Scribe • Audio Recorder

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{ Hono } from \"https://deno.land/x/hono@v3.11.7/mod.ts\";\n\nimport { useRecorderExport } from \"https://esm.town/v/yawnxyz/scribeUseRecorderExport\";\n\n// Inlined content from ./middleware.ts\nimport \"jsr:@std/dotenv/load\"\nimport { Groq } from \"npm:groq-sdk\";\nconst groq = new Groq();\nimport { HfInference } from \"npm:@huggingface/inference\";\nconst hf = new HfInference(Deno.env.get(\"HUGGINGFACE_API_KEY\"));\n\nconst MAX_CHUNK_SIZE_BYTES = 25 * 1024 * 1024; // 25 MB in bytes\nconst DEFAULT_WHISPER_MODEL = 'whisper-large-v3-turbo';\nconst MAX_TRANSCRIPT_CONTEXT = 3000; // Characters to keep from transcript history\n\ntype WhisperModel = 'whisper-large-v3-turbo' | 'distil-whisper-large-v3-en' | 'whisper-large-v3';\ntype WhisperResponseFormat = 'json' | 'verbose_json' | 'text';\n\ninterface TranscriptionParams {\n model?: WhisperModel;\n prompt?: string;\n response_format?: WhisperResponseFormat;\n language?: string;\n temperature?: number;\n}\n\ninterface Transcription {\n text: string;\n segments?: Array<{\n start: number;\n end: number;\n text: string;\n }>;\n duration?: number;\n}\n\n// Helper function to normalize transcription response\nfunction normalizeTranscription(response: string | Transcription): Transcription {\n console.log('Normalized transcription:', response);\n if (typeof response === 'string') {\n return { text: response };\n }\n return response;\n}\n\n// Fetch the size of the file using HTTP HEAD request\nexport async function fetchFileSize(url: string): Promise {\n const response = await fetch(url, { method: 'HEAD' });\n const contentLength = response.headers.get('content-length');\n return contentLength ? parseInt(contentLength, 10) : 0;\n}\n\n// Transcribe a single chunk of audio data using Deno's ReadableStream\nasync function transcribeChunk(buffer: Uint8Array, params: TranscriptionParams): Promise {\n console.log('Transcribing chunk with params:', {\n model: params.model,\n language: params.language,\n response_format: params.response_format\n });\n \n const blob = new Blob([buffer], { type: 'audio/wav' });\n const file = new File([blob], 'audio.wav', { type: 'audio/wav' });\n\n const transcription = await groq.audio.transcriptions.create({\n file,\n model: params?.model || DEFAULT_WHISPER_MODEL,\n prompt: params?.prompt,\n response_format: params?.response_format || 'text',\n language: params?.language || 'en',\n temperature: 0 // Set to 0 for consistency\n });\n\n return normalizeTranscription(transcription);\n}\n\n// Add this interface\ninterface AudioInput {\n type: 'url' | 'file';\n content: string | File;\n}\n\nexport async function transcribeAudio(input: AudioInput, params: TranscriptionParams): Promise {\n console.log('Transcribing audio with params:', params);\n \n let fileSize: number;\n let getBuffer: (start: number, end: number) => Promise;\n\n if (input.type === 'file') {\n const file = input.content as File;\n fileSize = file.size;\n \n // For files, create buffer getter that slices the file\n getBuffer = async (start: number, end: number) => {\n const slice = file.slice(start, end + 1);\n const arrayBuffer = await slice.arrayBuffer();\n return new Uint8Array(arrayBuffer);\n };\n } else {\n const url = input.content as string;\n fileSize = await fetchFileSize(url);\n \n // For URLs, create buffer getter that fetches ranges\n getBuffer = async (start: number, end: number) => {\n const response = await fetch(url, {\n headers: { Range: `bytes=${start}-${end}` },\n });\n if (!response.ok && response.status !== 206) {\n throw new Error(`Failed to fetch range ${start}-${end}: ${response.statusText}`);\n }\n const arrayBuffer = await response.arrayBuffer();\n return new Uint8Array(arrayBuffer);\n };\n }\n\n if (fileSize === 0) {\n throw new Error('Unable to determine the file size.');\n }\n\n console.log('Check Upload File size:', fileSize, MAX_CHUNK_SIZE_BYTES);\n\n if (fileSize <= MAX_CHUNK_SIZE_BYTES) {\n // Handle small files directly\n if (input.type === 'file') {\n const file = input.content as File;\n const transcription = await groq.audio.transcriptions.create({\n file,\n model: params?.model || DEFAULT_WHISPER_MODEL,\n prompt: params?.prompt,\n response_format: params?.response_format || 'verbose_json',\n language: params?.language || 'en',\n temperature: params?.temperature,\n });\n return normalizeTranscription(transcription);\n } else {\n const url = input.content as string;\n const transcription = await groq.audio.transcriptions.create({\n url,\n model: params?.model || DEFAULT_WHISPER_MODEL,\n prompt: params?.prompt,\n response_format: params?.response_format || 'text',\n language: params?.language || 'en',\n temperature: params?.temperature,\n });\n return normalizeTranscription(transcription);\n }\n }\n\n // Handle large files by splitting into equal-sized chunks\n const numChunks = Math.ceil(fileSize / MAX_CHUNK_SIZE_BYTES);\n const chunkSize = Math.ceil(fileSize / numChunks);\n const transcriptions: (string | Transcription)[] = [];\n\n\n console.log('Handle large files by splitting into equal-sized chunks', numChunks);\n\n for (let i = 0; i < numChunks; i++) {\n const start = i * chunkSize;\n const end = Math.min((i + 1) * chunkSize - 1, fileSize - 1);\n\n console.log(`Processing chunk ${i + 1}/${numChunks} (bytes ${start}-${end})`);\n const buffer = await getBuffer(start, end);\n\n try {\n const transcription = await transcribeChunk(buffer, {\n ...params,\n model: params.model || DEFAULT_WHISPER_MODEL,\n language: params.language || 'en'\n });\n transcriptions.push(transcription);\n } catch (error: any) {\n console.error(`Error transcribing chunk ${i + 1}:`, error);\n throw new Error(`Transcription failed for chunk ${i + 1}: ${error.message}`);\n }\n }\n\n // Handle joining based on response format\n if (params.response_format === 'text' || !params.response_format) {\n const combinedText = transcriptions.map(t => {\n const normalized = normalizeTranscription(t);\n return normalized.text;\n }).join(' ');\n return { text: combinedText };\n } else {\n // For JSON responses, combine segments and adjust timestamps\n const firstChunk = normalizeTranscription(transcriptions[0]);\n const combinedResponse: Transcription = {\n text: firstChunk.text,\n segments: firstChunk.segments || [],\n duration: firstChunk.duration || 0\n };\n\n let timeOffset = combinedResponse.duration || 0;\n\n for (let i = 1; i < transcriptions.length; i++) {\n const currentChunk = normalizeTranscription(transcriptions[i]);\n\n if (currentChunk.segments && combinedResponse.segments) {\n const adjustedSegments = currentChunk.segments.map(segment => ({\n ...segment,\n start: segment.start + timeOffset,\n end: segment.end + timeOffset\n }));\n combinedResponse.segments.push(...adjustedSegments);\n }\n\n combinedResponse.duration = (combinedResponse.duration || 0) + (currentChunk.duration || 0);\n combinedResponse.text += ' ' + currentChunk.text;\n\n timeOffset += currentChunk.duration || 0;\n }\n\n return combinedResponse;\n }\n}\n\nasync function getTranscript(data) {\n try {\n console.log(\"Starting transcription...\");\n\n let input;\n let fileToTranscribe;\n \n // Extract parameters from FormData\n const language = data.get('language') || 'en';\n const model = data.get('model') || DEFAULT_WHISPER_MODEL;\n \n console.log(\"Transcript params:\", { language, model });\n\n // Check if formData is a JSON object with a URL\n if (data.url) {\n console.log(\"Processing URL input:\", data.url);\n input = {\n type: 'url' as const,\n content: data.url\n };\n } else {\n // Handle file upload\n const file = data.get('file');\n if (!file) {\n throw new Error('No file found in form data');\n }\n\n console.log(\"Input file:\", {\n name: file.name,\n size: file.size,\n type: file.type\n });\n\n fileToTranscribe = file instanceof File ? file :\n new File([file], 'audio.wav', { type: file.type || 'audio/wav' });\n\n input = {\n type: 'file' as const,\n content: fileToTranscribe\n };\n }\n\n const params: TranscriptionParams = {\n model,\n response_format: 'text',\n language,\n temperature: 0\n };\n\n console.log(\"Using transcription params:\", params);\n\n const transcription = await transcribeAudio(input, params);\n const transcriptText = typeof transcription === 'string' ?\n transcription : transcription.text;\n\n console.log(\"Transcription completed:\", transcriptText);\n return transcriptText.trim() || null;\n\n } catch (error) {\n console.error(\"Transcription failed:\", error);\n return null;\n }\n}\n\n\n// Keep existing transcriptHandler for SSE responses during recording\nexport const transcriptHandler = async (c) => {\n c.header('Content-Type', 'text/event-stream');\n\n console.log('Starting transcript handler');\n\n return new Response(\n new ReadableStream({\n async start(controller) {\n const encoder = new TextEncoder();\n try {\n const formData = await c.req.formData();\n const transcript = await getTranscript(formData);\n \n if (!transcript) {\n throw new Error('Invalid audio or transcription failed');\n }\n\n const messageWithTimestamp = {\n timestamp: new Date().toLocaleString(),\n content: transcript\n };\n\n controller.enqueue(encoder.encode(`data: ${JSON.stringify({ \n type: 'transcript', \n message: messageWithTimestamp \n })}\\n\\n`));\n\n } catch (error) {\n controller.enqueue(encoder.encode(`data: ${JSON.stringify({ \n type: 'error', \n content: error.message, \n timestamp: new Date().toLocaleString() \n })}\\n\\n`));\n }\n controller.close();\n }\n })\n );\n};\n\n// Add new transcribeHandler for file/URL uploads\nexport const uploadTranscribeHandler = async (c) => {\n try {\n let file: File | null = null;\n let url: string | null = null;\n let params: TranscriptionParams = {\n model: DEFAULT_WHISPER_MODEL,\n response_format: 'text',\n language: 'en'\n };\n\n \n const contentType = c.req.header('content-type');\n console.log('starting uploadTranscribeHandler', contentType);\n \n if (contentType?.includes('multipart/form-data')) {\n try {\n console.log('[multipart/form-data] Received multipart/form-data');\n const formData = await c.req.formData();\n url = formData.get('url') as string | null;\n console.log('url', url);\n file = formData.get('file') as File | null;\n console.log('file', file);\n \n console.log('[multipart/form-data] Received language:', formData.get('language'));\n \n params = {\n model: (formData.get('model') as WhisperModel) || DEFAULT_WHISPER_MODEL,\n response_format: (formData.get('response_format') as WhisperResponseFormat) || 'text',\n language: formData.get('language')?.toString() || 'en',\n prompt: formData.get('prompt')?.toString(),\n temperature: formData.get('temperature') ? parseFloat(formData.get('temperature').toString()) : undefined,\n };\n } catch(error) {\n console.error('Failed to parse multipart/form-data', error);\n };\n \n }\n\n console.log('Using transcription params:::::', params);\n\n const input: AudioInput = file \n ? { type: 'file', content: file }\n : { type: 'url', content: url! };\n \n const transcription = await transcribeAudio(input, params);\n \n return c.json({\n transcript: params.response_format === 'text' ? \n transcription.text :\n {\n text: transcription.text,\n ...(transcription.segments && { segments: transcription.segments })\n }\n });\n } catch (error: any) {\n console.error(\"[uploadTranscribeHandler] Transcription failed:\", error);\n return c.json({ error: `Transcription failed: ${error.message}` }, 500);\n }\n};\n\n\nexport const classifySpeechHandler = async (c) => {\n try {\n let file;\n const contentType = c.req.header('content-type');\n\n if (contentType?.includes('multipart/form-data')) {\n const formData = await c.req.formData();\n file = formData.get('file');\n } else if (contentType?.includes('application/json')) {\n const body = await c.req.json();\n if (body.url) {\n const response = await fetch(body.url);\n const blob = await response.blob();\n file = new File([blob], 'audio.wav', { type: response.headers.get('content-type') || 'audio/wav' });\n }\n }\n\n if (!file) {\n return c.json({ error: 'No audio file or URL provided' }, 400);\n }\n\n try {\n const result = await hf.audioClassification({\n model: 'MIT/ast-finetuned-audioset-10-10-0.4593',\n data: file,\n });\n\n console.log('HF classification result:', result);\n\n const speechResult = result.find(pred => pred.label === 'Speech');\n const speechScore = speechResult?.score || 0;\n const SPEECH_THRESHOLD = 0.45;\n const isHumanSpeech = speechScore > SPEECH_THRESHOLD;\n\n return c.json({\n isHumanSpeech,\n confidence: speechScore,\n details: result,\n shouldProcess: isHumanSpeech\n });\n } catch (classificationError) {\n // console.warn('Classification failed, defaulting to yes [rate limit]', classificationError);\n console.warn('Classification failed, defaulting to yes [rate limit]',);\n // Default to \"yes\" for any classification errors (including rate limits)\n return c.json({\n isHumanSpeech: true,\n confidence: 1,\n details: [],\n shouldProcess: true\n });\n }\n } catch (error) {\n console.error('Speech classification failed:', error);\n return c.json({ error: error.message }, 500);\n }\n};\n\n// Add new handler\nexport const validateTranscriptHandler = async (c) => {\n try {\n const formData = await c.req.formData();\n const messages = JSON.parse(formData.get('messages'));\n const model = formData.get('model') || 'mixtral-8x7b-32768';\n const temperature = parseFloat(formData.get('temperature')) || 0.1;\n const maxTokens = parseInt(formData.get('max_tokens')) || 256;\n\n const completion = await groq.chat.completions.create({\n messages,\n model,\n temperature,\n max_tokens: maxTokens,\n response_format: { type: \"json_object\" } // Enable JSON mode\n });\n\n return c.json(completion);\n } catch (error) {\n console.error('Transcript validation failed:', error);\n return c.json({ error: error.message }, 500);\n }\n};\n\n// Add new chat completion handler\nexport const chatCompletionHandler = async (c) => {\n try {\n const formData = await c.req.formData();\n const messages = JSON.parse(formData.get('messages'));\n const transcriptContext = formData.get('transcriptContext')?.toString() || '';\n const temperature = parseFloat(formData.get('temperature')?.toString() || '0.2');\n const model = formData.get('model')?.toString() || 'llama-3.1-8b-instant';\n const systemPrompt = formData.get('systemPrompt')?.toString();\n const isAutoChat = formData.get('isAutoChat') === 'true';\n\n // Add different system prompt for auto-chat\n const autoChatPrompt = `You are a helpful AI assistant analyzing real-time transcripts.\nYour role is to:\n1. Monitor for technical terms or jargon\n2. Provide very brief, clear explanations when needed\n3. Stay quiet (respond with \"...\") if no explanation is needed\n4. Keep responses under 2 sentences\n5. Focus only on technical terminology, ignore conversation context\n\nExample:\nTranscript: \"We need to implement a REST API with proper CRUD operations\"\nResponse: \"REST API: Style of web API using HTTP methods. CRUD: Create, Read, Update, Delete - basic data operations.\"\n\nTranscript: \"Let's grab coffee later\"\nResponse: \"...\"`;\n\n const finalSystemPrompt = isAutoChat ? autoChatPrompt : systemPrompt;\n \n // Trim transcript context to max length from the end\n const trimmedContext = transcriptContext.slice(-MAX_TRANSCRIPT_CONTEXT);\n \n // Add system message with transcript context and custom prompt\n const systemMessage = {\n role: \"system\",\n content: `${finalSystemPrompt || ''}\\n\\nTranscript context:\\n\\n${trimmedContext}`\n };\n \n const completion = await groq.chat.completions.create({\n messages: [systemMessage, ...messages],\n model,\n temperature,\n max_tokens: 1024,\n top_p: 1,\n stream: true\n });\n\n // Set up SSE response\n c.header('Content-Type', 'text/event-stream');\n\n return new Response(\n new ReadableStream({\n async start(controller) {\n const encoder = new TextEncoder();\n \n for await (const chunk of completion) {\n const content = chunk.choices[0]?.delta?.content || '';\n if (content) {\n controller.enqueue(encoder.encode(`data: ${JSON.stringify({\n type: 'chat',\n content\n })}\\n\\n`));\n }\n }\n controller.close();\n }\n })\n );\n } catch (error: any) {\n console.error(\"Chat completion failed:\", error);\n return c.json({ error: `Chat failed: ${error.message}` }, 500);\n }\n};\n\n// Inlined content from ./styles.js\nexport const getTailwindConfig = () => ({\n theme: {\n extend: {\n colors: {\n 'primary': '#004699',\n 'accent': '#FFFBEB',\n 'background': '#F5F4F4',\n 'page-bg': '#EBF4FF',\n 'app-bg': '#FFFFFF',\n 'app-bg-alt': '#FFFFFF',\n 'app-black': '#004699',\n 'app-accent': '#FFF2C2',\n 'secondary': '#D9D5D4',\n 'text': '#100C04',\n 'button': '#004699',\n 'button-hover': '#0063CC',\n 'button-secondary': '#D6EAFF',\n 'button-secondary-hover': '#99CAFF',\n 'button-secondary-accent': '#161413',\n 'grad-start': '#FFF2C2',\n 'grad-end': '#D1F0DE',\n }\n }\n }\n})\n\nexport const getStyles = () => `\n\n @tailwind base;\n @tailwind components;\n @tailwind utilities;\n\n body {\n @apply bg-gradient-to-b from-grad-start to-grad-end bg-grad-end min-h-screen;\n }\n .AppBody {\n @apply text-text py-4 min-h-screen;\n }\n .AppContainer {\n @apply container mx-auto max-w-2xl;\n }\n .App {\n @apply mx-2 md:mx-auto p-4 bg-app-bg border border-app-accent rounded-md shadow-lg;\n }\n .AppLogo {\n @apply mb-2;\n }\n h1 {\n @apply text-3xl font-semibold mb-4 text-primary;\n }\n textarea {\n @apply w-full mb-4 py-2 px-4 bg-app-bg-alt border border-app-accent rounded-md focus:outline-none focus:ring-1 focus:ring-secondary;\n }\n\n button.cta {\n @apply bg-button text-white px-2 py-2 rounded-md hover:bg-button-hover transition duration-300;\n }\n button:disabled {\n @apply opacity-50 cursor-not-allowed;\n }\n #textOutput {\n @apply text-sm my-0 w-full bg-accent border-0 rounded-md whitespace-pre-wrap;\n }\n .AppFooter {\n @apply mt-4 text-center text-sm;\n }\n .AppFooter a {\n @apply text-primary hover:text-button-hover underline;\n }\n select {\n @apply w-full py-2 px-4 pr-8 bg-app-bg-alt border border-app-accent rounded-md appearance-none focus:outline-none focus:ring-1 focus:ring-secondary;\n }\n .form-checkbox {\n @apply rounded border-gray-300 text-button focus:ring-button;\n }\n details summary {\n @apply mb-2;\n }\n \n textarea {\n @apply block text-primary w-full mt-2 p-2 bg-app-bg-alt border border-app-accent rounded-md focus:outline-none focus:ring-1 focus:ring-secondary;\n }\n \n /* Add history styles */\n .history-item {\n @apply p-2 bg-accent hover:bg-app-bg-alt border border-app-accent rounded-md cursor-pointer transition-colors;\n }\n .delete-button {\n @apply self-end bg-button-secondary text-white px-2 py-1 rounded text-sm hover:bg-button-secondary-hover transition-colors;\n }\n button.text-sm {\n @apply text-xs bg-button-secondary border border-button-secondary-accent text-button-secondary-accent px-2 py-1 rounded-md hover:bg-button-secondary-hover transition duration-300;\n }\n .TextOutput {\n @apply mt-4;\n }\n .TextOutput-content {\n @apply p-4 bg-app-bg-alt border border-primary rounded-md;\n }\n .TextOutput-header {\n @apply mb-2 text-xs text-gray-600 flex justify-between items-center;\n }\n #textOutput {\n @apply resize-none focus:outline-none focus:ring-0;\n }\n .monaco-editor {\n @apply w-full border border-app-accent rounded-md;\n }\n .monaco-editor .monaco-scrollable-element > .scrollbar > .slider {\n background: rgba(100, 100, 100, 0.4) !important;\n }\n\n`;\n\n\n// Inlined content from ./auth.ts\nconst AUTH_SERVICE_URL = \"https://auth.labspace.ai\";\n\n// Auth middleware\nexport const checkAuth = async (c, next) => {\n const cookies = c.req.header('Cookie');\n const sessionMatch = cookies?.match(/auth_session=([^;]+)/);\n const sessionToken = sessionMatch?.[1];\n\n if (sessionToken) {\n try {\n const response = await fetch(`${AUTH_SERVICE_URL}/api/auth/user`, {\n headers: {\n 'Cookie': cookies,\n 'Accept': 'application/json'\n },\n mode: 'cors',\n credentials: 'include'\n });\n\n if (response.ok) {\n const result = await response.json();\n result.sessionId = sessionToken;\n c.set('user', result);\n }\n } catch (error) {\n console.error('[checkAuth] error:', error);\n }\n }\n\n await next();\n};\n\n// OAuth callback handler\nexport const handleOAuthCallback = async (c) => {\n const searchParams = new URL(c.req.url).searchParams;\n const sessionToken = searchParams.get(\"session_token\");\n const error = searchParams.get(\"error\");\n\n if (error) {\n return c.html(`\n \n `);\n }\n\n if (!sessionToken) {\n return c.html(`\n \n `);\n }\n\n c.header(\"Set-Cookie\", `auth_session=${sessionToken}; Path=/; SameSite=Lax`);\n\n return c.html(`\n \n `);\n}; \n// Inlined content from ./plugins/useAuth.js\n// Based on threadg1rl's auth system\nfunction useAuth(Alpine) {\n Alpine.data('authApp', () => ({\n user: {},\n isLoggingIn: false,\n\n init() {\n console.log('[initAuth] init');\n if (window.initialUserData) {\n console.log('[initAuth] user:', window.initialUserData);\n this.user = window.initialUserData;\n // Set the session ID if available\n if (this.user.sessionId) {\n Alpine.store('recorder').sessionId = this.user.sessionId;\n }\n }\n },\n\n loginWithGithub() {\n console.log('Logging in with GitHub');\n this.isLoggingIn = true;\n window.location.href = \"https://auth.labspace.ai/auth/github?redirect_uri=\" + encodeURIComponent(window.location.origin + \"/callback\");\n },\n\n loginWithGoogle() {\n console.log('Logging in with Google');\n this.isLoggingIn = true;\n window.location.href = \"https://auth.labspace.ai/auth/google?redirect_uri=\" + encodeURIComponent(window.location.origin + \"/callback\");\n },\n\n async handleLogout(e) {\n e.preventDefault();\n\n try {\n await fetch(\"https://auth.labspace.ai/logout\", {\n method: 'POST',\n credentials: 'include',\n mode: 'cors'\n });\n\n // Clear cookie\n document.cookie = 'auth_session=; expires=Thu, 01 Jan 1970 00:00:00 GMT; path=/';\n\n // Reset user state and session ID\n this.user = {};\n Alpine.store('recorder').sessionId = '';\n\n // Reload page\n window.location.reload();\n } catch (error) {\n console.error('[handleLogout] error:', error);\n }\n }\n }));\n}\n\n// Export the function properly\nexport const useAuthExport = `\n const useAuth = ${useAuth.toString()};\n \n document.addEventListener('alpine:init', () => {\n useAuth(Alpine);\n });\n`;\n\n\n\n\n// SVG icons for auth buttons\nconst githubSvg = ``.replace(/\"/g, '"');\nconst googleSvg = ``.replace(/\"/g, '"');\n\n// Auth UI template\nexport const authTemplate = `\n

\n \n \n \n

\n`; \n\nimport { bodyLimit } from 'npm:hono/body-limit'\nconst app = new Hono();\napp.post(\"/transcript\", transcriptHandler);\napp.post(\"/transcribe\", bodyLimit({\n maxSize: 100000 * 1024, // 100mb\n onError: (c) => {\n return c.text('too large :(', 413)\n },\n }), uploadTranscribeHandler);\napp.post(\"/classify-speech\", classifySpeechHandler);\n// app.post(\"/validate-transcript\", validateTranscriptHandler);\napp.post(\"/chat\", chatCompletionHandler);\napp.use('/*', checkAuth);\napp.get(\"/callback\", handleOAuthCallback);\nconst getHtml = () => `\n\n\n\n \n \n \n Scribe • Audio Recorder\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n

\n ${authTemplate}\n

\n \n

\n \n \n \n \n \n \n\n \n

\n \n

\n\n \n

\n \n \n \n \n \n Upload\n \n \n \n

\n \n \n

\n \n

\n\n \n \n

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