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",12,"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","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- AI KNOWLEDGE BASE & BEHAVIORAL GUIDELINES ---\n\nconst AI_SYSTEM_PROMPT = `\nYou are an advanced AI Quality Control Analyst. Your function is to act as a **Defect Probability Mapper**. You will analyze an image of an injection molded part and generate a two-part report: \n1. A high-level analysis of the defects present.\n2. A heatmap grid representing the probability of a defect at each location on the part.\n\n--- KNOWLEDGE BASE START ---\n\n# PART 1: DEFECT IDENTIFICATION\n(This section provides the knowledge for your high-level analysis.)\n\n### 1. Sink Marks\n* **Definition**: Depressions on a part's surface caused by internal material shrinkage.\n* **Potential Causes**: Excessively thick sections; Insufficient cooling time; Low cavity pressure.\n\n### 2. Knit Lines (Weld Lines)\n* **Definition**: A visible line where two molten plastic fronts failed to fuse completely.\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; Excessive injection pressure.\n\n### 4. Splay (Silver Streaks)\n* **Definition**: A silvery spray pattern on the part surface.\n* **Potential Causes**: Moisture in the resin; Trapped gasses; Thermal degradation.\n\n### 5. Burn Marks\n* **Definition**: Black or brown scorch marks on the part from super-heated trapped air.\n* **Potential Causes**: Trapped air; Excessive injection speed.\n\n---\n\n# PART 2: HEATMAP LOCALIZATION & OUTPUT FORMAT\n\n## Your Task & Response Format\nYou MUST respond ONLY with a single, minified JSON object. Adherence to this structure is critical.\nThe root object must have two keys: \"defectAnalysis\" and \"heatmapGrid\".\n\n### 1. \"defectAnalysis\"\nThe value must be an array of objects, where each object represents a type of defect you identified. Each object must have \"defectName\", \"definition\", and \"potentialCauses\".\n\n### 2. \"heatmapGrid\"\nThe value must be a **flat, 1D array containing exactly 256 numbers**. This represents a 16x16 grid.\n- Each number in the array must be a float from **0.0** (no defect) to **1.0** (high confidence of a defect).\n- The array is ordered from top-left to bottom-right, row by row.\n\n## Example Response\n{\n \"defectAnalysis\": [\n {\n \"defectName\": \"Flash\",\n \"definition\": \"A thin, unwanted layer of excess plastic on the part's edge.\",\n \"potentialCauses\": [\"Insufficient clamp force\", \"Worn mold surfaces\"]\n }\n ],\n \"heatmapGrid\": [0.0, 0.0, 0.1, ..., 0.2, 0.1, 0.0]\n}\n\nIf no defects are visible, respond with {\"defectAnalysis\": [], \"heatmapGrid\": [0.0, 0.0, ..., 0.0]}.\n`;\n\n// --- HTML & FRONTEND ---\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

AI is generating analysis and heatmap...

Analysis Failed

\n\n\n`;\n}\n\n// --- BACKEND LOGIC ---\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n\n if (req.method === \"POST\") {\n try {\n const openai = new OpenAI();\n const { image } = await req.json();\n\n if (!image || !image.startsWith(\"data:image/\")) {\n return new Response(\n JSON.stringify({ error: \"A valid base64 image string is required.\" }),\n {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: AI_SYSTEM_PROMPT },\n {\n role: \"user\",\n content: [{\n type: \"text\",\n text:\n \"Analyze this image and respond strictly with the JSON format defined in the system prompt.\",\n }, {\n type: \"image_url\",\n image_url: { url: image },\n }],\n },\n ],\n max_tokens: 2500,\n temperature: 0.1,\n response_format: { type: \"json_object\" },\n });\n\n const messageContent = completion.choices[0].message.content;\n let parsedContent;\n try {\n parsedContent = JSON.parse(messageContent);\n } catch (e) {\n console.error(\"OpenAI returned non-JSON response:\", messageContent);\n throw new Error(\"AI failed to generate valid JSON.\");\n }\n\n // Validate the new, simpler heatmap structure\n if (\n !parsedContent.defectAnalysis ||\n !Array.isArray(parsedContent.defectAnalysis) ||\n !parsedContent.heatmapGrid ||\n !Array.isArray(parsedContent.heatmapGrid) ||\n parsedContent.heatmapGrid.length !== 256\n ) {\n console.error(\"AI returned improperly structured data:\", parsedContent);\n throw new Error(\n \"AI analysis returned an incomplete or invalid data structure.\",\n );\n }\n\n return new Response(messageContent, {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (error) {\n console.error(\"AI Analyzer Error:\", error);\n return new Response(\n JSON.stringify({\n error: error.message || \"An unexpected server error occurred.\",\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n }\n\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n return new Response(generateHtml(sourceUrl), {\n headers: { \"Content-Type\": \"text/html; charset=utf-8\" },\n });\n}","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- AI KNOWLEDGE BASE & BEHAVIORAL GUIDELINES ---\n\nconst AI_SYSTEM_PROMPT = `\nYou are an advanced AI Quality Control Analyst. Your function is to act as a **Defect Probability Mapper**. You will analyze an image of an injection molded part and generate a two-part report: \n1. A high-level analysis of the defects present.\n2. A heatmap grid representing the probability of a defect at each location on the part.\n\n--- KNOWLEDGE BASE START ---\n\n# PART 1: DEFECT IDENTIFICATION\n(This section provides the knowledge for your high-level analysis.)\n\n### 1. Sink Marks\n* **Definition**: Depressions on a part's surface caused by internal material shrinkage.\n* **Potential Causes**: Excessively thick sections; Insufficient cooling time; Low cavity pressure.\n\n### 2. Knit Lines (Weld Lines)\n* **Definition**: A visible line where two molten plastic fronts failed to fuse completely.\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; Excessive injection pressure.\n\n### 4. Splay (Silver Streaks)\n* **Definition**: A silvery spray pattern on the part surface.\n* **Potential Causes**: Moisture in the resin; Trapped gasses; Thermal degradation.\n\n### 5. Burn Marks\n* **Definition**: Black or brown scorch marks on the part from super-heated trapped air.\n* **Potential Causes**: Trapped air; Excessive injection speed.\n\n---\n\n# PART 2: HEATMAP LOCALIZATION & OUTPUT FORMAT\n\n## Your Task & Response Format\nYou MUST respond ONLY with a single, minified JSON object. Adherence to this structure is critical.\nThe root object must have two keys: \"defectAnalysis\" and \"heatmapGrid\".\n\n### 1. \"defectAnalysis\"\nThe value must be an array of objects, where each object represents a type of defect you identified. Each object must have \"defectName\", \"definition\", and \"potentialCauses\".\n\n### 2. \"heatmapGrid\"\nThe value must be a **flat, 1D array containing exactly 256 numbers**. This represents a 16x16 grid.\n- Each number in the array must be a float from **0.0** (no defect) to **1.0** (high confidence of a defect).\n- The array is ordered from top-left to bottom-right, row by row.\n\n## Example Response\n{\n \"defectAnalysis\": [\n {\n \"defectName\": \"Flash\",\n \"definition\": \"A thin, unwanted layer of excess plastic on the part's edge.\",\n \"potentialCauses\": [\"Insufficient clamp force\", \"Worn mold surfaces\"]\n }\n ],\n \"heatmapGrid\": [0.0, 0.0, 0.1, ..., 0.2, 0.1, 0.0]\n}\n\nIf no defects are visible, respond with {\"defectAnalysis\": [], \"heatmapGrid\": [0.0, 0.0, ..., 0.0]}.\n`;\n\n// --- HTML & FRONTEND ---\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

AI is generating analysis and heatmap...

Analysis Failed

\n\n\n`;\n}\n\n// --- BACKEND LOGIC ---\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n\n if (req.method === \"POST\") {\n try {\n const openai = new OpenAI();\n const { image } = await req.json();\n\n if (!image || !image.startsWith(\"data:image/\")) {\n return new Response(\n JSON.stringify({ error: \"A valid base64 image string is required.\" }),\n {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: AI_SYSTEM_PROMPT },\n {\n role: \"user\",\n content: [{\n type: \"text\",\n text:\n \"Analyze this image and respond strictly with the JSON format defined in the system prompt.\",\n }, {\n type: \"image_url\",\n image_url: { url: image },\n }],\n },\n ],\n temperature: 0.1,\n response_format: { type: \"json_object\" },\n });\n\n const messageContent = completion.choices[0].message.content;\n let parsedContent;\n try {\n parsedContent = JSON.parse(messageContent);\n } catch (e) {\n console.error(\"OpenAI returned non-JSON response:\", messageContent);\n throw new Error(\"AI failed to generate valid JSON.\");\n }\n\n // Validate the new, simpler heatmap structure\n if (\n !parsedContent.defectAnalysis ||\n !Array.isArray(parsedContent.defectAnalysis) ||\n !parsedContent.heatmapGrid ||\n !Array.isArray(parsedContent.heatmapGrid) ||\n parsedContent.heatmapGrid.length !== 256\n ) {\n console.error(\"AI returned improperly structured data:\", parsedContent);\n throw new Error(\n \"AI analysis returned an incomplete or invalid data structure.\",\n );\n }\n\n return new Response(messageContent, {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (error) {\n console.error(\"AI Analyzer Error:\", error);\n return new Response(\n JSON.stringify({\n error: error.message || \"An unexpected server error occurred.\",\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n }\n\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n return new Response(generateHtml(sourceUrl), {\n headers: { \"Content-Type\": \"text/html; 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