\n );\n}\n\nfunction App() {\n const [prompt, setPrompt] = useState(DEFAULT_PROMPT);\n const [imageUrl, setImageUrl] = useState(PLACEHOLDER_IMAGE);\n const [latency, setLatency] = useState(null);\n const [loading, setLoading] = useState(false);\n const [error, setError] = useState(\"\");\n\n const generateImage = async () => {\n setLoading(true);\n setError(\"\");\n try {\n const response = await fetch(\"/generate\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ prompt }),\n });\n const data = await response.json();\n if (!response.ok) throw new Error(data.error || \"Failed to generate image\");\n setLatency(data.latency);\n setImageUrl(data.imageUrl);\n setError(\"\"); // Clear any previous errors\n } catch (err) {\n setError(err.message);\n console.error(err);\n } finally {\n setLoading(false);\n }\n };\n\n return (\n <>\n \n

AI Image Generator

\n setPrompt(e.target.value)}\n placeholder={DEFAULT_PROMPT}\n className=\"prompt-input\"\n />\n

\n \n {error &&

{error}

}\n

\n {latency !== null && (\n

Inference time (doesn't include network): {latency.toFixed(2)} ms

\n )}\n {loading\n ? \n : $\"Generated$ }\n

\n \n View Source\n \n

\n Powered by\n \n \n \n

\n \n );\n}\n\nfunction client() {\n createRoot(document.getElementById(\"root\")).render();\n}\n\nif (typeof document !== \"undefined\") {\n client();\n}\n\nasync function checkRateLimit(clientIP: string): Promise {\n const now = Date.now();\n const key = `xratelimit_${clientIP}`;\n\n let clientData = await blob.getJSON(key);\n if (!clientData) {\n clientData = { count: 1, firstRequest: now };\n } else {\n if (now - clientData.firstRequest > RATE_LIMIT_WINDOW) {\n // Reset if the window has passed\n clientData.count = 1;\n clientData.firstRequest = now;\n } else {\n clientData.count++;\n }\n }\n console.log(clientData);\n await blob.setJSON(key, clientData);\n\n return clientData.count <= RATE_LIMIT;\n}\n\nasync function server(request: Request): Promise {\n if (request.method === \"POST\" && new URL(request.url).pathname === \"/generate\") {\n const clientIP = (request.headers.get(\"x-forwarded-for\") || \"unknown\").split(\",\")[0].trim();\n const isAllowed = await checkRateLimit(clientIP);\n if (!isAllowed) {\n return new Response(JSON.stringify({ error: \"Rate limit exceeded. Please try again in a couple of minutes.\" }), {\n status: 429,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n const { prompt } = await request.json();\n try {\n const result = await fal.run(\"fal-ai/flux/schnell\", {\n input: {\n prompt,\n image_size: \"landscape_4_3\",\n num_inference_steps: 4,\n num_images: 1,\n enable_safety_checker: true,\n sync_mode: true,\n },\n });\n return new Response(\n JSON.stringify({ imageUrl: result.images[0].url, latency: result.timings.inference * 1000 }),\n {\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n } catch (error) {\n return new Response(JSON.stringify({ error: \"Failed to generate image\" }), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n\n return new Response(\n `\n \n \n \n \n \n AI Image Generator\n \n \n \n

\n \n \n \n `,\n {\n headers: { \"Content-Type\": \"text/html\" },\n },\n );\n}\n\nconst css = `\n body {\n font-family: Arial, sans-serif;\n line-height: 1.6;\n margin: 0;\n padding: 20px;\n background-color: #1a1a1a;\n color: #fff;\n overflow-x: hidden;\n min-height: 100vh;\n }\n .background {\n position: fixed;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n z-index: -1;\n opacity: 0.2;\n pointer-events: none;\n }\n .container {\n max-width: 800px;\n margin: 0 auto;\n background-color: rgba(255, 255, 255, 0.15);\n padding: 30px;\n border-radius: 5px;\n backdrop-filter: blur(5px);\n }\n .title {\n text-align: center;\n color: #fff;\n font-size: 2.5em;\n margin-bottom: 30px;\n }\n .input-container {\n position: relative;\n margin-bottom: 20px;\n }\n .prompt-input {\n width: 100%;\n padding: 15px;\n border: 2px solid #FFFF00;\n border-radius: 25px;\n background-color: rgba(255, 255, 255, 0.2);\n color: #fff;\n font-size: 1em;\n transition: all 0.3s ease;\n }\n .prompt-input:focus {\n outline: none;\n background-color: rgba(255, 255, 255, 0.3);\n }\n .generate-btn {\n display: block;\n width: 100%;\n padding: 15px;\n background-color: #FFFF00;\n color: #000;\n border: none;\n border-radius: 25px;\n cursor: pointer;\n transition: all 0.3s ease;\n font-size: 1em;\n text-transform: uppercase;\n letter-spacing: 1px;\n font-weight: bold;\n }\n .generate-btn:hover {\n background-color: #FFFF99;\n }\n .generate-btn:disabled {\n background-color: #666666;\n cursor: not-allowed;\n }\n .error {\n color: #FF6666;\n text-align: center;\n }\n .image-container {\n margin-top: 20px;\n text-align: center;\n background-color: rgba(0, 0, 0, 0.2);\n padding: 20px;\n border-radius: 10px;\n }\n .generated-image {\n max-width: 100%;\n height: auto;\n }\n .latency {\n font-size: 14px;\n color: #fff;\n margin-bottom: 10px;\n }\n .footer {\n display: flex;\n justify-content: space-between;\n align-items: center;\n margin-top: 20px;\n font-size: 0.9em;\n }\n .source-link a {\n color: #fff;\n text-decoration: none;\n transition: all 0.3s ease;\n }\n .source-link a:hover {\n text-decoration: underline;\n }\n .powered-by {\n display: flex;\n align-items: center;\n gap: 10px;\n color: #ccc;\n }\n .fal-logo {\n height: 30px;\n transition: transform 0.3s ease;\n }\n .fal-logo:hover {\n transform: scale(1.1);\n }\n .loading-spinner {\n display: flex;\n justify-content: center;\n align-items: center;\n height: 200px;\n }\n .spinner {\n border: 4px solid rgba(255, 255, 255, 0.3);\n border-radius: 50%;\n border-top: 4px solid #FFFF00;\n width: 40px;\n height: 40px;\n animation: spin 1s linear infinite;\n }\n @keyframes spin {\n 0% { transform: rotate(0deg); }\n 100% { transform: rotate(360deg); }\n }\n @media (max-width: 600px) {\n .container {\n padding: 15px;\n }\n .title {\n font-size: 2em;\n }\n .prompt-input,\n .generate-btn {\n padding: 12px;\n font-size: 0.9em;\n }\n .footer {\n flex-direction: column;\n gap: 10px;\n }\n .powered-by {\n order: -1;\n }\n .fal-logo {\n height: 25px;\n }\n }\n`;\n\nexport default server;","createdAt",["D",1724814978065],"version",54,"currentUserHasLiked","likeCount","forkCount",1,"referenceCount","dependencyCount","commentCount","prCount","mergedPr",{"_131":132,"_133":134},"handle","isidentical","profileImageUrl","https://img.clerk.com/eyJ0eXBlIjoicHJveHkiLCJzcmMiOiJodHRwczovL2F2YXRhcnMuZ2l0aHVidXNlcmNvbnRlbnQuY29tL3UvNDczNTg5MTM/dj00IiwicyI6ImtBaTRva25BQXh2QXNWK3g0WjRTU3A4dmZzUDNOWnBEakN5bWkzNGpoUTAifQ","forkedVal","interval","slug","isidentical/fluxImageGenerator","description","Sign in to fal.ai, generate an API key, and set it as FAL_KEY for your 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This app uses the fal.ai API to generate images based on user prompts.\n// It features a clean UI with an input field for the prompt and a button to generate the image.\n// The generated image is displayed below the input field.\n// React is used for the UI and the fal.ai serverless client for image generation.\n// The app measures and displays the latency for each image generation.\n// The background features randomly placed pixelart lightning bolts in neon yellow.\n\n/** @jsxImportSource https://esm.sh/react */\nimport * as fal from \"https://esm.sh/@fal-ai/serverless-client\";\nimport React, { useEffect, useState } from \"https://esm.sh/react\";\nimport { createRoot } from \"https://esm.sh/react-dom/client\";\nimport { blob } from \"https://esm.town/v/std/blob\";\n\n// Constants for background\nconst GRID_SIZE = 100; // Size of each grid cell\nconst BOLT_CHANCE = 0.2; // 20% chance to place a bolt in each cell\nconst BOLT_SIZE = 40; // Width and height of each bolt\nconst DEFAULT_PROMPT = \"green grass landscape with text \\\"val.town\\\" is engraved, a townhouse in the background\";\nconst PLACEHOLDER_IMAGE = \"https://fal.media/files/penguin/PysYf1-_ddhM7JKiLrkcF.png\";\nconst RATE_LIMIT = 15; // requests per hour\nconst RATE_LIMIT_WINDOW = 5 * 60 * 1000; // 5 minutes in milliseconds\n\ninterface RateLimitData {\n count: number;\n firstRequest: number;\n}\n\nfunction LightningBolt({ x, y }) {\n return (\n \n );\n}\n\nfunction Background() {\n const [bolts, setBolts] = useState([]);\n\n useEffect(() => {\n const newBolts = [];\n const numColumns = Math.ceil(window.innerWidth / GRID_SIZE);\n const numRows = Math.ceil(window.innerHeight / GRID_SIZE);\n\n for (let i = 0; i < numColumns; i++) {\n for (let j = 0; j < numRows; j++) {\n if (Math.random() < BOLT_CHANCE) {\n newBolts.push({\n x: i * GRID_SIZE + Math.random() * (GRID_SIZE - BOLT_SIZE),\n y: j * GRID_SIZE + Math.random() * (GRID_SIZE - BOLT_SIZE),\n });\n }\n }\n }\n setBolts(newBolts);\n }, []);\n\n return (\n \n );\n}\n\nfunction LoadingSpinner() {\n return (\n

\n );\n}\n\nfunction App() {\n const [prompt, setPrompt] = useState(DEFAULT_PROMPT);\n const [imageUrl, setImageUrl] = useState(PLACEHOLDER_IMAGE);\n const [latency, setLatency] = useState(null);\n const [loading, setLoading] = useState(false);\n const [error, setError] = useState(\"\");\n\n const generateImage = async () => {\n setLoading(true);\n setError(\"\");\n try {\n const response = await fetch(\"/generate\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ prompt }),\n });\n if (!response.ok) throw new Error(data.error || \"Failed to generate image\");\n const data = await response.json();\n setLatency(data.latency);\n setImageUrl(data.imageUrl);\n setError(\"\"); // Clear any previous errors\n } catch (err) {\n setError(\"Failed to generate image. Please try again.\");\n console.error(err);\n } finally {\n setLoading(false);\n }\n };\n\n return (\n <>\n \n

fluxImageGenerator

Version 50