${currentLocaleStrings.pageTitle}

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_152":153,"_154":155},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_115":-5,"_20":116,"_22":117,"_118":119,"_120":121,"_25":122,"_24":-5,"_123":-5,"_124":-5,"_125":53,"_126":127,"_128":44,"_129":-5,"_130":-5,"_131":132},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":-7,"_25":26,"_27":-5,"_28":-5,"_29":-5,"_30":-5,"_31":-5},"handle","legal","name","kay","branchName","main","version","path","","project",{"_33":61,"_13":14,"_45":62,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":-5,"_69":-5,"_70":71,"_72":37,"_73":44,"_74":44,"_75":-5,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":-5,"_84":85,"_86":85,"_87":88,"_89":88,"_90":88,"_91":85,"_92":85,"_93":94,"_95":96,"_97":98,"_99":53},"tree",[56],"file","contents",[32],"upstream","pull","ahead","behind","envVars",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_18":55},"id","101e0262-4e87-11f0-823e-76b3cceeab13","main.tsx","fileId","61209300-4e46-11f0-a314-76b3cceeab13","parentId","branchId","611ec674-4e46-11f0-a314-76b3cceeab13","branchVersion",7,"latest",true,"createdAt",["D",1750500244634],6,"fileType","http","content","/**\n * Customizable Document Analysis Dashboard\n *\n * Description: This script creates a standalone, customizable document analysis web application.\n *\n * Features:\n * - Tabbed glassmorphism UI for a modern look and feel.\n * - Multiple document ingestion methods: URL, direct text input, and PDF file upload.\n * - A dynamic content analysis pipeline driven by configurable \"agents\".\n * - Extensible agent system for tasks like summarization, theme extraction, and reference finding.\n * - Optional validation of extracted reference URLs.\n * - Real-time progress indicators and loading animations for a smooth user experience.\n * - Bilingual support (English/Spanish) for UI elements.\n * - Built with 'npm:pdf.js-extract' for robust server-side PDF text extraction.\n * - Serves both the interactive HTML UI and the backend API endpoint from a single Val Town function.\n *\n * Configuration for the application (like agent definitions, UI text, and application settings)\n * is defined directly in the main function handler below.\n *\n * Assumes the 'openai' secret, containing your OpenAI API key, is set in your Val Town environment.\n *\n * Last Updated: 2024-05-22\n */\n\n// --- Type Definitions ---\ninterface AnalysisResultBase {\n [key: string]: any;\n}\ninterface Reference {\n text: string;\n context?: string;\n potentialUrl?: string | null;\n relevance?: string;\n traversalStatus?: \"success\" | \"not_attempted\" | \"failed\" | \"url_missing\";\n error?: string;\n}\ninterface LogEntry {\n agent: string;\n message: string | object;\n type: \"step\" | \"result\" | \"error\" | \"input\" | \"final\";\n}\n\n// --- HTML Generation Function (Glassmorphism UI) ---\nfunction generateHtmlShell(\n initialUrl,\n initialText,\n sourceUrl,\n appConfig,\n analysisAgents,\n localizationEn,\n localizationEs,\n) {\n // Determine the set of strings to use for initial server-side rendering based on the default language\n const currentLocaleStrings = appConfig.default_language === \"es\" ? localizationEs : localizationEn;\n currentLocaleStrings.lang = appConfig.default_language || \"en\";\n\n const escapedUrl = initialUrl ? initialUrl.replace(/\"/g, \""\") : \"\";\n const escapedText = initialText ? initialText.replace(//g, \">\") : \"\";\n\n let resultCardsHtml = \"\";\n analysisAgents.forEach(agent => {\n if (agent.ui_display_info) {\n resultCardsHtml += `\n

${\n agent.ui_display_info[`card_title_${currentLocaleStrings.lang}`] || agent.ui_display_info.card_title_en\n }

\n ${\n agent.ui_display_info.display_type === \"table\"\n ? `\n

\n \n \n \n ${\n agent.ui_display_info.table_columns.map((col) =>\n ``\n ).join(\"\")\n }\n ${\n agent.agent_id === \"reference_identifier\"\n ? ``\n : \"\"\n }\n \n \n \n

${\n col[`header_${currentLocaleStrings.lang}`] \|\| col.header_en\n }	${\n currentLocaleStrings.referenceStatusHeader \|\| \"Status\"\n }

\n `\n : `

`\n }\n

`;\n }\n });\n\n const galleryItemsHtml = appConfig.gallery_images.map((img) =>\n `

$\"${img.alt}\"$

`\n ).join(\"\");\n\n // NOTE: The {{...}} placeholders in the script tag below are replaced with JSON strings.\n return `\n\n \n \n \n ${currentLocaleStrings.pageTitle} \n \n \n \n \n \n\n\n\n \n

\n |\n \n

\n\n

\n \n

\n\n

\n ${resultCardsHtml}\n

\n\n

${galleryItemsHtml}

\n\n

\n \n \n \n \n

\n\n \n\n \n\n`;\n}\n\n// --- Main Request Handler (Server Code) ---\nexport default async function(req: Request) {\n // --- Dynamic Imports ---\n const { OpenAI } = await import(\"https://esm.town/v/std/openai\");\n const { fetch } = await import(\"https://esm.town/v/std/fetch\");\n const { PDFExtract } = await import(\"npm:pdf.js-extract\");\n\n // --- APPLICATION CONFIGURATION ---\n const APPLICATION_TITLE = \"Customizable Document Analysis Dashboard\";\n\n const APP_CONFIG = {\n default_language: \"en\",\n max_pdf_size_mb: 10,\n text_truncation_length: 25000,\n openai_model_name: \"gpt-4o\",\n contact_form_placeholders_en: { name: \"Your Name\", email: \"Your Email\", message: \"Message\" },\n contact_form_placeholders_es: { name: \"Tu Nombre\", email: \"Tu Correo\", message: \"Mensaje\" },\n gallery_images: [\n {\n src:\n \"https://images.unsplash.com/photo-1677756119517-756a188d2d94?ixlib=rb-4.0.3&q=85&fm=jpg&crop=entropy&cs=srgb&w=600\",\n alt: \"AI 1\",\n },\n {\n src:\n \"https://images.unsplash.com/photo-1696258680260-492036cd6559?ixlib=rb-4.0.3&q=85&fm=jpg&crop=entropy&cs=srgb&w=600\",\n alt: \"AI 2\",\n },\n ],\n footer_powered_by_link_text: \"Val Town\",\n document_format_accepted_label: \"PDF\",\n document_format_accepted_mime_type: \"application/pdf\",\n };\n\n const ANALYSIS_AGENTS = [\n {\n agent_id: \"summary_generator\",\n agent_name_en: \"Content Summarizer\",\n agent_name_es: \"Resumidor de Contenido\",\n system_prompt: \"You are an AI assistant. Summarize the following document: {{document_text}}\",\n expects_json: false,\n ui_display_info: {\n card_title_en: \"Summary\",\n card_title_es: \"Resumen\",\n display_type: \"paragraph\",\n },\n },\n {\n agent_id: \"theme_extractor\",\n agent_name_en: \"Theme Extractor\",\n agent_name_es: \"Extractor de Temas\",\n system_prompt:\n \"Extract the main themes from the document. Respond ONLY in JSON with a key \\\"themes\\\" holding an array of strings. Document: {{document_text}}\",\n expects_json: true,\n ui_display_info: {\n card_title_en: \"Main Themes\",\n card_title_es: \"Temas Principales\",\n display_type: \"list_of_strings\",\n source_key: \"themes\",\n },\n },\n {\n agent_id: \"reference_identifier\",\n agent_name_en: \"Reference Identifier\",\n agent_name_es: \"Identificador de Referencias\",\n system_prompt:\n \"Extract all references from the document. For each, provide text, context, potentialUrl, and relevance. Respond ONLY in JSON with a key \\\"references\\\" holding an array of objects. Document: {{document_text}}\",\n expects_json: true,\n ui_display_info: {\n card_title_en: \"Extracted References\",\n card_title_es: \"Referencias Extraídas\",\n display_type: \"table\",\n source_key: \"references\",\n table_columns: [\n { header_en: \"Text\", header_es: \"Texto\", data_key: \"text\" },\n { header_en: \"Context\", header_es: \"Contexto\", data_key: \"context\" },\n { header_en: \"Relevance\", header_es: \"Relevancia\", data_key: \"relevance\" },\n { header_en: \"URL\", header_es: \"URL\", data_key: \"potentialUrl\", is_link: true },\n ],\n },\n },\n ];\n\n const LOCALIZATION_STRINGS_EN = {\n navInicio: \"Input\",\n navInformacion: \"Results\",\n navGaleria: \"Gallery\",\n navContacto: \"Contact\",\n pageTitle: APPLICATION_TITLE,\n inputSectionTitle: \"Document Input\",\n inputUrlLabel: \"Document URL (Optional):\",\n inputUrlPlaceholder: \"https://example.com/document.html\",\n inputTextLabel: \"Or Paste Document Text:\",\n inputTextPlaceholder: \"Paste the full document text here...\",\n inputFileLabel: \"Or Upload {{document_format_accepted | default('PDF')}} Document:\",\n analyzeButton: \"Analyze Document\",\n processingButton: \"Processing...\",\n resultsTitle: \"Analysis Results\",\n galleryTitle: \"Gallery\",\n contactTitle: \"Contact Us\",\n referenceStatusHeader: \"Status\",\n statusSuccess: \"Accessed\",\n statusFailed: \"Failed\",\n statusUrlMissing: \"No URL\",\n statusNotAttempted: \"Not Checked\",\n noResultsYet: \"Submit a document on the 'Input' tab to see results here.\",\n loadingAnalysis: \"Analyzing Document...\",\n loadingStatusAgentPrefix: \"Running: \",\n loadingStatusIngestion: \"Ingesting document...\",\n loadingStatusTraversal: \"Checking references (if any)...\",\n loadingStatusComplete: \"Analysis Complete!\",\n errorInputRequired:\n \"Please provide a Document URL, paste Text, or upload a {{document_format_accepted | default('PDF')}} file.\",\n errorInvalidFile: \"Invalid file type. Please upload a {{document_format_accepted | default('PDF')}}.\",\n errorFileSize: \"File is too large (Max {{max_pdf_size_mb}}MB).\",\n errorFetchFailed: \"Failed to perform analysis: {{errorMessage}}\",\n contactNamePlaceholder: APP_CONFIG.contact_form_placeholders_en.name,\n contactEmailPlaceholder: APP_CONFIG.contact_form_placeholders_en.email,\n contactMessagePlaceholder: APP_CONFIG.contact_form_placeholders_en.message,\n sendButton: \"Send\",\n poweredBy: \"Powered by {{link_html}}\",\n };\n\n const LOCALIZATION_STRINGS_ES = {\n navInicio: \"Entrada\",\n navInformacion: \"Resultados\",\n navGaleria: \"Galería\",\n navContacto: \"Contacto\",\n pageTitle: APPLICATION_TITLE,\n inputSectionTitle: \"Entrada de Documento\",\n inputUrlLabel: \"URL del Documento (Opcional):\",\n inputUrlPlaceholder: \"https://ejemplo.com/documento.html\",\n inputTextLabel: \"O Pegue el Texto del Documento:\",\n inputTextPlaceholder: \"Pegue el texto completo del documento aquí...\",\n inputFileLabel: \"O Suba un Documento {{document_format_accepted | default('PDF')}}:\",\n analyzeButton: \"Analizar Documento\",\n processingButton: \"Procesando...\",\n resultsTitle: \"Resultados del Análisis\",\n galleryTitle: \"Galería\",\n contactTitle: \"Contacto\",\n referenceStatusHeader: \"Estado\",\n statusSuccess: \"Accedido\",\n statusFailed: \"Falló\",\n statusUrlMissing: \"Sin URL\",\n statusNotAttempted: \"No Verificado\",\n noResultsYet: \"Envíe un documento en la pestaña 'Entrada' para ver los resultados aquí.\",\n loadingAnalysis: \"Analizando Documento...\",\n loadingStatusAgentPrefix: \"Ejecutando: \",\n loadingStatusIngestion: \"Ingiriendo documento...\",\n loadingStatusTraversal: \"Verificando referencias (si las hay)...\",\n loadingStatusComplete: \"¡Análisis Completo!\",\n errorInputRequired:\n \"Por favor, proporcione una URL de documento, pegue texto o cargue un archivo {{document_format_accepted | default('PDF')}}.\",\n errorInvalidFile: \"Tipo de archivo inválido. Por favor, suba un {{document_format_accepted | default('PDF')}}.\",\n errorFileSize: \"El archivo es demasiado grande (Máx {{max_pdf_size_mb}}MB).\",\n errorFetchFailed: \"Falló la realización del análisis: {{errorMessage}}\",\n contactNamePlaceholder: APP_CONFIG.contact_form_placeholders_es.name,\n contactEmailPlaceholder: APP_CONFIG.contact_form_placeholders_es.email,\n contactMessagePlaceholder: APP_CONFIG.contact_form_placeholders_es.message,\n sendButton: \"Enviar\",\n poweredBy: \"Impulsado por {{link_html}}\",\n };\n\n // --- Helper: Extract Text using pdf.js-extract ---\n async function extractPdfTextNative(data: ArrayBuffer, fileName: string, log: LogEntry[]): Promise {\n const agent = \"PDF Extraction Agent\";\n try {\n log.push({ agent, type: \"step\", message: `Extracting text from ${fileName}...` });\n const pdfExtractor = new PDFExtract();\n const extracted = await pdfExtractor.extractBuffer(Buffer.from(data), {});\n const text = extracted.pages.map((page) => page.content.map((item) => item.str).join(\" \")).join(\"\\n\\n\");\n log.push({ agent, type: \"result\", message: `Extracted ${text.length} characters.` });\n return text;\n } catch (error) {\n log.push({ agent, type: \"error\", message: `Failed to extract PDF text: ${error.message}` });\n return null;\n }\n }\n\n // --- Helper Function: Call OpenAI API ---\n async function callOpenAI(\n openai: OpenAI,\n systemPrompt: string,\n userMessage: string,\n modelFromConfig = APP_CONFIG.openai_model_name || \"gpt-4o\",\n expectJson = false,\n ): Promise<{ role: \"assistant\" | \"system\"; content: string | object }> {\n const model = modelFromConfig;\n try {\n const response = await openai.chat.completions.create({\n model,\n messages: [{ role: \"system\", content: systemPrompt }, { role: \"user\", content: userMessage }],\n temperature: 0.5,\n ...(expectJson && { response_format: { type: \"json_object\" } }),\n });\n const content = response.choices?.[0]?.message?.content;\n if (!content) throw new Error(\"AI returned empty content.\");\n if (expectJson) {\n try {\n return { role: \"assistant\", content: JSON.parse(content) };\n } catch (e) {\n console.error(\"AI JSON Parse Error:\", e, \"Raw:\", content);\n throw new Error(`AI response not valid JSON: ${content.substring(0, 150)}`);\n }\n } else {\n return { role: \"assistant\", content };\n }\n } catch (error) {\n console.error(\"OpenAI API Error:\", error.message);\n return { role: \"system\", content: `AI Error: ${error.message}` };\n }\n }\n\n // --- Helper Function: Traverse References ---\n async function traverseReferences(references: Reference[], log: LogEntry[]): Promise {\n const agent = \"Reference Traversal Agent\";\n log.push({ agent, type: \"step\", message: `Traversing ${references.length} potential reference URLs... ` });\n\n const promises = references.map(async (ref): Promise => {\n if (!ref.potentialUrl || typeof ref.potentialUrl !== \"string\" || !ref.potentialUrl.startsWith(\"http\")) {\n ref.traversalStatus = \"url_missing\";\n return ref;\n }\n try {\n const response = await fetch(ref.potentialUrl, { method: \"HEAD\", redirect: \"follow\", timeout: 5000 });\n ref.traversalStatus = response.ok ? \"success\" : \"failed\";\n if (!response.ok) ref.error = `Status ${response.status}`;\n } catch (e) {\n ref.traversalStatus = \"failed\";\n ref.error = e.name === \"AbortError\" || e.message.includes(\"timed out\") ? \"Timeout\" : \"Fetch Error\";\n }\n return ref;\n });\n\n const results = await Promise.all(promises);\n log.push({ agent: agent, type: \"final\", message: results });\n return results;\n }\n\n // --- Main Agent Flow Logic ---\n async function runAgentFlow(\n input: { documentUrl?: string; documentText?: string; documentFile?: File },\n log: LogEntry[],\n ): Promise {\n const openai = new OpenAI();\n log.push({ agent: \"System\", type: \"step\", message: \"Workflow started.\" });\n\n let docText: string | null = null;\n const ingestAgent = \"Ingestion Agent\";\n\n // --- Ingestion ---\n if (input.documentFile) {\n log.push({ agent: ingestAgent, type: \"input\", message: `Processing uploaded file: ${input.documentFile.name}` });\n const buffer = await input.documentFile.arrayBuffer();\n docText = await extractPdfTextNative(buffer, input.documentFile.name, log);\n } else if (input.documentUrl) {\n log.push({ agent: ingestAgent, type: \"input\", message: `Fetching content from URL: ${input.documentUrl}` });\n try {\n const response = await fetch(input.documentUrl);\n if (!response.ok) throw new Error(`HTTP error! status: ${response.status}`);\n docText = await response.text();\n log.push({ agent: ingestAgent, type: \"result\", message: `Fetched ${docText.length} characters.` });\n } catch (e) {\n log.push({ agent: ingestAgent, type: \"error\", message: `Failed to fetch URL: ${e.message}. Halting.` });\n }\n } else if (input.documentText) {\n log.push({ agent: ingestAgent, type: \"input\", message: \"Processing pasted text.\" });\n docText = input.documentText;\n log.push({ agent: ingestAgent, type: \"result\", message: `Received ${docText.length} characters.` });\n }\n\n if (!docText) {\n log.push({ agent: \"System\", type: \"error\", message: \"Document ingestion failed. Halting workflow.\" });\n return log;\n }\n\n // --- Truncation ---\n const MAX_LEN = APP_CONFIG.text_truncation_length || 25000;\n let truncText = docText;\n if (docText.length > MAX_LEN) {\n truncText = docText.substring(0, MAX_LEN);\n log.push({\n agent: \"System\",\n type: \"step\",\n message: `Document truncated from ${docText.length} to ${MAX_LEN} characters.`,\n });\n }\n\n // --- Dynamic Agent Execution ---\n let extractedReferencesForTraversal: Reference[] | null = null;\n for (const agentConfig of ANALYSIS_AGENTS) {\n const currentAgentName = agentConfig.agent_name_en;\n log.push({ agent: \"System\", type: \"step\", message: `Executing Agent: ${currentAgentName}...` });\n\n const agentSystemPrompt = agentConfig.system_prompt.replace(\"{{document_text}}\", truncText);\n const agentResult = await callOpenAI(\n openai,\n agentSystemPrompt,\n truncText,\n APP_CONFIG.openai_model_name,\n agentConfig.expects_json,\n );\n\n if (agentResult.role === \"assistant\" && agentResult.content) {\n log.push({ agent: currentAgentName, type: \"result\", message: agentResult.content });\n\n if (\n agentConfig.agent_id === \"reference_identifier\" && agentConfig.expects_json\n && typeof agentResult.content === \"object\" && agentResult.content !== null\n ) {\n const sourceKey = agentConfig.ui_display_info.source_key || \"references\";\n if (Array.isArray(agentResult.content[sourceKey])) {\n extractedReferencesForTraversal = agentResult.content[sourceKey];\n }\n }\n } else {\n log.push({ agent: currentAgentName, type: \"error\", message: agentResult.content });\n }\n }\n\n // --- Optional Reference Traversal ---\n if (extractedReferencesForTraversal && extractedReferencesForTraversal.length > 0) {\n log.push({ agent: \"System\", type: \"step\", message: \"Attempting to verify reference URLs...\" });\n await traverseReferences(extractedReferencesForTraversal, log);\n } else {\n log.push({\n agent: \"Reference Traversal Agent\",\n type: \"step\",\n message: \"Skipped - no valid references for traversal.\",\n });\n log.push({ agent: \"Reference Traversal Agent\", type: \"final\", message: [] });\n }\n\n log.push({ agent: \"System\", type: \"final\", message: \"Workflow finished.\" });\n return log;\n }\n\n // --- Request Handling Logic ---\n const url = new URL(req.url);\n const format = url.searchParams.get(\"format\");\n const dynamicSourceUrl = import.meta.url.replace(\"esm.town\", \"val.town\");\n const appConfigWithUrl = { ...APP_CONFIG, footer_powered_by_url: dynamicSourceUrl };\n\n const CORS_HEADERS = {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"GET, POST, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, Accept\",\n };\n if (req.method === \"OPTIONS\") {\n return new Response(null, { headers: CORS_HEADERS });\n }\n\n if (format === \"json\" && req.method === \"POST\") {\n let inputData: { documentUrl?: string; documentText?: string; documentFile?: File } = {};\n const initialLog: LogEntry[] = [];\n try {\n const formData = await req.formData();\n const file = formData.get(\"documentFile\") as File | null;\n const urlVal = formData.get(\"documentUrl\") as string | null;\n const textVal = formData.get(\"documentText\") as string | null;\n\n if (file && file.size > 0) inputData.documentFile = file;\n if (urlVal) inputData.documentUrl = urlVal;\n if (textVal) inputData.documentText = textVal;\n\n if (!inputData.documentFile && !inputData.documentText && !inputData.documentUrl) {\n throw new Error(\"No document input provided in the request.\");\n }\n\n const finalLog = await runAgentFlow(inputData, initialLog);\n const sysHalt = finalLog.some((e) => e.agent === \"System\" && e.type === \"error\" && e.message?.includes(\"Halt\"));\n\n return new Response(JSON.stringify({ log: finalLog }), {\n status: sysHalt ? 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