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\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(\"Failed to generate image\");\n const data = await response.json();\n setLatency(data.latency);\n setImageUrl(data.imageUrl);\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

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 &&

Generation time: {latency.toFixed(2)} ms

}\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 = `ratelimit_${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 later.\" }), {\n status: 429,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n const { prompt } = await request.json();\n try {\n const startTime = performance.now();\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 const endTime = performance.now();\n return new Response(JSON.stringify({ imageUrl: result.images[0].url, latency: endTime - startTime }), {\n headers: { \"Content-Type\": \"application/json\" },\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

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