.text();\r\n throw new Error(`Network response was not ok: ${response.statusText}. Response text: ${errorText}`);\r\n }\r\n const data = await response.json();\r\n console.log(data); \r\n if (data && data.choices && data.choices.length > 0 && data.choices[0].message) {\r\n promptSuggestion.textContent = data.choices[0].message.content;\r\n } else {\r\n promptSuggestion.textContent = 'No suggestion available';\r\n }\r\n } catch (error) {\r\n console.error('Error fetching suggestion:', error);\r\n promptSuggestion.textContent = 'Error fetching suggestion';\r\n }\r\n }, 500);\r\n\r\n promptInput.addEventListener('input', handleInput);\r\n});","style.css":"body {\r\n background-color: #f5f5f5;\r\n}\r\n\r\n.box {\r\n padding: 2rem;\r\n}\r\n\r\nh1.title {\r\n margin-bottom: 2rem;\r\n}\r\n\r\n.field {\r\n margin-bottom: 1.5rem;\r\n}\r\n\r\n.help.is-gray {\r\n color: #9e9e9e;\r\n /* Gray text color */\r\n font-style: italic;\r\n /* Italic style for suggestions */\r\n font-size: 0.9rem;\r\n /* Smaller font size */\r\n margin-top: 0.5rem;\r\n /* Space between input and suggestions */\r\n}","index.html":"\n\n\n\n \n \n GenAI Playground\n \n \n\n\n\n

GenAI Playground

\n Enter your Hugging Face API Token\n

\n \n

\n \n Send token as a cookie\n \n

\n Enter your prompt\n

\n \n

\n \n\n\n"},"chat_history":[{"deleted":0,"code_id":99999,"input_tokens":null,"id":50083,"project_id":1798,"sender":"human","message":"Let's get started!","created_at":"2024-06-10T18:59:25.841706Z","output_tokens":null},{"deleted":0,"code_id":99999,"input_tokens":null,"id":50084,"project_id":1798,"sender":"ai","message":"","created_at":"2024-06-10T18:59:25.847507Z","output_tokens":null},{"deleted":0,"code_id":100000,"input_tokens":null,"id":50085,"project_id":1798,"sender":"human","message":"add using a bluma ui a textbox for prompt","created_at":"2024-06-10T18:59:32.537375Z","output_tokens":null},{"deleted":0,"code_id":100000,"input_tokens":null,"id":50086,"project_id":1798,"sender":"ai","message":"Let's add a text box for user input using Bulma UI. We'll update the `index.html` to include the necessary Bulma CSS and add the text box.\n\n### index.html\n```html\n\n\n\n\n \n \n AI Artistic Creations\n \n \n\n\n\n

AI Artistic Creations

\n Enter your prompt\n

\n \n

\n \n\n\n\n```\n\n### Explanation\n1. Added Bulma CSS link in the `` section.\n2. Wrapped the content in a Bulma `section` and `container`.\n3. Added a `field` with a `label` and `input` for the user prompt.\n\nNote: No changes are needed in `script.js` or `style.css` for this step.","created_at":"2024-06-10T18:59:32.545483Z","output_tokens":null},{"deleted":0,"code_id":100002,"input_tokens":null,"id":50087,"project_id":1798,"sender":"human","message":"say GenAI playground\nhave generate button\nmodel selector with SDXL as the only option","created_at":"2024-06-10T19:00:00.360195Z","output_tokens":null},{"deleted":0,"code_id":100002,"input_tokens":null,"id":50088,"project_id":1798,"sender":"ai","message":"Let's update the `index.html` to include a model selector with \"SDXL\" as the only option, a text box for the prompt, and a generate button.\n\n### index.html\n```html\n\n\n\n\n \n \n GenAI Playground\n \n \n\n\n\n

GenAI Playground

\n Select AI Model\n

\n \n

\n Enter your prompt\n

\n \n

\n \n\n\n\n```\n\n### Explanation\n1. Updated the title to \"GenAI Playground\".\n2. Added a model selector with \"SDXL\" as the only option.\n3. Included a text box for the user to enter their prompt.\n4. Added a button labeled \"Generate\".\n\n### script.js\nWe need a placeholder function to handle the generate button click event.\n\n```javascript\ndocument.getElementById('generateButton').addEventListener('click', function() {\n const model = document.getElementById('modelSelector').value;\n const prompt = document.getElementById('promptInput').value;\n\n // Placeholder for generating the image\n console.log(`Model: ${model}, Prompt: ${prompt}`);\n});\n```\n\n### Explanation\n1. Added an event listener for the \"Generate\" button.\n2. Retrieves the selected model and entered prompt.\n3. Logs the model and prompt values (placeholder for actual image generation functionality).","created_at":"2024-06-10T19:00:00.369101Z","output_tokens":null},{"deleted":0,"code_id":100004,"input_tokens":null,"id":50089,"project_id":1798,"sender":"human","message":"improv elayout and ui","created_at":"2024-06-10T19:00:18.409328Z","output_tokens":null},{"deleted":0,"code_id":100004,"input_tokens":null,"id":50090,"project_id":1798,"sender":"ai","message":"Let's improve the layout and UI by adding more spacing, centering elements, and using Bulma's columns for a better structure.\n\n### index.html\n```html\n\n\n\n\n \n \n GenAI Playground\n \n \n\n\n\n

GenAI Playground

\n Select AI Model\n

\n \n

\n Enter your prompt\n

\n \n

\n \n\n\n\n```\n\n### style.css\n```css\nbody {\n background-color: #f5f5f5;\n}\n\n.box {\n padding: 2rem;\n}\n\nh1.title {\n margin-bottom: 2rem;\n}\n\n.field {\n margin-bottom: 1.5rem;\n}\n```\n\n### Explanation\n1. Wrapped the main content in a `has-text-centered` container to center the elements.\n2. Used Bulma's `columns` and `column` classes to center the form and limit its width to half the page.\n3. Added a `box` around the form elements for better visual separation.\n4. Applied `is-fullwidth` to the `select` and `button` elements to make them span the full width of the container.\n5. Added custom CSS to `style.css` for background color, padding, and margins to enhance the layout.","created_at":"2024-06-10T19:00:18.416246Z","output_tokens":null},{"deleted":0,"code_id":100007,"input_tokens":null,"id":50091,"project_id":1798,"sender":"human","message":"make generate start loading animation","created_at":"2024-06-10T19:02:17.726965Z","output_tokens":null},{"deleted":0,"code_id":100007,"input_tokens":null,"id":50092,"project_id":1798,"sender":"ai","message":"Let's add a loading animation that starts when the \"Generate\" button is clicked and stops when the generation process is complete. We'll use Bulma's built-in spinner for the loading animation.\n\n### index.html\nWe'll need to add a spinner element and make sure it is hidden initially.\n\n```html\n\n\n\n\n \n \n GenAI Playground\n \n \n\n\n\n

GenAI Playground

\n Select AI Model\n

\n \n

\n Enter your prompt\n

\n \n

\n \n\n\n\n```\n\n### Explanation\n1. Added a new `field` with a `button` that has the `is-loading` class for the loading animation.\n2. The `field` containing the loading spinner is initially hidden using the `is-hidden` class.\n\n### script.js\nWe'll update the event listener to show the loading spinner when the \"Generate\" button is clicked and hide it after a simulated generation process.\n\n```javascript\ndocument.getElementById('generateButton').addEventListener('click', function() {\n const model = document.getElementById('modelSelector').value;\n const prompt = document.getElementById('promptInput').value;\n\n // Show loading spinner\n document.getElementById('loadingSpinner').classList.remove('is-hidden');\n\n // Simulate the image generation process\n setTimeout(function() {\n // Placeholder for generating the image\n console.log(`Model: ${model}, Prompt: ${prompt}`);\n\n // Hide loading spinner\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n }, 3000); // Simulate a 3-second delay for the generation process\n});\n```\n\n### Explanation\n1. Added code to show the loading spinner by removing the `is-hidden` class when the \"Generate\" button is clicked.\n2. Simulated the image generation process with a 3-second delay using `setTimeout`.\n3. Hid the loading spinner by adding the `is-hidden` class after the simulated delay.","created_at":"2024-06-10T19:02:17.733774Z","output_tokens":null},{"deleted":0,"code_id":100010,"input_tokens":null,"id":50093,"project_id":1798,"sender":"human","message":"key: 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\ncurl https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0 \\\n\t-X POST \\\n\t-d '{\"inputs\": \"Astronaut riding a horse\"}' \\\n\t-H 'Content-Type: application/json' \\\n\t-H \"Authorization: Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\"\n","created_at":"2024-06-10T19:03:30.547918Z","output_tokens":null},{"deleted":0,"code_id":100010,"input_tokens":null,"id":50094,"project_id":1798,"sender":"ai","message":"Let's incorporate the API call to Hugging Face's inference endpoint into our script. We'll use the Fetch API to make the POST request and handle the response.\n\n### script.js\nWe'll update the event listener to make the actual API call instead of the simulated delay.\n\n```javascript\ndocument.getElementById('generateButton').addEventListener('click', function() {\n const model = document.getElementById('modelSelector').value;\n const prompt = document.getElementById('promptInput').value;\n const apiKey = \"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\"; // Your actual API key\n\n // Show loading spinner\n document.getElementById('loadingSpinner').classList.remove('is-hidden');\n\n // Define the API URL\n const apiUrl = \"https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0\";\n\n // Options for the fetch request\n const requestOptions = {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`\n },\n body: JSON.stringify({ inputs: prompt })\n };\n\n // Make the API call\n fetch(apiUrl, requestOptions)\n .then(response => response.json())\n .then(data => {\n // Handle the response data (for now just log it)\n console.log(data);\n\n // Hide loading spinner\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n })\n .catch(error => {\n console.error('Error:', error);\n\n // Hide loading spinner in case of error\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n });\n});\n```\n\n### Explanation\n1. Defined the `apiKey` variable with your actual API key.\n2. Constructed the `apiUrl` for the Hugging Face inference endpoint.\n3. Set up the `requestOptions` including method, headers, and body.\n4. Used `fetch` to make the POST request to the API.\n5. Handled the response by logging it to the console for now.\n6. Ensured the loading spinner is hidden both on success and error.\n\nThis will make a POST request to the Hugging Face API and handle the response accordingly. For now, the response is simply logged to the console. You can further process this data to display the generated image or any other relevant information.","created_at":"2024-06-10T19:03:30.556022Z","output_tokens":null},{"deleted":0,"code_id":100012,"input_tokens":null,"id":50095,"project_id":1798,"sender":"human","message":"ERROR Error:,SyntaxError: Unexpected token '�', \"��\u0010JFIF\"... is not valid JSON\ngives base64","created_at":"2024-06-10T19:03:57.686178Z","output_tokens":null},{"deleted":0,"code_id":100012,"input_tokens":null,"id":50096,"project_id":1798,"sender":"ai","message":"The error indicates that the response from the API is in a binary format (likely an image in base64) rather than JSON. We need to handle this by processing the response as a blob and then converting it to a base64 string to display the image.\n\nLet's update the `script.js` to handle the binary response correctly and display the generated image.\n\n### index.html\nWe need to add an `img` element to display the generated image.\n\n```html\n\n\n\n\n \n \n GenAI Playground\n \n \n\n\n\n

GenAI Playground

\n Select AI Model\n

\n \n

\n Enter your prompt\n

\n \n

\n $\"Generated$ \n

\n \n\n\n\n```\n\n### Explanation\n1. Added a new `field` with an `img` element to display the generated image.\n2. Initially, the `imageContainer` is hidden using `style=\"display: none;\"`.\n\n### script.js\nUpdate the script to handle the binary response and display the image.\n\n```javascript\ndocument.getElementById('generateButton').addEventListener('click', function() {\n const model = document.getElementById('modelSelector').value;\n const prompt = document.getElementById('promptInput').value;\n const apiKey = \"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\"; // Your actual API key\n\n // Show loading spinner\n document.getElementById('loadingSpinner').classList.remove('is-hidden');\n document.getElementById('imageContainer').style.display = 'none';\n\n // Define the API URL\n const apiUrl = \"https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0\";\n\n // Options for the fetch request\n const requestOptions = {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`\n },\n body: JSON.stringify({ inputs: prompt })\n };\n\n // Make the API call\n fetch(apiUrl, requestOptions)\n .then(response => response.blob())\n .then(blob => {\n const reader = new FileReader();\n reader.onloadend = () => {\n const base64data = reader.result;\n document.getElementById('generatedImage').src = base64data;\n document.getElementById('imageContainer').style.display = 'block';\n };\n reader.readAsDataURL(blob);\n\n // Hide loading spinner\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n })\n .catch(error => {\n console.error('Error:', error);\n\n // Hide loading spinner in case of error\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n });\n});\n```\n\n### Explanation\n1. Modified the fetch call to handle the response as a `blob`.\n2. Used a `FileReader` to convert the `blob` to a base64 string.\n3. Set the `src` attribute of the `img` element to the base64 string.\n4. Made the `imageContainer` visible once the image is loaded.\n5. Ensured the loading spinner is hidden both on success and error.","created_at":"2024-06-10T19:03:57.693854Z","output_tokens":null},{"deleted":0,"code_id":100014,"input_tokens":null,"id":50097,"project_id":1798,"sender":"human","message":"make model repersent api url\nadd dropdowns dynamiclly via js","created_at":"2024-06-10T19:05:07.350183Z","output_tokens":null},{"deleted":0,"code_id":100014,"input_tokens":null,"id":50098,"project_id":1798,"sender":"ai","message":"We'll update the code to make the model selection dynamic and use the selected model to construct the API URL. This approach will allow you to easily add more models in the future.\n\n### index.html\nRemove the model option hardcoding since it will now be added dynamically via JavaScript.\n\n```html\n\n\n\n\n \n \n GenAI Playground\n \n \n\n\n\n

GenAI Playground

\n Select AI Model\n

\n \n

\n Enter your prompt\n

\n \n

\n $\"Generated$ \n

\n \n\n\n\n```\n\n### script.js\nWe'll add the model options dynamically and use the selected model to construct the API URL.\n\n```javascript\ndocument.addEventListener('DOMContentLoaded', function() {\n const models = {\n 'SDXL': 'stabilityai/stable-diffusion-xl-base-1.0',\n // Add more models here as needed\n };\n\n const modelSelector = document.getElementById('modelSelector');\n\n // Populate the model dropdown\n Object.keys(models).forEach(modelName => {\n const option = document.createElement('option');\n option.value = models[modelName];\n option.text = modelName;\n modelSelector.appendChild(option);\n });\n});\n\ndocument.getElementById('generateButton').addEventListener('click', function() {\n const model = document.getElementById('modelSelector').value;\n const prompt = document.getElementById('promptInput').value;\n const apiKey = \"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\"; // Your actual API key\n\n // Show loading spinner\n document.getElementById('loadingSpinner').classList.remove('is-hidden');\n document.getElementById('imageContainer').style.display = 'none';\n\n // Define the API URL\n const apiUrl = `https://api-inference.huggingface.co/models/${model}`;\n\n // Options for the fetch request\n const requestOptions = {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`\n },\n body: JSON.stringify({ inputs: prompt })\n };\n\n // Make the API call\n fetch(apiUrl, requestOptions)\n .then(response => response.blob())\n .then(blob => {\n const reader = new FileReader();\n reader.onloadend = () => {\n const base64data = reader.result;\n document.getElementById('generatedImage').src = base64data;\n document.getElementById('imageContainer').style.display = 'block';\n };\n reader.readAsDataURL(blob);\n\n // Hide loading spinner\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n })\n .catch(error => {\n console.error('Error:', error);\n\n // Hide loading spinner in case of error\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n });\n});\n```\n\n### Explanation\n1. Added an event listener for `DOMContentLoaded` to populate the model dropdown dynamically.\n2. Defined a `models` object to map model names to their respective API endpoints.\n3. Populated the `modelSelector` with options based on the `models` object.\n4. Updated the API URL construction to use the selected model's endpoint.\n","created_at":"2024-06-10T19:05:07.358146Z","output_tokens":null},{"deleted":0,"code_id":100021,"input_tokens":null,"id":50099,"project_id":1798,"sender":"human","message":"if it does {\"error\":\"Model stabilityai/stable-diffusion-xl-base-0.9 is currently loading\",\"estimated_time\":1387.5771484375} keep regenerating until it suceeds","created_at":"2024-06-10T19:09:12.909113Z","output_tokens":null},{"deleted":0,"code_id":100021,"input_tokens":null,"id":50100,"project_id":1798,"sender":"ai","message":"To handle the scenario where the model is currently loading, we can implement a retry mechanism. If the response contains the `error` field indicating the model is loading, we'll wait for a short duration and then retry the API call.\n\n### script.js\nUpdate the script to include a retry mechanism.\n\n```javascript\ndocument.addEventListener('DOMContentLoaded', function() {\n const models = {\n 'SDXL 1.0': 'stabilityai/stable-diffusion-xl-base-1.0',\n 'SDXL 0.9': 'stabilityai/stable-diffusion-xl-base-0.9',\n 'SD1.5': 'runwayml/stable-diffusion-v1-5'\n // Add more models here as needed\n };\n\n const modelSelector = document.getElementById('modelSelector');\n\n // Populate the model dropdown\n Object.keys(models).forEach(modelName => {\n const option = document.createElement('option');\n option.value = models[modelName];\n option.text = modelName;\n modelSelector.appendChild(option);\n });\n});\n\ndocument.getElementById('generateButton').addEventListener('click', function() {\n const model = document.getElementById('modelSelector').value;\n const prompt = document.getElementById('promptInput').value;\n const apiKey = \"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\"; // Your actual API key\n const apiUrl = `https://api-inference.huggingface.co/models/${model}`;\n\n // Show loading spinner\n document.getElementById('loadingSpinner').classList.remove('is-hidden');\n document.getElementById('imageContainer').style.display = 'none';\n\n // Options for the fetch request\n const requestOptions = {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`\n },\n body: JSON.stringify({ inputs: prompt })\n };\n\n // Function to make the API call with retry logic\n function generateImage() {\n fetch(apiUrl, requestOptions)\n .then(response => response.json())\n .then(data => {\n if (data.error) {\n console.error('Error:', data.error);\n if (data.error.includes('currently loading')) {\n // Retry after a short delay if the model is loading\n setTimeout(generateImage, 5000); // Retry after 5 seconds\n } else {\n // Hide loading spinner in case of other errors\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n }\n } else {\n // Process the response as a blob\n return fetch(apiUrl, requestOptions)\n .then(response => response.blob())\n .then(blob => {\n const reader = new FileReader();\n reader.onloadend = () => {\n const base64data = reader.result;\n document.getElementById('generatedImage').src = base64data;\n document.getElementById('imageContainer').style.display = 'block';\n };\n reader.readAsDataURL(blob);\n\n // Hide loading spinner\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n });\n }\n })\n .catch(error => {\n console.error('Error:', error);\n\n // Hide loading spinner in case of error\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n });\n }\n\n // Start the image generation process\n generateImage();\n});\n```\n\n### Explanation\n1. Added a new function `generateImage` to encapsulate the API call and retry logic.\n2. If the response contains an error indicating the model is loading, the function retries the API call after a 5-second delay.\n3. If the response is successful or contains a different error, it processes the response accordingly or logs the error.\n4. Called `generateImage` initially when the \"Generate\" button is clicked to start the process.","created_at":"2024-06-10T19:09:12.915487Z","output_tokens":null},{"deleted":0,"code_id":100025,"input_tokens":null,"id":50101,"project_id":1798,"sender":"human","message":"ERROR Error:,SyntaxError: Unexpected token '�', \"��\u0010JFIF\"... is not valid JSON\n","created_at":"2024-06-10T19:10:11.996009Z","output_tokens":null},{"deleted":0,"code_id":100025,"input_tokens":null,"id":50102,"project_id":1798,"sender":"ai","message":"The error indicates that the response from the API is a binary format (likely an image in base64) rather than JSON. To handle this correctly, we need to process the response as a blob and then convert it to a base64 string to display the image. Additionally, we'll need to handle the retry mechanism properly by checking the error message and retrying the request if the model is still loading.\n\nHere's the corrected code:\n\n### script.js\n```javascript\ndocument.addEventListener('DOMContentLoaded', function() {\n const models = {\n 'SDXL 1.0': 'stabilityai/stable-diffusion-xl-base-1.0',\n 'SDXL 0.9': 'stabilityai/stable-diffusion-xl-base-0.9',\n 'SD1.5': 'runwayml/stable-diffusion-v1-5'\n // Add more models here as needed\n };\n\n const modelSelector = document.getElementById('modelSelector');\n\n // Populate the model dropdown\n Object.keys(models).forEach(modelName => {\n const option = document.createElement('option');\n option.value = models[modelName];\n option.text = modelName;\n modelSelector.appendChild(option);\n });\n});\n\ndocument.getElementById('generateButton').addEventListener('click', function() {\n const model = document.getElementById('modelSelector').value;\n const prompt = document.getElementById('promptInput').value;\n const apiKey = \"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\"; // Your actual API key\n const apiUrl = `https://api-inference.huggingface.co/models/${model}`;\n\n // Show loading spinner\n document.getElementById('loadingSpinner').classList.remove('is-hidden');\n document.getElementById('imageContainer').style.display = 'none';\n\n // Options for the fetch request\n const requestOptions = {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`\n },\n body: JSON.stringify({ inputs: prompt })\n };\n\n // Function to make the API call with retry logic\n function generateImage() {\n fetch(apiUrl, requestOptions)\n .then(response => {\n if (!response.ok) {\n return response.json().then(err => { throw err; });\n }\n return response.blob();\n })\n .then(blob => {\n const reader = new FileReader();\n reader.onloadend = () => {\n const base64data = reader.result;\n document.getElementById('generatedImage').src = base64data;\n document.getElementById('imageContainer').style.display = 'block';\n };\n reader.readAsDataURL(blob);\n\n // Hide loading spinner\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n })\n .catch(error => {\n console.error('Error:', error);\n if (error.error && error.error.includes('currently loading')) {\n // Retry after a short delay if the model is loading\n setTimeout(generateImage, 5000); // Retry after 5 seconds\n } else {\n // Hide loading spinner in case of other errors\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n }\n });\n }\n\n // Start the image generation process\n generateImage();\n});\n```\n\n### Explanation\n1. Updated the `fetch` call to handle both successful and unsuccessful responses correctly by checking `response.ok`.\n2. Processed the successful response as a `blob` and used `FileReader` to convert it to a base64 string.\n3. Implemented the retry mechanism to handle the \"model loading\" error by checking the error message and retrying after 5 seconds.\n4. Ensured the loading spinner is hidden both on success and error.","created_at":"2024-06-10T19:10:12.003430Z","output_tokens":null},{"deleted":0,"code_id":100027,"input_tokens":null,"id":50103,"project_id":1798,"sender":"human","message":"ERROR Error:,[object Object]\n","created_at":"2024-06-10T19:11:00.519167Z","output_tokens":null},{"deleted":0,"code_id":100027,"input_tokens":null,"id":50104,"project_id":1798,"sender":"ai","message":"The issue arises from the fact that the error object is being logged directly, which is not helpful for debugging. We need to properly extract and log the error messages.\n\nAdditionally, we need to ensure that the retry mechanism properly handles the binary response when the model is not loading. Let's update the retry mechanism to handle this and improve error logging.\n\n### script.js\n```javascript\ndocument.addEventListener('DOMContentLoaded', function() {\n const models = {\n 'SDXL 1.0': 'stabilityai/stable-diffusion-xl-base-1.0',\n 'SDXL 0.9': 'stabilityai/stable-diffusion-xl-base-0.9',\n 'SD1.5': 'runwayml/stable-diffusion-v1-5'\n // Add more models here as needed\n };\n\n const modelSelector = document.getElementById('modelSelector');\n\n // Populate the model dropdown\n Object.keys(models).forEach(modelName => {\n const option = document.createElement('option');\n option.value = models[modelName];\n option.text = modelName;\n modelSelector.appendChild(option);\n });\n});\n\ndocument.getElementById('generateButton').addEventListener('click', function() {\n const model = document.getElementById('modelSelector').value;\n const prompt = document.getElementById('promptInput').value;\n const apiKey = \"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\"; // Your actual API key\n const apiUrl = `https://api-inference.huggingface.co/models/${model}`;\n\n // Show loading spinner\n document.getElementById('loadingSpinner').classList.remove('is-hidden');\n document.getElementById('imageContainer').style.display = 'none';\n\n // Options for the fetch request\n const requestOptions = {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`\n },\n body: JSON.stringify({ inputs: prompt })\n };\n\n // Function to make the API call with retry logic\n function generateImage() {\n fetch(apiUrl, requestOptions)\n .then(response => {\n if (!response.ok) {\n return response.json().then(err => { throw err; });\n }\n return response.blob();\n })\n .then(blob => {\n const reader = new FileReader();\n reader.onloadend = () => {\n const base64data = reader.result;\n document.getElementById('generatedImage').src = base64data;\n document.getElementById('imageContainer').style.display = 'block';\n };\n reader.readAsDataURL(blob);\n\n // Hide loading spinner\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n })\n .catch(error => {\n console.error('Error:', error);\n if (error.error && error.error.includes('currently loading')) {\n // Retry after a short delay if the model is loading\n console.log(`Retrying in ${Math.ceil(error.estimated_time / 1000)} seconds...`);\n setTimeout(generateImage, error.estimated_time);\n } else {\n // Hide loading spinner in case of other errors\n document.getElementById('loadingSpinner').classList.add('is-hidden');\n }\n });\n }\n\n // Start the image generation process\n generateImage();\n});\n```\n\n### Explanation\n1. Updated error handling to properly log and check for the `error` message.\n2. Used `Math.ceil(error.estimated_time / 1000)` to log the retry delay in seconds.\n3. Added a retry mechanism with `setTimeout(generateImage, error.estimated_time)` to retry after the estimated loading time.\n\nThis should handle the retry logic correctly and provide better error logging for easier debugging.","created_at":"2024-06-10T19:11:00.525673Z","output_tokens":null},{"deleted":0,"code_id":100030,"input_tokens":null,"id":50105,"project_id":1798,"sender":"human","message":"modify this to be an ai automcomplete text input with no js","created_at":"2024-06-10T19:12:19.869161Z","output_tokens":null},{"deleted":0,"code_id":100030,"input_tokens":null,"id":50106,"project_id":1798,"sender":"ai","message":"To create an AI autocomplete text input using HTML and Bulma (without JavaScript), we can use a simple static approach. However, keep in mind that without JavaScript, we won't be able to dynamically fetch suggestions from an API. Instead, we can use the HTML `datalist` element to provide a list of predefined suggestions.\n\nHere's how you can modify your existing code to include an autocomplete text input using `datalist`:\n\n### index.html\n```html\n\n\n\n\n \n \n GenAI Playground\n \n \n\n\n\n

GenAI Playground

\n Select AI Model\n

\n \n

\n Enter your prompt\n

\n \n \n \n

\n \n

\n $\"Generated$ \n

\n \n\n\n\n```\n\n### Explanation\n1. **Model Selector**: The model selector is kept as a dropdown (`\n \n