AI Defect Heatmap Analyzer

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_168":169,"_170":171},"routes/_app.x.$handle.$name",{"_99":98,"_32":45,"_107":85,"_27":108,"_13":14,"_109":-5,"_11":110,"_111":112,"_113":114,"_115":116,"_117":118,"_20":-5,"_119":120,"_121":122,"_123":25,"_124":125,"_126":57,"_127":-5,"_128":-5,"_129":130},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",18,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","01988bc3-0163-721a-a41f-36e2c8c94164","name","fileType","http","content","// This is a Val Town (https://val.town) HTTP Val using the Hono web framework.\n// This is the final, corrected version with a robust, two-call streaming strategy.\n\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Hono } from \"npm:hono@4.4.12\";\nimport { stream } from \"npm:hono/streaming\";\nimport type { Context } from \"npm:hono@4.4.12\";\n\n// --- AI PROMPT FOR STEP 1: INITIAL ANALYSIS ---\nconst ANALYSIS_PROMPT = `\nYou are a high-speed AI Quality Control Analyst. Your sole function is to look at an image of a plastic part and provide a brief, high-level analysis of the visible defects.\n\n# Output Format\nYou MUST respond with a single, minified JSON object with one key: \"defectAnalysis\".\nThe value should be an array of objects. Each object must have \"defectName\", \"definition\", and \"potentialCauses\".\nIf no defects are visible, return an empty array.\n\n# Knowledge Base\n\n### 1. Sink Marks\n* **Definition**: Depressions on a part's surface.\n* **Potential Causes**: Excessively thick sections; Insufficient cooling time.\n\n### 2. Knit Lines\n* **Definition**: A visible line where two molten plastic fronts failed to fuse.\n* **Potential Causes**: Part geometry with holes; Low material/mold temperature.\n\n### 3. Flash\n* **Definition**: A thin, unwanted layer of excess plastic on the part's edge.\n* **Potential Causes**: Insufficient clamp force; Worn mold surfaces.\n\n### 4. Splay\n* **Definition**: A silvery spray pattern on the part surface.\n* **Potential Causes**: Moisture in the resin; Trapped gasses.\n\n### 5. Burn Marks\n* **Definition**: Black or brown scorch marks on the part.\n* **Potential Causes**: Trapped air; Excessive injection speed.\n`;\n\n// --- AI PROMPT FOR STEP 2: HEATMAP GENERATION ---\nconst HEATMAP_PROMPT = `\nYou are a focused AI **Heatmap Generator**. You will be given an image and a high-level analysis of its defects. Your only job is to generate a stream of JSON objects representing a 16x16 heatmap of defect probabilities.\n\n# Core Rule: Output Format\nYou MUST generate a sequence of individual JSON objects, ONE PER LINE.\n\n## Output Sequence:\n1. **Heatmap Grid (256 Lines)**: Output exactly 256 lines. Each line represents one cell of a 16x16 grid, from index 0 to 255.\n - Format: \\`{\"type\": \"heatmap\", \"index\": N, \"value\": P}\\`\n - \\`N\\` is the integer index (0-255).\n - \\`P\\` is the float probability (0.0-1.0) of a defect in that cell, based on the image and the provided analysis.\n\n2. **Done Signal (Final Line)**: After the heatmap, your final line must be:\n - Format: \\`{\"type\": \"done\"}\\`\n`;\n\n// --- FRONTEND GENERATION ---\n\nfunction generateHtml(sourceUrl: string) {\n const baseUrl = sourceUrl.endsWith(\"/\") ? sourceUrl : `${sourceUrl}/`;\n\n return `\n\n\n \n \n AI Defect Heatmap Analyzer\n \n \n \n\n\n\n

\n \n

Awaiting Analysis...

Analysis Failed

\n\n\n`;\n}\n\n// --- HONO APPLICATION SETUP ---\n\nconst app = new Hono();\n\n// 1. Serve the frontend application\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// 2. NEW: Non-streaming endpoint for fast initial analysis\napp.post(\"/api/analyze\", async (c: Context) => {\n try {\n const { image } = await c.req.json();\n if (!image || !image.startsWith(\"data:image/\")) {\n return c.json({ error: \"A valid base64 image string is required.\" }, 400);\n }\n\n const openai = new OpenAI();\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: ANALYSIS_PROMPT },\n {\n role: \"user\",\n content: [{\n type: \"image_url\",\n image_url: { url: image, detail: \"low\" },\n }],\n },\n ],\n temperature: 0.1,\n response_format: { type: \"json_object\" },\n });\n\n const analysisResult = JSON.parse(response.choices[0].message.content);\n return c.json(analysisResult);\n } catch (error) {\n console.error(\"Analysis Error:\", error);\n return c.json({ error: \"Failed to get initial analysis from AI.\" }, 500);\n }\n});\n\n// 3. NEW: Streaming endpoint dedicated to heatmap generation\napp.post(\"/api/generate-heatmap\", (c: Context) => {\n return stream(c, async (sse) => {\n const keepAlive = setInterval(() => {\n sse.write(\": keep-alive\\n\");\n }, 10000);\n sse.onAbort(() => {\n clearInterval(keepAlive);\n });\n\n try {\n const { image, analysisContext } = await c.req.json();\n if (!image || !analysisContext) {\n await sse.write(\n JSON.stringify({\n type: \"error\",\n message: \"Image and analysisContext are required.\",\n }) + \"\\n\",\n );\n return;\n }\n\n const openai = new OpenAI();\n const aiStream = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: HEATMAP_PROMPT },\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text:\n `Based on the following analysis, generate the heatmap: ${analysisContext}`,\n },\n { type: \"image_url\", image_url: { url: image, detail: \"low\" } },\n ],\n },\n ],\n temperature: 0.1,\n stream: true,\n });\n\n let buffer = \"\";\n for await (const part of aiStream) {\n buffer += part.choices[0]?.delta?.content || \"\";\n let newlineIndex;\n while ((newlineIndex = buffer.indexOf(\"\\n\")) !== -1) {\n const line = buffer.slice(0, newlineIndex);\n buffer = buffer.slice(newlineIndex + 1);\n if (line.trim()) await sse.write(line + \"\\n\");\n }\n }\n if (buffer.trim()) await sse.write(buffer + \"\\n\");\n } catch (error) {\n console.error(\"Heatmap Stream Error:\", error);\n await sse.write(\n JSON.stringify({ type: \"error\", message: error.message }) + \"\\n\",\n );\n } finally {\n clearInterval(keepAlive);\n }\n });\n});\n\nexport default app.fetch;","// This is a Val Town (https://val.town) HTTP Val using the Hono web framework.\n// This is the final, corrected version with a robust, two-call streaming strategy.\n\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Hono } from \"npm:hono@4.4.12\";\nimport { stream } from \"npm:hono/streaming\";\nimport type { Context } from \"npm:hono@4.4.12\";\n\n// --- AI PROMPT FOR STEP 1: INITIAL ANALYSIS ---\n// --- AI PROMPT FOR STEP 1: INITIAL ANALYSIS ---\nconst ANALYSIS_PROMPT = `\nYou are a high-speed AI Quality Control Analyst. Your sole function is to look at an image of a plastic part and provide a brief, high-level analysis of the visible defects.\n\n# Output Format\nYou MUST respond with a single, minified JSON object with one key: \"defectAnalysis\".\nThe value should be an array of objects. Each object must have \"defectName\", \"definition\", and \"potentialCauses\".\nThe \"potentialCauses\" key MUST always have a value that is an array of strings.\nIf no defects are visible, return an empty array for the \"defectAnalysis\" value.\n\n# Knowledge Base\n\n### 1. Sink Marks\n* **Definition**: \"Depressions on a part's surface.\"\n* **Potential Causes**: [\"Excessively thick sections\", \"Insufficient cooling time\"]\n\n### 2. Knit Lines\n* **Definition**: \"A visible line where two molten plastic fronts failed to fuse.\"\n* **Potential Causes**: [\"Part geometry with holes\", \"Low material/mold temperature\"]\n\n### 3. Flash\n* **Definition**: \"A thin, unwanted layer of excess plastic on the part's edge.\"\n* **Potential Causes**: [\"Insufficient clamp force\", \"Worn mold surfaces\"]\n\n### 4. Splay\n* **Definition**: \"A silvery spray pattern on the part surface.\"\n* **Potential Causes**: [\"Moisture in the resin\", \"Trapped gasses\"]\n\n### 5. Burn Marks\n* **Definition**: \"Black or brown scorch marks on the part.\"\n* **Potential Causes**: [\"Trapped air\", \"Excessive injection speed\"]\n`;\n\n// --- AI PROMPT FOR STEP 2: HEATMAP GENERATION ---\nconst HEATMAP_PROMPT = `\nYou are a focused AI **Heatmap Generator**. You will be given an image and a high-level analysis of its defects. Your only job is to generate a stream of JSON objects representing a 16x16 heatmap of defect probabilities.\n\n# Core Rule: Output Format\nYou MUST generate a sequence of individual JSON objects, ONE PER LINE.\n\n## Output Sequence:\n1. **Heatmap Grid (256 Lines)**: Output exactly 256 lines. Each line represents one cell of a 16x16 grid, from index 0 to 255.\n - Format: \\`{\"type\": \"heatmap\", \"index\": N, \"value\": P}\\`\n - \\`N\\` is the integer index (0-255).\n - \\`P\\` is the float probability (0.0-1.0) of a defect in that cell, based on the image and the provided analysis.\n\n2. **Done Signal (Final Line)**: After the heatmap, your final line must be:\n - Format: \\`{\"type\": \"done\"}\\`\n`;\n\n// --- FRONTEND GENERATION ---\n\nfunction generateHtml(sourceUrl: string) {\n const baseUrl = sourceUrl.endsWith(\"/\") ? sourceUrl : `${sourceUrl}/`;\n\n return `\n\n\n \n \n AI Defect Heatmap Analyzer\n \n \n \n\n\n\n

\n \n

Awaiting Analysis...

Analysis Failed

\n\n\n`;\n}\n\n// --- HONO APPLICATION SETUP ---\n\nconst app = new Hono();\n\n// 1. Serve the frontend application\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// 2. NEW: Non-streaming endpoint for fast initial analysis\napp.post(\"/api/analyze\", async (c: Context) => {\n try {\n const { image } = await c.req.json();\n if (!image || !image.startsWith(\"data:image/\")) {\n return c.json({ error: \"A valid base64 image string is required.\" }, 400);\n }\n\n const openai = new OpenAI();\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: ANALYSIS_PROMPT },\n {\n role: \"user\",\n content: [{\n type: \"image_url\",\n image_url: { url: image, detail: \"low\" },\n }],\n },\n ],\n temperature: 0.1,\n response_format: { type: \"json_object\" },\n });\n\n const analysisResult = JSON.parse(response.choices[0].message.content);\n return c.json(analysisResult);\n } catch (error) {\n console.error(\"Analysis Error:\", error);\n return c.json({ error: \"Failed to get initial analysis from AI.\" }, 500);\n }\n});\n\n// 3. NEW: Streaming endpoint dedicated to heatmap generation\napp.post(\"/api/generate-heatmap\", (c: Context) => {\n return stream(c, async (sse) => {\n const keepAlive = setInterval(() => {\n sse.write(\": keep-alive\\n\");\n }, 10000);\n sse.onAbort(() => {\n clearInterval(keepAlive);\n });\n\n try {\n const { image, analysisContext } = await c.req.json();\n if (!image || !analysisContext) {\n await sse.write(\n JSON.stringify({\n type: \"error\",\n message: \"Image and analysisContext are required.\",\n }) + \"\\n\",\n );\n return;\n }\n\n const openai = new OpenAI();\n const aiStream = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: HEATMAP_PROMPT },\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text:\n `Based on the following analysis, generate the heatmap: ${analysisContext}`,\n },\n { type: \"image_url\", image_url: { url: image, detail: \"low\" } },\n ],\n },\n ],\n temperature: 0.1,\n stream: true,\n });\n\n let buffer = \"\";\n for await (const part of aiStream) {\n buffer += part.choices[0]?.delta?.content || \"\";\n let newlineIndex;\n while ((newlineIndex = buffer.indexOf(\"\\n\")) !== -1) {\n const line = buffer.slice(0, newlineIndex);\n buffer = buffer.slice(newlineIndex + 1);\n if (line.trim()) await sse.write(line + \"\\n\");\n }\n }\n if (buffer.trim()) await sse.write(buffer + \"\\n\");\n } catch (error) {\n console.error(\"Heatmap Stream Error:\", error);\n await sse.write(\n JSON.stringify({ type: \"error\", message: error.message }) + \"\\n\",\n );\n } finally {\n clearInterval(keepAlive);\n }\n });\n});\n\nexport default app.fetch;","createdAt",["D",1754696624155],"branchVersion","branchId","84e5b66e-74a5-11f0-b763-0224a6c84d84","id","84e1847c-74a5-11f0-b763-0224a6c84d84","Mold",["D",1754691469591],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","imageUrl","2c50f100-32ef-11f0-b519-ff88f6bc2fe6","pinnedFileId","pinPreview",true,"pinMigrated","screenshotVersion","likeCount",0,"orgId","337daaab-8c3d-4b0e-843a-d7fe5ed4748b","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_99":98,"_106":102},"org",{"_99":98,"_100":-5,"_101":102,"_18":54,"_103":104,"_105":25},"branch",{"_13":84,"_32":85,"_43":42,"_86":44,"_87":88,"_89":90,"_91":50,"_92":93,"_94":95,"_96":45,"_97":98},"isOwned",23,"main","projectId","forkedBranchId","4332ee8c-726e-11f0-a8a6-0224a6c84d84","forkedBranchVersion",4,"forkedProjectPrivacy","forkedProjectId","4332a148-726e-11f0-a8a6-0224a6c84d84","forkedProjectOrgId","0abcaa5c-2531-4e34-967b-6f68bd9904a8","projectName","userHandle","join","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzJ4RElTYm91QXRtUWhBeEdMM1RYYUVmMVVwYSJ9","tier","free","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_43":44,"_32":45,"_38":164,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":165,"_79":166,"_81":167,"_83":25},"tree",[159],"files",[159],"counts",{"_148":61,"_149":150,"_151":61,"_152":153,"_154":155},"contents",[139],"upstreamProject",{"_43":93,"_32":45,"_52":-5,"_53":-5,"_49":50,"_77":137},"upstreamBranch",{"_43":88,"_38":131,"_132":133,"_32":85,"_86":93,"_13":90,"_87":-5,"_89":-5,"_134":135,"_47":-5,"_83":25,"_49":50,"_97":136,"_62":95,"_96":45},"emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},["D",1754447834780],"updatedAt",["D",1754447878274],"lockfileKey","1754447878540-esm.town_v_real_Mold@4-main","real",{"_99":136,"_106":138},"https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzJ6Rng5NEh2b3dPMmVwbThwVm1keXA3aTk5bCJ9",{"_43":140,"_32":28,"_30":31,"_141":-5,"_41":42,"_40":14,"_142":25,"_38":143,"_13":144,"_33":34,"_35":37,"_24":25,"_145":146,"_27":147},"853a5514-74b1-11f0-83cd-0224a6c84d84","parentId","latest",["D",1754696624155],17,"label","defects",[28],"pulls","branches",1,"forks","envVars",{"_156":61,"_158":61},"connections",{"_156":61,"_157":61},"count","unfilledCount","emptyCount",{"_43":140,"_32":28,"_30":31,"_141":-5,"_41":42,"_40":14,"_142":25,"_38":160,"_13":144,"_33":34,"_35":37,"_24":25,"_145":146,"_161":162,"_27":163},["D",1754696624155],"children",[],[28],["D",1754691469591],{"_99":98,"_106":102},{"_99":98,"_100":-5,"_101":102,"_18":54,"_103":104,"_105":25},{"_13":84,"_32":85,"_43":42,"_86":44,"_87":88,"_89":90,"_91":50,"_92":93,"_94":95,"_96":45,"_97":98},"ctx",{"_77":-5,"_128":201,"_202":203,"_204":205,"_206":25,"_207":25,"_208":25,"_209":57,"_210":-5,"_211":-7,"_212":-7,"_213":61,"_214":25,"_215":25,"_216":-5,"_217":25,"_218":25,"_219":25,"_220":-5},"ENV",{"_172":173,"_174":175,"_176":85,"_177":178,"_179":180,"_181":182,"_183":184,"_185":186,"_187":188,"_189":190,"_191":192,"_193":194,"_195":196,"_197":198,"_199":200},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","71c1b20804b3232a7e513be260cfffc1932a9e97","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v18