AI Defect Area Analyzer

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\n \n

Awaiting Analysis...

\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. Main streaming API endpoint\napp.post(\"/api/analyze\", (c: Context) => {\n return stream(c, async (sse) => {\n const keepAlive = setInterval(() => {\n sse.write(\": keep-alive\\n\");\n }, 15000);\n sse.onAbort(() => {\n clearInterval(keepAlive);\n });\n\n try {\n const { image } = await c.req.json();\n if (!image || !image.startsWith(\"data:image/\")) {\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: ANALYSIS_PROMPT },\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text:\n \"Analyze the defects in this image and report their locations using the 3x3 grid system.\",\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(\"AI Stream Error:\", error);\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 version asks the user for additional context to provide a more comprehensive report.\n\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Hono } from \"npm:hono@4.4.12\";\nimport type { Context } from \"npm:hono@4.4.12\";\n\n// --- AI PROMPT FOR COMPREHENSIVE DEFECT REPORT ---\nconst ANALYSIS_PROMPT = `\nYou are an expert AI Quality Control Analyst and plastics molding technician. Your function is to analyze an image of a plastic part along with user-provided process parameters to generate a comprehensive report on all visible manufacturing defects.\n\n# Core Task\nYou will be given an image and a set of user-provided context (like material type, mold temperature, etc.). You MUST use this context to refine your analysis. Your primary value is in connecting the visual defect to the provided parameters to give a highly relevant diagnosis.\n\n**Example of Expert Logic:**\n- If the user specifies the material is \"Polycarbonate (PC)\" and you see splay marks, your top recommendation MUST be about pre-drying the material, as PC is highly hygroscopic.\n- If the user reports a low mold temperature and you see knit lines, you MUST connect these two facts in your analysis.\n\n# Output Format\nYou MUST respond with a single, minified JSON object.\nThe root object must have one key: \"defects\".\nThe value of \"defects\" MUST be an array of JSON objects. Each object represents a single detected defect.\nIf no defects are found, return an empty array.\n\nEach defect object must contain:\n1. \"defectName\": The formal name of the defect.\n2. \"definition\": A concise, technical definition.\n3. \"potentialCauses\": An array of strings listing the most likely causes, prioritized based on the user's context.\n4. \"recommendedResolutions\": An array of strings listing actionable steps to fix the defect, prioritized based on the user's context.\n\n# Knowledge Base\n\n### 1. Sink Marks\n* **Definition**: \"Depressions or craters on a part's surface caused by internal material shrinkage as it cools.\"\n* **Potential Causes**: [\"Excessively thick wall/rib sections\", \"Insufficient holding pressure or time\", \"Melt or mold temperature is too high\", \"Inadequate cooling time\"]\n* **Recommended Resolutions**: [\"Reduce the thickness of the thickest sections\", \"Increase holding pressure and/or time\", \"Lower mold/melt temperature\", \"Increase cooling time before ejection\"]\n\n### 2. Knit Lines (Weld Lines)\n* **Definition**: \"A visible line or seam where two or more fronts of molten plastic have met and failed to fuse completely.\"\n* **Potential Causes**: [\"Part geometry has holes or obstructions\", \"Material or mold temperature is too low\", \"Low injection speed or pressure\"]\n* **Recommended Resolutions**: [\"Increase material and/or mold temperature\", \"Increase injection speed and/or pressure\", \"Relocate the gate to alter flow paths\", \"Add an overflow well at the weld location\"]\n\n### 3. Flash\n* **Definition**: \"A thin, unwanted layer of excess plastic that forms on the edge of a part, typically at the parting line.\"\n* **Potential Causes**: [\"Insufficient clamp force to hold the mold shut\", \"Worn or damaged mold parting line surfaces\", \"Excessive injection pressure or shot size\"]\n* **Recommended Resolutions**: [\"Increase clamp tonnage\", \"Repair, clean, or re-seat the mold\", \"Decrease injection pressure, shot size, or melt temperature\"]\n\n### 4. Splay (Silver Streaks)\n* **Definition**: \"A silvery or streaky spray pattern on the surface of the part.\"\n* **Potential Causes**: [\"Moisture in the resin (improper drying)\", \"Trapped gasses being dragged across the surface\", \"Thermal degradation of the material due to excessive heat\"]\n* **Recommended Resolutions**: [\"Properly dry the resin according to manufacturer specifications\", \"Lower the injection speed\", \"Reduce melt temperature and check for hot spots in the barrel\", \"Ensure mold vents are clear and adequate\"]\n\n### 5. Burn Marks\n* **Definition**: \"Black or brown discolored patches on the part, often at the furthest points from the gate or in blind corners.\"\n* **Potential Causes**: [\"Trapped air becoming super-compressed and heating up, scorching the plastic\", \"Excessive injection speed\", \"Blocked or inadequate mold vents\"]\n* **Recommended Resolutions**: [\"Lower the injection speed to reduce shear heat\", \"Improve or add venting in the affected areas of the mold\", \"Reduce clamp tonnage slightly to aid venting\"]\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 Report Generator\n \n \n \n\n\n\n

\n \n

\n\n\n`;\n}\n\n// --- HONO APPLICATION SETUP ---\n\nconst app = new Hono();\n\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// MODIFIED: This is now a simple, non-streaming endpoint\napp.post(\"/api/analyze\", async (c: Context) => {\n try {\n const { image, context } = await c.req.json();\n if (!image) {\n return c.json({ error: \"Image is required.\" }, 400);\n }\n\n // NEW: Construct the context string to send to the AI\n const contextString = \\`\n ### User-Provided Process Parameters\n - Material Type: \\${context.material || 'Not specified'}\n - Mold Temperature: \\${context.moldTemp || 'Not specified'}\n - Injection Pressure: \\${context.injectionPressure || 'Not specified'}\n \\`.trim();\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 { \n type: \"text\", \n text: \\`Analyze the defects in this image using the following context:\\\\n\\${contextString}\\`\n },\n { \n type: \"image_url\", \n image_url: { url: image, detail: \"low\" } \n }\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\n } catch(error) {\n console.error(\"Analysis Error:\", error);\n return c.json({ error: \"Failed to get analysis from AI.\" }, 500);\n }\n});\n\nexport default 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