AI Defect Report Generator

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\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;","deleted","label","defects",[99],"permalink","https://www.val.town/x/join/Mold/code/main.tsx","extension","actionData","errors"]