AI Defect Heatmap Analyzer (Streaming)

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\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. Main API route for streaming AI analysis\napp.post(\"/api/analyze\", (c: Context) => {\n return stream(c, async (sse) => {\n try {\n const { image } = await c.req.json();\n if (!image || !image.startsWith(\"data:image/\")) {\n await sse.write(\n JSON.stringify({\n type: \"error\",\n message: \"A valid base64 image string is required.\",\n }),\n );\n await sse.close();\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: AI_SYSTEM_PROMPT },\n {\n role: \"user\",\n content: [{\n type: \"text\",\n text:\n \"Analyze this image and respond strictly with the line-by-line JSON stream format defined in the system prompt.\",\n }, {\n type: \"image_url\",\n image_url: { url: image },\n }],\n },\n ],\n temperature: 0.1,\n stream: true,\n });\n\n // Use the stream reader to handle line-by-line data\n const reader = aiStream.body.getReader();\n const decoder = new TextDecoder();\n let buffer = \"\";\n\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n\n buffer += decoder.decode(value, { stream: true });\n const lines = buffer.split(\"\\n\");\n buffer = lines.pop(); // Keep the last, possibly incomplete line\n\n for (const line of lines) {\n if (line.trim()) {\n // Extract content from the stream chunk\n const match = line.match(/data: (.*)/);\n if (match) {\n const content = match[1];\n if (content === \"[DONE]\") {\n await sse.write(JSON.stringify({ type: \"done\" }) + \"\\n\");\n } else {\n const chunk = JSON.parse(content);\n const delta = chunk.choices[0]?.delta?.content || \"\";\n if (delta) {\n // Because the AI is prompted to output line-by-line JSON, we just proxy the delta\n // A more robust solution would re-assemble the JSON objects here\n // But for this use case, we assume the AI follows instructions closely.\n // Let's try a simple newline split proxy\n const jsonLines = delta.split(\"\\n\").filter((l) => l.trim());\n for (const jsonLine of jsonLines) {\n await sse.write(jsonLine + \"\\n\");\n }\n }\n }\n }\n }\n }\n }\n // Send any remaining buffer content (though it should ideally be empty)\n if (buffer.trim()) {\n await sse.write(buffer + \"\\n\");\n }\n } catch (error) {\n console.error(\"AI Stream Error:\", error);\n await sse.write(\n JSON.stringify({ type: \"error\", message: error.message }),\n );\n } finally {\n await sse.close();\n }\n });\n});\n\n// --- EXPORT THE VAL ---\nexport default app.fetch;","// This is a Val Town (https://val.town) HTTP Val using the Hono web framework.\n// It serves a Single-Page Application (SPA) and streams AI responses correctly.\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 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 and generate a stream of JSON objects to report your findings.\n\n# Core Rule: Output Format\nThis is your most critical instruction. You MUST generate a sequence of individual JSON objects, ONE PER LINE. Do NOT wrap the entire output in a single JSON object or array. Each line you output must be a complete, parsable JSON object.\n\n## Output Sequence:\n1. **Defect Analysis (First Line)**: Your very first line of output must be a single JSON object containing the high-level analysis.\n - Format: \\`{\"type\": \"analysis\", \"payload\": [{\"defectName\": \"...\", \"definition\": \"...\", \"potentialCauses\": [...]}]}\\`\n - If no defects are found, the payload should be an empty array: \\`{\"type\": \"analysis\", \"payload\": []}\\`\n\n2. **Heatmap Grid (Next 256 Lines)**: Immediately following the analysis, you will 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 of the cell (0-255).\n - \\`P\\` is the float probability of a defect in that cell (0.0 to 1.0).\n\n3. **Done Signal (Final Line)**: After outputting all 256 heatmap cells, your final line of output must be:\n - Format: \\`{\"type\": \"done\"}\\`\n\n# Knowledge Base for 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// --- 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 (Streaming)\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. Main API route for streaming AI analysis\napp.post(\"/api/analyze\", (c: Context) => {\n return stream(c, async (sse) => {\n try {\n const { image } = await c.req.json();\n if (!image || !image.startsWith(\"data:image/\")) {\n await sse.write(\n JSON.stringify({\n type: \"error\",\n message: \"A valid base64 image string is 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: AI_SYSTEM_PROMPT },\n {\n role: \"user\",\n content: [{\n type: \"text\",\n text:\n \"Analyze this image and respond strictly with the line-by-line JSON stream format defined in the system prompt.\",\n }, {\n type: \"image_url\",\n image_url: { url: image },\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 // Process buffer line by line\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()) {\n await sse.write(line + \"\\n\");\n }\n }\n }\n // Process any remaining content in the buffer after the loop\n if (buffer.trim()) {\n await sse.write(buffer + \"\\n\");\n }\n } catch (error) {\n console.error(\"AI Stream Error:\", error);\n await sse.write(\n JSON.stringify({ type: \"error\", message: error.message }) + \"\\n\",\n );\n }\n });\n});\n\n// --- EXPORT THE VAL ---\nexport default 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Diff · v14