GitHub Issues Analyzer

\n \n \n

🔍 Analyzing repository...

This may take a few minutes for large repositories.

\n\n \n\n",[],[221],{"_103":230,"_95":231,"_154":232,"_156":-5,"_157":139,"_158":233,"_160":212,"_105":234,"_99":235,"_162":236,"_164":237,"_165":22,"_166":-5,"_167":238,"_98":239},"2005b5ee-634a-11f0-9a84-0224a6c84d84","main.ts","29173144-61f6-11f0-b506-0224a6c84d84",140,["D",1752783046224],26,"http","import { Hono } from 'npm:hono'\nimport { serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\"\nimport { analyzeRepository, formatAsMarkdown } from './utils.ts'\n\nconst app = new Hono()\n\napp.get('/', async (c) => {\n return serveFile('./index.html', import.meta.url)\n})\n\napp.post('/analyze-issues', async (c) => {\n const { repo } = await c.req.json()\n return c.json(await analyzeRepository(repo))\n})\n\napp.post('/analyze-issues/markdown', async (c) => {\n const { repo } = await c.req.json()\n const result = await analyzeRepository(repo)\n return c.text(formatAsMarkdown(result), 200, { 'Content-Type': 'text/plain; charset=utf-8' })\n})\n\napp.get('/analyze/:owner/:repo/markdown', async (c) => {\n const fullRepo = `${c.req.param('owner')}/${c.req.param('repo')}`\n const result = await analyzeRepository(fullRepo)\n return c.text(formatAsMarkdown(result), 200, { 'Content-Type': 'text/plain; charset=utf-8' })\n})\n\nexport default app.fetch",[],[231],{"_103":241,"_95":242,"_154":243,"_156":-5,"_157":139,"_158":244,"_160":22,"_105":245,"_99":246,"_162":215,"_164":247,"_165":22,"_166":-5,"_167":248,"_98":249},"fd5f277c-634a-11f0-abd7-0224a6c84d84","openai.ts","291c39dc-61f6-11f0-b000-0224a6c84d84",142,["D",1752783417765],25,"import OpenAI from 'npm:openai'\n\nconst openai = new OpenAI({\n apiKey: Deno.env.get('OPENAI_API_KEY')\n})\n\nexport async function categorizeIssues(issues: any[]) {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [{ role: \"user\", content: `Analyze these GitHub issues and categorize them into a two-level system:\n\n1. First identify 3-6 main topic areas (e.g., Authentication, Performance, UI/UX, API, etc.)\n2. Within each main topic, categorize issues into exactly these 4 subcategories:\n - \"Big ticket\": Major features, architectural changes, complex implementations\n - \"Bugs\": Bug fixes, errors, broken functionality\n - \"Enhancements\": Improvements to existing features, optimizations\n - \"Nice to haves\": Optional features, quality of life improvements\n\nIssues:\n${issues.map(issue => `#${issue.number}: ${issue.title}\\nBody: ${issue.body}\\n---`).join('\\n')}\n\nReturn a JSON object with:\n- topics: array of main topic objects, each containing:\n - name: descriptive main topic name (e.g., \"Authentication\", \"Performance\")\n - subcategories: object with exactly these 4 keys:\n - \"Big ticket\": array of issue numbers\n - \"Bugs\": array of issue numbers \n - \"Enhancements\": array of issue numbers\n - \"Nice to haves\": array of issue numbers\n\nEvery issue should be assigned to exactly one main topic and one subcategory within that topic.` }],\n response_format: { type: \"json_object\" },\n temperature: 0.3,\n max_tokens: 4000\n })\n\n const aiResponse = completion.choices[0].message.content\n if (!aiResponse) throw new Error('OpenAI returned empty response')\n \n try {\n return JSON.parse(aiResponse)\n } catch (error) {\n console.error('Failed to parse OpenAI response:', aiResponse)\n throw new Error(`Invalid JSON response from OpenAI: ${error.message}`)\n }\n}",[],[242],{"_103":251,"_95":252,"_154":253,"_156":-5,"_157":139,"_158":254,"_160":22,"_105":255,"_99":194,"_162":215,"_164":256,"_165":22,"_166":-5,"_167":257,"_98":258},"fd590a7c-634a-11f0-8a1b-0224a6c84d84","utils.ts","1fd9649e-634a-11f0-ba73-0224a6c84d84",141,["D",1752783417758],"import { getAllIssues } from './github.ts'\n\ninterface Issue {\n number: number\n title: string\n body: string\n}\n\ninterface Subcategory {\n issues: Issue[]\n count: number\n}\n\ninterface Topic {\n name: string\n subcategories: {\n \"Big ticket\": Subcategory\n \"Bugs\": Subcategory\n \"Enhancements\": Subcategory\n \"Nice to haves\": Subcategory\n }\n}\n\nconst createBatches = (array: T[], size: number): T[][] => \n Array.from({ length: Math.ceil(array.length / size) }, (_, i) => \n array.slice(i * size, i * size + size))\n\nconst mergeTopicResults = (allBatchTopics: any[]) => {\n const topicMap = new Map()\n \n for (const { topics } of allBatchTopics) {\n for (const topic of topics) {\n const existing = topicMap.get(topic.name)\n \n if (existing) {\n Object.keys(topic.subcategories).forEach(key => {\n existing.subcategories[key].push(...topic.subcategories[key])\n })\n } else {\n topicMap.set(topic.name, {\n name: topic.name,\n subcategories: Object.fromEntries(\n Object.entries(topic.subcategories).map(([key, values]) => [key, [...values as any[]]])\n )\n })\n }\n }\n }\n \n return { topics: Array.from(topicMap.values()) }\n}\n\nasync function categorizeBatch(issues: any[]) {\n const processedIssues = issues.map(issue => ({\n number: issue.number,\n title: issue.title,\n body: issue.body?.substring(0, 500) || ''\n }))\n\n const { categorizeIssues: categorizeWithAI } = await import('./openai.ts')\n return categorizeWithAI(processedIssues)\n}\n\nasync function categorizeIssues(issues: any[], batchSize = 75) {\n if (issues.length === 0) return { topics: [] }\n if (issues.length <= batchSize) return categorizeBatch(issues)\n \n const batches = createBatches(issues, batchSize)\n console.log(`Processing ${issues.length} issues in ${batches.length} batches`)\n \n const allBatchTopics = []\n for (let i = 0; i < batches.length; i++) {\n console.log(`Processing batch ${i + 1}/${batches.length} (${batches[i].length} issues)`)\n \n allBatchTopics.push(await categorizeBatch(batches[i]))\n \n if (i < batches.length - 1) {\n await new Promise(resolve => setTimeout(resolve, 1000))\n }\n }\n \n return mergeTopicResults(allBatchTopics)\n}\n\nexport async function extractTopics(issues: Issue[]): Promise {\n if (issues.length === 0) return []\n\n const aiCategorization = await categorizeIssues(issues)\n \n const topics: Topic[] = aiCategorization.topics.map((topic: any) => {\n const subcategories = Object.fromEntries(\n [\"Big ticket\", \"Bugs\", \"Enhancements\", \"Nice to haves\"].map(categoryName => {\n const topicIssues = issues.filter(issue => topic.subcategories[categoryName].includes(issue.number))\n return [categoryName, {\n issues: topicIssues,\n count: topicIssues.length\n }]\n })\n ) as Topic['subcategories']\n \n return {\n name: topic.name,\n subcategories\n }\n })\n\n const totalCount = (topic: Topic) => Object.values(topic.subcategories).reduce((sum, category) => sum + category.count, 0)\n return topics.filter(topic => totalCount(topic) > 0).sort((a, b) => totalCount(b) - totalCount(a))\n}\n\nexport async function analyzeRepository(repo: string) {\n const issues = await getAllIssues(repo)\n const topics = await extractTopics(issues)\n \n return {\n repository: repo,\n totalIssues: issues.length,\n topics: topics.map(topic => ({\n name: topic.name,\n subcategories: Object.fromEntries(\n Object.entries(topic.subcategories).map(([name, category]: [string, any]) => [\n name,\n {\n count: category.count,\n issues: category.issues.map((issue: any) => ({\n number: issue.number,\n title: issue.title\n }))\n }\n ])\n )\n }))\n }\n}\n\nexport function formatAsMarkdown(result: any): string {\n const { repository, topics } = result\n \n return topics.map((topic: any) => {\n let markdown = `# ${topic.name}\\n\\n`\n \n [\"Big ticket\", \"Bugs\", \"Enhancements\", \"Nice to haves\"].forEach(categoryName => {\n const category = topic.subcategories[categoryName]\n if (category.count > 0) {\n markdown += `**${categoryName}**\\n\\n`\n category.issues.forEach((issue: any) => {\n markdown += `* [${issue.title} #${issue.number}](https://github.com/${repository}/issues/${issue.number})\\n`\n })\n markdown += '\\n'\n }\n })\n \n return markdown\n 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charmaine

analyze-github-issues