feat: repo scanner

app/api/analyze/route.ts

@@ -0,0 +1,77 @@
+import { NextRequest, NextResponse } from 'next/server'
+import { readFileSync } from 'fs'
+import { join } from 'path'
+
+const promptTemplate = readFileSync(join(process.cwd(), 'app/api/analyze/prompt.txt'), 'utf-8')
+
+// In-memory cache: persists for the lifetime of the server process
+const cache = new Map<string, { data: any; timestamp: number }>()
+const CACHE_TTL = 1000 * 60 * 60 * 24 // 24 hours
+
+export async function POST(req: NextRequest) {
+  try {
+    const { owner, repo, files, stack, dependencies, sensitiveFiles, configFiles } = await req.json()
+    const baseUrl = process.env.SHARED_LLM_BASE_URL
+    const apiKey = process.env.SHARED_LLM_API_KEY
+
+    if (!baseUrl || !apiKey) {
+      return NextResponse.json({ error: 'LLM service not configured' }, { status: 500 })
+    }
+
+    // Check cache
+    const cacheKey = `${owner}/${repo}`.toLowerCase()
+    const cached = cache.get(cacheKey)
+    if (cached && Date.now() - cached.timestamp < CACHE_TTL) {
+      return NextResponse.json(cached.data)
+    }
+
+    const prompt = promptTemplate
+      .replace('{{owner}}', owner)
+      .replace('{{repo}}', repo)
+      .replace('{{files}}', files.slice(0, 80).join(', '))
+      .replace('{{stack}}', stack.join(', ') || 'Unknown')
+      .replace('{{dependencies}}', Object.keys(dependencies || {}).slice(0, 40).join(', ') || 'None detected')
+      .replace('{{sensitiveFiles}}', sensitiveFiles.join(', ') || 'None')
+      .replace('{{configFiles}}', configFiles.join(', ') || 'None')
+
+    let endpoint = baseUrl.replace(/\/+$/, '')
+    endpoint = endpoint.replace(/\/v1$/, '')
+    endpoint += '/v1/chat/completions'
+
+    const llmRes = await fetch(endpoint, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${apiKey}` },
+      body: JSON.stringify({
+        model: process.env.SHARED_LLM_MODEL || 'Kimi-K2.5-sandbox',
+        messages: [{ role: 'user', content: prompt }],
+        max_tokens: 8000,
+        temperature: 0.7,
+      }),
+    })
+
+    if (!llmRes.ok) {
+      console.error('LLM error:', await llmRes.text())
+      return NextResponse.json({ error: 'Failed to generate report' }, { status: 500 })
+    }
+
+    const data = await llmRes.json()
+    const msg = data.choices?.[0]?.message
+    const content = msg?.content || msg?.reasoning_content || ''
+    if (!content) {
+      console.error('LLM returned empty content:', JSON.stringify(data).slice(0, 500))
+      return NextResponse.json({ error: 'LLM returned empty response' }, { status: 500 })
+    }
+    const jsonMatch = content.match(/\{[\s\S]*\}/)
+    if (!jsonMatch) {
+      console.error('No JSON found in LLM response:', content.slice(0, 500))
+      return NextResponse.json({ error: 'Failed to parse report' }, { status: 500 })
+    }
+
+    const report = JSON.parse(jsonMatch[0])
+    cache.set(cacheKey, { data: report, timestamp: Date.now() })
+    return NextResponse.json(report)
+  } catch (e: any) {
+    console.error('Analyze error:', e)
+    return NextResponse.json({ error: e.message || 'Analysis failed' }, { status: 500 })
+  }
+}