\n {phase === \"analyzing\" && (\n

Analyzing Aircraft Component

{processingStep}

\n \n

\n )}\n\n {phase === \"results\" && (\n

Analysis Complete!

\n {/* Display N/A if score is not a number */}\n \n {typeof reliabilityScore === \"number\" ? reliabilityScore.toFixed(1) : \"N/A\"}\n \n Reliability Score\n

Generating detailed maintenance report...

\n )}\n

\n \n );\n}\n\n// --- Main Aircraft Maintenance Analyzer Component (Client-side) ---\nfunction AircraftMaintenanceAnalyzerClient() {\n const [imageFile, setImageFile] = useState(null);\n const [imageBase64, setImageBase64] = useState(null);\n const [analysisResult, setAnalysisResult] = useState(null); // Final displayed result\n const [pastResults, setPastResults] = useState([]);\n const [isLoading, setIsLoading] = useState(false); // Tracks the fetch request\n const [error, setError] = useState(null);\n\n // --- State for Analysis Reveal ---\n const [isRevealingAnalysis, setIsRevealingAnalysis] = useState(false); // Tracks animation visibility\n const [pendingAnalysisData, setPendingAnalysisData] = useState(null); // Temp store result for animation\n const [revealTrigger, setRevealTrigger] = useState(0); // Increment to trigger animation\n\n // Load past results from localStorage on mount\n useEffect(() => {\n try {\n const storedResults = localStorage.getItem(LOCAL_STORAGE_KEY);\n if (storedResults) {\n setPastResults(JSON.parse(storedResults));\n }\n } catch (e) {\n console.error(\"Failed to load past results from localStorage:\", e);\n setError(\"Could not load past maintenance results.\");\n }\n }, []);\n\n // Save result to localStorage when analysisResult changes AND not revealing analysis\n useEffect(() => {\n // Only save when a valid result is set and animation is finished\n if (analysisResult && analysisResult.timestamp && !isRevealingAnalysis) {\n try {\n // Avoid adding duplicates if viewing past results caused re-render\n const exists = pastResults.some(pr =>\n pr.timestamp === analysisResult.timestamp && pr.partId === analysisResult.partId\n );\n if (!exists) {\n const updatedResults = [analysisResult, ...pastResults].slice(0, 20); // Keep last 20\n setPastResults(updatedResults);\n localStorage.setItem(LOCAL_STORAGE_KEY, JSON.stringify(updatedResults));\n }\n } catch (e) {\n console.error(\"Failed to save result to localStorage:\", e);\n // Avoid setting a persistent error for this non-critical operation\n }\n }\n }, [analysisResult, pastResults, isRevealingAnalysis]); // Dependencies\n\n const handleFileChange = (event) => {\n const file = event.target.files[0];\n if (file && file.type.startsWith(\"image/\")) {\n setError(null);\n setAnalysisResult(null); // Clear previous results\n setIsRevealingAnalysis(false); // Ensure animation is reset\n setPendingAnalysisData(null); // Clear pending data\n setImageFile(file); // Keep track of the file object if needed\n\n const reader = new FileReader();\n reader.onloadend = () => {\n setImageBase64(reader.result); // Set base64 preview\n };\n reader.onerror = () => {\n setError(\"Failed to read the image file.\");\n setImageBase64(null);\n setImageFile(null);\n };\n reader.readAsDataURL(file);\n } else {\n setError(\"Please select a valid image file (jpg, png, webp, etc.).\");\n setImageBase64(null);\n setImageFile(null);\n }\n };\n\n const handleAnalysisRequest = useCallback(async () => {\n // Prevent request if no image, already loading, or animation is playing\n if (!imageBase64 || isLoading || isRevealingAnalysis) return;\n\n setIsLoading(true); // Indicate network activity start\n setError(null);\n setAnalysisResult(null); // Clear previous final result\n setIsRevealingAnalysis(false); // Ensure animation isn't showing yet\n setPendingAnalysisData(null); // Clear old pending data\n\n try {\n // Extract base64 data part (remove data:image/...;base64,)\n const base64Data = imageBase64.split(\",\")[1];\n if (!base64Data) {\n throw new Error(\"Could not extract image data for processing.\");\n }\n\n // --- Call the backend API ---\n const response = await fetch(\"/analyze-aircraft\", { // Match server endpoint\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ imageBase64: base64Data }), // Send only the data\n });\n\n if (!response.ok) {\n let errorMsg = `Analysis request failed: ${response.status} ${response.statusText}`;\n try {\n // Try to get more specific error from response body\n const errData = await response.json();\n errorMsg = errData.error || errorMsg;\n } catch { /* Ignore if response body isn't JSON */ }\n throw new Error(errorMsg);\n }\n\n const data = await response.json();\n\n // Validate response structure (check for the main analysisReport key)\n if (!data || !data.analysisReport) {\n console.error(\"Received unexpected data structure from server:\", data);\n throw new Error(\"Received incomplete or invalid analysis data from the server.\");\n }\n\n // Add the image preview back for local storage and viewing history\n const resultWithPreview = {\n ...data.analysisReport, // Use the nested report\n imagePreview: imageBase64, // Add preview for history list\n // timestamp is added by the server\n };\n\n // --- Trigger Analysis Reveal ---\n console.log(\"Analysis data received from server, preparing animation...\");\n setPendingAnalysisData(resultWithPreview); // Store data for the animation/final display\n setIsLoading(false); // Network request finished\n setIsRevealingAnalysis(true); // Start the animation visibility\n setRevealTrigger(prev => prev + 1); // Increment trigger to start animation effect\n } catch (err) {\n console.error(\"Analysis request failed:\", err);\n setError(`Analysis Error: ${err.message}`);\n setAnalysisResult(null); // Clear any potential stale result\n setIsLoading(false); // Stop loading indicator\n setIsRevealingAnalysis(false); // Ensure animation is off\n setPendingAnalysisData(null); // Clear pending data\n }\n }, [imageBase64, isLoading, isRevealingAnalysis]); // Dependencies\n\n // --- Handler for when animation completes ---\n const handleRevealComplete = useCallback(() => {\n console.log(\"Handling reveal completion: displaying final report.\");\n setIsRevealingAnalysis(false); // Hide the animation overlay\n setAnalysisResult(pendingAnalysisData); // Move data to final display state\n setPendingAnalysisData(null); // Clear the temporary data\n window.scrollTo(0, 0); // Scroll to top to show the report\n }, [pendingAnalysisData]); // Dependency\n\n const clearHistory = () => {\n try {\n localStorage.removeItem(LOCAL_STORAGE_KEY);\n setPastResults([]);\n setError(null); // Clear errors when history is cleared\n } catch (e) {\n console.error(\"Failed to clear localStorage:\", e);\n setError(\"Could not clear maintenance history.\");\n }\n };\n\n const viewPastResult = (result) => {\n setImageBase64(result.imagePreview); // Show the image from history\n setAnalysisResult(result); // Display the report from history\n setError(null);\n setImageFile(null); // Clear current file selection\n setIsRevealingAnalysis(false); // Ensure reveal animation is off\n setPendingAnalysisData(null); // Clear pending data\n window.scrollTo(0, 0); // Scroll to top\n };\n\n // --- Render Functions (Mostly unchanged from original, adapt as needed) ---\n // These functions take the `analysisResult` data and render sections.\n // Added checks for potentially null/undefined data from AI.\n\n const renderMaintenanceStatus = (status) => {\n if (!status) return null;\n // Provide defaults for missing fields\n const safeStatus = {\n lastServiced: status.lastServiced || \"N/A\",\n hoursInService: status.hoursInService ?? \"N/A\", // Use ?? for 0 or null/undefined\n cyclesCompleted: status.cyclesCompleted ?? \"N/A\",\n scheduledMaintenance: status.scheduledMaintenance || \"N/A\",\n };\n\n return (\n

Maintenance Status

Last Serviced

{safeStatus.lastServiced}

Hours In Service

{safeStatus.hoursInService}

Cycles Completed

{safeStatus.cyclesCompleted}

Next Scheduled

{safeStatus.scheduledMaintenance}

\n );\n };\n\n const renderRelatedComponents = (components) => {\n if (!Array.isArray(components) || components.length === 0) return null;\n\n return (\n

Related Components Analysis

\n {components.map((component, index) => {\n // Provide defaults for missing fields\n const safeComponent = {\n id: component.id || `Comp-${index + 1}`,\n name: component.name || \"Unknown Component\",\n reliabilityScore: component.reliabilityScore ?? null, // Keep null if missing\n maintenanceRecommended: component.maintenanceRecommended || false,\n upsellOpportunity: component.upsellOpportunity || false,\n };\n const score = safeComponent.reliabilityScore;\n const scoreClass = getReliabilityColorClass(score);\n const scoreDisplay = typeof score === \"number\" ? score.toFixed(1) : \"N/A\";\n\n return (\n \n

\n {safeComponent.id}\n \n {scoreDisplay}\n \n

{safeComponent.name}

\n {safeComponent.maintenanceRecommended && (\n

Maintenance Recommended

\n )}\n {safeComponent.upsellOpportunity &&

Upsell Opportunity

}\n

\n );\n })}\n

\n \n );\n };\n\n const renderSafetyIssues = (issues) => {\n if (!Array.isArray(issues) || issues.length === 0) return null;\n\n return (\n

Identified Safety Issues / Concerns

\n
\n {safeIssue.component}\n {safeIssue.severity} Severity\n
\n
{safeIssue.description}
\n
\n Recommended Action: {safeIssue.recommendedAction}\n
\n

\n );\n };\n\n const renderRecommendedServices = (services) => {\n if (!Array.isArray(services) || services.length === 0) return null;\n\n return (\n

Recommended Services

\n {services.map((service, index) => {\n // Provide defaults for missing fields\n const safeService = {\n service: service.service || \"General Maintenance\",\n priority: service.priority || \"Low\",\n estimatedCost: service.estimatedCost || \"N/A\",\n timeframe: service.timeframe || \"At next opportunity\",\n benefit: service.benefit || \"General upkeep\",\n };\n const priorityClass = `priority-${(safeService.priority || \"low\").toLowerCase()}`;\n\n return (\n

\n {safeService.service}\n {safeService.priority} Priority\n

\n Cost: {safeService.estimatedCost}\n

\n Timeframe: {safeService.timeframe}\n

\n Benefit: {safeService.benefit}\n

\n );\n })}\n

\n );\n };\n\n const renderUpsellOpportunities = (opportunities) => {\n if (!Array.isArray(opportunities) || opportunities.length === 0) return null;\n\n return (\n

Potential Upsell Opportunities

\n {opportunities.map((item, index) => {\n // Provide defaults for missing fields\n const safeItem = {\n item: item.item || \"Additional Service/Part\",\n estimatedCost: item.estimatedCost || \"Variable\",\n benefits: item.benefits || \"Potential improvements.\",\n roi: item.roi || \"Not specified.\",\n };\n\n return (\n

\n {safeItem.item}\n {safeItem.estimatedCost}\n

\n Benefits: {safeItem.benefits}\n

\n ROI/Justification: {safeItem.roi}\n

\n );\n })}\n

\n );\n };\n\n const renderReport = (report) => {\n if (!report) return null;\n\n // Provide defaults for top-level fields\n const safeReport = {\n timestamp: report.timestamp || \"N/A\",\n partId: report.partId || \"ID Unknown\",\n partName: report.partName || \"Component Unknown\",\n aircraftType: report.aircraftType || \"Aircraft Type Unknown\",\n reliabilityScore: report.reliabilityScore ?? null, // Use null if missing for color logic\n maintenanceStatus: report.maintenanceStatus || null,\n safetyIssues: report.safetyIssues || [],\n recommendedServices: report.recommendedServices || [],\n upsellOpportunities: report.upsellOpportunities || [],\n relatedComponents: report.relatedComponents || [],\n disclaimer: report.disclaimer || \"Standard analysis disclaimer applies.\", // AI might generate one\n };\n\n const score = safeReport.reliabilityScore;\n const scoreClass = getReliabilityColorClass(score);\n const scoreDisplay = typeof score === \"number\" ? score.toFixed(1) : \"N/A\";\n\n return (\n

AI-Powered Aircraft Maintenance Analysis

{safeReport.timestamp}

\n\n

\n {safeReport.partId}\n {safeReport.partName}\n

{safeReport.aircraftType}

\n\n

Est. Reliability Score

\n {scoreDisplay}\n

\n\n {/* Render sections using safe data */}\n {renderMaintenanceStatus(safeReport.maintenanceStatus)}\n {renderSafetyIssues(safeReport.safetyIssues)}\n {renderRecommendedServices(safeReport.recommendedServices)}\n {renderUpsellOpportunities(safeReport.upsellOpportunities)}\n {renderRelatedComponents(safeReport.relatedComponents)}\n\n

\n Disclaimer: {safeReport.disclaimer}{\" \"}\n This AI analysis is based on visual data and pattern recognition. It is intended for informational purposes\n and to assist qualified personnel. Always adhere to official manufacturer maintenance manuals, service\n bulletins, and perform physical inspections by certified technicians before undertaking any maintenance\n actions. Real-world conditions and internal component states may differ from visual estimations.\n

\n );\n };\n\n const renderPastResults = () => {\n if (pastResults.length === 0) {\n return

No past analysis results found in local storage.

;\n }\n return (\n

Analysis History (Last {pastResults.length})

\n \n Clear History\n \n

\n \n \n {/* Provide fallback names/IDs */}\n {result.partName || \"Aircraft Component\"} - {result.partId || \"ID Unknown\"}\n
\n ({result.timestamp || \"Timestamp missing\"})\n \n viewPastResult(result)}\n className=\"secondary view-btn\"\n disabled={isLoading || isRevealingAnalysis} // Disable if loading or animating\n >\n View\n \n

\n );\n };\n\n // --- Main Render ---\n return (\n // Changed from Card to basic div for simpler Val Town compatibility\n

\n {/* --- Render Analysis Reveal Overlay conditionally --- */}\n {isRevealingAnalysis && pendingAnalysisData && (\n \n )}\n\n {/* Changed from CardHeader/Title/Content */}\n

Aircraft Maintenance Analyzer (AI Vision)

\n Upload a clear photo of an aircraft component. The AI will analyze it for potential maintenance needs,\n estimate reliability, identify related components, suggest services, and flag potential safety concerns or\n upsell opportunities based on visual inspection.\n

\n\n

\n \n {/* Show preview only if base64 exists and animation is NOT playing */}\n {imageBase64 && !isRevealingAnalysis && (\n

\n $\"Component$ \n

\n )}\n \n {/* Dynamic button text */}\n {isLoading ? \"Analyzing via AI...\" : \"Analyze Component\"}\n \n

\n\n {/* Show loading message ONLY if loading AND NOT revealing */}\n {isLoading && !isRevealingAnalysis && (\n

\n Contacting AI Vision Service... Analyzing image features. This may take a few moments.\n

\n )}\n {/* Show error message ONLY if error exists AND NOT revealing */}\n {error && !isRevealingAnalysis &&

{error}

}\n\n {/* Show final report ONLY if result exists AND NOT revealing */}\n {analysisResult && !isRevealingAnalysis && renderReport(analysisResult)}\n\n

\n {/* Render past results section */}\n {renderPastResults()}\n

\n );\n}\n\n// --- Client-side Entry Point ---\nfunction client() {\n const rootElement = document.getElementById(\"root\");\n if (rootElement) {\n createRoot(rootElement).render();\n } else {\n console.error(\"Root element not found for React app\");\n }\n}\n// Ensure this runs only in the browser environment\nif (typeof document !== \"undefined\") {\n // Use requestAnimationFrame to ensure the DOM is ready\n requestAnimationFrame(client);\n}\n\n// --- Backend Server (Val Town / Cloud Function) ---\nexport default async function server(request: Request): Promise {\n // Dynamically import OpenAI only when needed on the server\n const { OpenAI } = await import(\"https://esm.town/v/std/openai\");\n\n // Standard CORS preflight handling\n if (request.method === \"OPTIONS\") {\n return new Response(null, {\n headers: {\n \"Allow\": \"POST, GET, OPTIONS\",\n \"Access-Control-Allow-Origin\": \"*\", // Allow all origins for simplicity\n \"Access-Control-Allow-Methods\": \"POST, GET, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type\",\n },\n });\n }\n\n // Standard CORS headers for actual requests\n const corsHeaders = {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"POST, GET, OPTIONS\", // Explicitly allow POST\n \"Access-Control-Allow-Headers\": \"Content-Type\",\n };\n\n // --- API Route: /analyze-aircraft ---\n if (request.method === \"POST\" && new URL(request.url).pathname === \"/analyze-aircraft\") {\n try {\n // Check for OpenAI API Key in Val Town secrets (Val Town automatically injects process.env)\n // The library handles finding the key from secrets/env vars.\n\n const openai = new OpenAI(); // Instantiates with key from secrets\n\n const { imageBase64 } = await request.json();\n\n if (!imageBase64 || typeof imageBase64 !== \"string\") {\n return Response.json({ error: \"Missing or invalid image data (imageBase64).\" }, {\n status: 400,\n headers: { ...corsHeaders, \"Content-Type\": \"application/json\" }, // Ensure correct Content-Type\n });\n }\n\n console.log(\"Received image data for aircraft analysis, preparing request to OpenAI...\");\n\n const modelChoice = \"gpt-4o\"; // Use the powerful vision model\n\n // --- Construct the detailed prompt for OpenAI ---\n // This prompt guides the AI to act as an aircraft maintenance analyzer.\n const prompt = `\nYou are an AI assistant simulating an experienced aircraft maintenance technician performing a visual inspection based SOLELY on the provided image. Your goal is to identify the component, assess its condition, estimate its reliability, suggest potential maintenance, and identify related factors.\n\n**Analysis Instructions:**\n\n1. **Component Identification:**\n * Identify the primary aircraft component visible in the image (e.g., \"Landing Gear Strut\", \"Turbine Blade\", \"Hydraulic Actuator\", \"Avionics Bay Wiring\").\n * Attempt to identify the likely aircraft type if possible from context or component style (e.g., \"General Aviation\", \"Commercial Airliner - Wide Body\", \"Military Fighter\"). State if uncertain.\n * Assign a plausible-sounding 'partId' (e.g., \"B777-LG-S001\", \"GA-PROP-B03\").\n\n2. **Condition Assessment:**\n * Visually inspect for signs of wear, damage, corrosion, leaks, cracks, loose fittings, discoloration, heat stress, or other anomalies.\n * Evaluate the general cleanliness and state of preservation.\n * Specifically comment on:\n * 'surfaceCondition': Note scratches, dents, paint condition, corrosion, contamination.\n * 'structuralIntegrity': Mention any visible cracks, bends, warping, or missing fasteners.\n * 'connections': Check visible wiring, hoses, linkages for security and condition.\n\n3. **Reliability Score:**\n * Based *only* on the visual assessment, estimate a 'reliabilityScore' on a scale of 0.0 to 10.0, where 10.0 is pristine condition and 0.0 is critically failed. Justify the score briefly based on observed condition.\n\n4. **Maintenance Status (Simulated):**\n * Provide *plausible* simulated data for 'maintenanceStatus'. Do not state these are real.\n * 'lastServiced': e.g., \"YYYY-MM-DD\" (approx 6-18 months ago)\n * 'hoursInService': e.g., a number between 1000-8000\n * 'cyclesCompleted': e.g., a number between 500-3000\n * 'scheduledMaintenanc': e.g., \"YYYY-MM-DD\" (approx 3-12 months from now)\n\n5. **Safety Issues:**\n * List any observed conditions that could pose a safety risk in a 'safetyIssues' array. Each item should have:\n * 'component': The specific part of the item affected (e.g., \"Seal\", \"Bracket\", \"Wiring Harness\").\n * 'severity': \"Low\", \"Medium\", \"High\", \"Critical\".\n * 'description': What was observed?\n * 'recommendedAction': e.g., \"Immediate inspection required\", \"Replace at next service interval\", \"Monitor closely\".\n\n6. **Recommended Services:**\n * Suggest relevant maintenance tasks based on the visual condition in a 'recommendedServices' array. Each item should have:\n * 'service': e.g., \"Clean and Lubricate\", \"Detailed NDT Inspection\", \"Replace Seal\", \"System Flush\".\n * 'priority': \"Low\", \"Medium\", \"High\".\n * 'estimatedCost': e.g., \"$XXX - $YYYY\" or \"Variable\".\n * 'timeframe': e.g., \"Within next 50 flight hours\", \"Next scheduled check\", \"Immediately\".\n * 'benefit': Why is this service needed? (e.g., \"Prevent corrosion\", \"Ensure proper function\").\n\n7. **Upsell Opportunities:**\n * Identify potential upgrades or related services that could be offered in an 'upsellOpportunities' array. Each item should have:\n * 'item': e.g., \"Corrosion Inhibitor Treatment\", \"Upgraded Seal Material\", \"Predictive Sensor Installation\".\n * 'estimatedCost': e.g., \"$XXX - $YYYY\".\n * 'benefits': What are the advantages? (e.g., \"Extended component life\", \"Improved reliability\", \"Early failure detection\").\n * 'roi': Brief justification (e.g., \"May prevent costly unscheduled maintenance\").\n\n8. **Related Components:**\n * Mention 1-3 other components typically associated with the primary one in a 'relatedComponents' array. For each, *estimate* based on general knowledge:\n * 'id': Plausible ID.\n * 'name': Name of the related component.\n * 'reliabilityScore': A *guess* (0.0-10.0) assuming average condition if not visible.\n * 'maintenanceRecommended': 'true' or 'false' (guess based on typical wear).\n * 'upsellOpportunity': 'true' or 'false' (if upgrades often apply).\n\n9. **Disclaimer:** Include a brief standard disclaimer about this being an AI visual estimate.\n\n**Output Format:**\nRespond ONLY with a single JSON object. No explanatory text before or after the JSON. Adhere strictly to this structure:\n{\n \"analysisReport\": {\n \"partId\": \"string\",\n \"partName\": \"string\",\n \"aircraftType\": \"string\",\n \"reliabilityScore\": number | null, // (0.0-10.0)\n \"condition\": {\n \"surfaceCondition\": \"string (Observations)\",\n \"structuralIntegrity\": \"string (Observations)\",\n \"connections\": \"string (Observations)\"\n },\n \"maintenanceStatus\": { // Simulated data\n \"lastServiced\": \"string (YYYY-MM-DD)\",\n \"hoursInService\": number,\n \"cyclesCompleted\": number,\n \"scheduledMaintenance\": \"string (YYYY-MM-DD)\"\n },\n \"safetyIssues\": [\n { \"component\": \"string\", \"severity\": \"string\", \"description\": \"string\", \"recommendedAction\": \"string\" }\n ],\n \"recommendedServices\": [\n { \"service\": \"string\", \"priority\": \"string\", \"estimatedCost\": \"string\", \"timeframe\": \"string\", \"benefit\": \"string\" }\n ],\n \"upsellOpportunities\": [\n { \"item\": \"string\", \"estimatedCost\": \"string\", \"benefits\": \"string\", \"roi\": \"string\" }\n ],\n \"relatedComponents\": [\n { \"id\": \"string\", \"name\": \"string\", \"reliabilityScore\": number | null, \"maintenanceRecommended\": boolean, \"upsellOpportunity\": boolean }\n ],\n \"disclaimer\": \"string\"\n }\n}\nEnsure all string fields provide meaningful information based on the visual analysis or plausible simulation. Use 'null' for scores if assessment is impossible from the image.\n`;\n\n // --- Call OpenAI API ---\n const response = await openai.chat.completions.create({\n model: modelChoice,\n response_format: { type: \"json_object\" }, // Enforce JSON output\n messages: [\n { role: \"system\", content: prompt }, // System prompt defines the AI's role and task\n {\n role: \"user\", // User message provides the image and instruction\n content: [\n {\n type: \"text\",\n text: \"Analyze this aircraft component image and provide the report in the specified JSON format.\",\n },\n {\n type: \"image_url\",\n image_url: {\n url: `data:image/jpeg;base64,${imageBase64}`, // Pass base64 image data directly\n detail: \"high\", // Use high detail for better analysis\n },\n },\n ],\n },\n ],\n max_tokens: 2500, // Adjust token limit as needed for potentially detailed reports\n temperature: 0.3, // Lower temperature for more deterministic/factual output\n });\n\n console.log(\"Received response from OpenAI for aircraft analysis.\");\n\n const jsonString = response.choices[0]?.message?.content;\n if (!jsonString) {\n throw new Error(\"OpenAI did not return valid response content.\");\n }\n\n let parsedResponse;\n try {\n parsedResponse = JSON.parse(jsonString);\n console.log(\"Successfully parsed JSON response from OpenAI.\");\n } catch (parseError) {\n console.error(\"Failed to parse JSON from OpenAI:\", jsonString);\n // Try to provide the invalid JSON in the error for debugging\n throw new Error(`AI returned improperly formatted JSON data. Received: ${jsonString.substring(0, 500)}...`);\n }\n\n // Add extra validation for the nested analysisReport structure\n if (!parsedResponse || !parsedResponse.analysisReport) {\n console.error(\"Parsed JSON missing 'analysisReport' key:\", parsedResponse);\n throw new Error(\"AI response missing expected 'analysisReport' structure.\");\n }\n\n // Add server-generated timestamp to the report\n parsedResponse.analysisReport.timestamp = generateTimestamp();\n\n // Return the complete parsed structure { analysisReport: { ... } }\n return Response.json(parsedResponse, {\n headers: { ...corsHeaders, \"Content-Type\": \"application/json\" },\n });\n } catch (error) {\n console.error(`Error in /analyze-aircraft endpoint: ${error.message}`, error.stack);\n // Provide a more informative error message to the client\n return Response.json({ error: `Server error during analysis: ${error.message}` }, {\n status: 500,\n headers: { ...corsHeaders, \"Content-Type\": \"application/json\" }, // Ensure correct Content-Type\n });\n }\n }\n\n // --- Serve HTML for GET requests (loads the React app) ---\n if (request.method === \"GET\" && new URL(request.url).pathname === \"/\") {\n // Inject the correct script source URL dynamically\n const finalHtmlContent = htmlContent.replace(\n \"\", // Placeholder in template\n ``, // Actual URL of the Val\n );\n return new Response(finalHtmlContent, {\n headers: { ...corsHeaders, \"content-type\": \"text/html; charset=utf-8\" },\n });\n }\n\n // --- Default 404 for other routes ---\n return new Response(\"Not Found\", {\n status: 404,\n headers: { ...corsHeaders, \"Content-Type\": \"text/plain\" }, // Plain text for 404\n });\n}\n\n// --- HTML Content (Includes CSS for both Analyzer and Animation) ---\n// NOTE: Removed ShadCN UI component classes, using basic semantic HTML and custom CSS.\nconst htmlContent = `\n\n\n\n AI Aircraft Maintenance Analyzer\n \n \n \n \n \n \n\n\n \n

\n Loading Aircraft Analyzer Interface...\n

\n \n \n\n\n`;","deleted","label",[98],"permalink","https://www.val.town/x/Learn/aircraft/code/main.tsx","extension","actionData","errors"]