AI Defect Report Generator

\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: ${context.injectionPressure || \"Not specified\"}\n `.trim();\n\n const openai = new OpenAI();\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: ANALYSIS_PROMPT },\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text:\n `Analyze the defects in this image using the following context:\\\\n${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 } 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 app.fetch;","label","defects","children",[70],"contentHash","7d201c4f38d92788c0ffeeb273b7a92d898df47559ed5090858defba81c674df","5d20b932-726e-11f0-b2d5-0224a6c84d84","4334dd00-726e-11f0-a8a6-0224a6c84d84",["D",1754447878274],3,"// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- AI KNOWLEDGE BASE & BEHAVIORAL GUIDELINES ---\n\nconst INSPECTION_GUIDE_KNOWLEDGE_BASE = `\n# Procedural Guide for Inspecting Injection Molded Parts\n\n## Objective\nThis guide provides a structured approach to conducting inspections on injection molded parts, detailing the necessary tools, equipment, and safety protocols to ensure an effective and safe inspection process.\n\n## 1. Overview of the Inspection Process\nThe inspection of injection molded parts is vital to maintaining quality standards. The procedure encompasses planning, execution, and reporting stages to ensure that parts meet defined specifications. Key Stages: Preparation, Inspection, Documentation.\n\n## 2. Required Tools and Equipment\n- **Measuring Tools**: Calipers, Micrometers, Coordinate Measuring Machine (CMM).\n- **Surface Inspection Tools**: Surface Roughness Tester, Magnifying Glass, Light Source.\n- **Defect Identification**: Inspection Checklist, Cameras/Smartphones, Computer/Tablet.\n- **Safety Equipment**: Safety Glasses, Gloves, Protective Clothing.\n\n## 3. Safety Protocols\n- **PPE**: Always wear safety glasses and use gloves when necessary.\n- **Work Environment**: Keep the area well-lit and free from clutter.\n- **Tool Safety**: Regularly inspect tools for calibration and store them properly.\n\n## 4. Detailed Inspection Steps\n### Step 1: Preparation\n1. Gather all necessary tools.\n2. Review inspection criteria.\n3. Conduct a safety briefing.\n\n### Step 2: Dimensional Inspection\n- **Procedure**: Utilize calipers and micrometers to measure parts against specified tolerances. Document all measurements.\n\n### Step 3: Surface Finish Inspection\n- **Procedure**: Use a surface roughness tester and visual inspection to check for blemishes and color consistency.\n\n### Step 4: Defect Identification\n- **Procedure**: Systematically check for common defects (e.g., short shots, warpage, flash, sink marks, burn marks, flow lines) using a predefined checklist. For each defect found, record the description, potential causes, and implications. Potential causes for defects often relate to material temperature, injection pressure, cooling time, and mold design.\n\n### Step 5: Reporting\n- Compile all data into a standardized inspection report.\n\n## 5. Conclusion\nFollowing this guide ensures thorough and consistent inspection, maintaining quality standards and safeguarding inspectors.\n`;\n\nconst GENERATION_STYLE_GUIDE = `\nAs the AI Quality Control Analyst, you must adopt the persona of an experienced manufacturing engineer. Your analysis must be formal, technical, and precise. Base your conclusions exclusively on the visual evidence in the provided image and the technical knowledge contained within the INSPECTION_GUIDE_KNOWLEDGE_BASE. Avoid speculative or conversational language. The objective is to produce an analysis that is immediately actionable for a quality control team.\n`;\n\nconst AI_SYSTEM_PROMPT = `\nYou are an expert AI Quality Control Analyst specializing in injection molded plastics. Your knowledge base is strictly limited to the provided guide. Your task is to analyze an image of a part, identify the most prominent manufacturing defect, and provide a structured report.\n\nYou MUST respond ONLY with a single, minified JSON object.\nThe root object must have these four keys: \"defectName\", \"definition\", \"potentialCauses\", and \"recommendedActions\".\n- \"defectName\": The formal name of the defect (e.g., \"Warpage\", \"Flash\", \"Sink Mark\").\n- \"definition\": A concise, technical definition of the defect.\n- \"potentialCauses\": An array of 2-4 likely causes based on the provided guide's principles.\n- \"recommendedActions\": An array of 2-3 procedural steps an inspector should take next, referencing the guide.\n\nIf the image does not clearly show a defect or is not an injection molded part, respond with a JSON object: {\"error\": \"A specific reason why analysis cannot be completed.\"}.\n${GENERATION_STYLE_GUIDE}\n`;\n\n// --- HTML & FRONTEND ---\n\nfunction generateHtml(sourceUrl: string) {\n // Ensure the base URL has a trailing slash for correct relative path resolution by the browser.\n const baseUrl = sourceUrl.endsWith(\"/\") ? sourceUrl : `${sourceUrl}/`;\n\n return `\n\n\n \n \n AI Defect Analyzer\n \n \n \n\n\n\n\n

\n \n\n

\n \n

\n\n

Analyzing...

The AI is examining the part. Please wait.

Analysis Failed

Definition

Potential Causes

Recommended Actions

\n\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(JSON.stringify({ error: \"A valid base64 image string is required.\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content: AI_SYSTEM_PROMPT.replace(\"INSPECTION_GUIDE_KNOWLEDGE_BASE\", INSPECTION_GUIDE_KNOWLEDGE_BASE),\n },\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text: \"Analyze the provided image and respond according to the system prompt's strict JSON format.\",\n },\n {\n type: \"image_url\",\n image_url: {\n url: image,\n detail: \"low\",\n },\n },\n ],\n },\n ],\n max_tokens: 500,\n temperature: 0.2,\n response_format: { type: \"json_object\" },\n });\n\n const messageContent = completion.choices[0].message.content;\n\n // AI responses are untrusted input; validate the structure before returning.\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 a valid analysis structure.\");\n }\n\n if (parsedContent.error) {\n return new Response(JSON.stringify({ error: parsedContent.error }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n if (\n !parsedContent.defectName || !parsedContent.definition || !Array.isArray(parsedContent.potentialCauses)\n || !Array.isArray(parsedContent.recommendedActions)\n ) {\n console.error(\"OpenAI returned improperly structured JSON:\", parsedContent);\n throw new Error(\"AI analysis returned an incomplete data structure.\");\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(JSON.stringify({ error: error.message || \"An unexpected server error occurred.\" }), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n\n // Serve the HTML for GET requests\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n return new Response(generateHtml(sourceUrl), { headers: { \"Content-Type\": \"text/html; 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Mergejoin/mainintoreal/main

Hmain.tsx+255-417