feat: AI image classification and dynamic fallback assignment

scripts/classify-images.ts

@@ -0,0 +1,135 @@
+import fs from 'fs/promises';
+import path from 'path';
+
+const OLLAMA_API = 'http://127.0.0.1:11434/api/generate';
+const MODEL = "llava:latest";
+
+const SYSTEM_PROMPT = `Du bist ein KI-Assistent zur Klassifizierung von Bildern für ein Infrastruktur-Unternehmen. 
+Bitte analysiere das Bild und antworte AUSSCHLIESSLICH mit einem validen JSON-Objekt, das folgendem Schema entspricht:
+{
+  "category": "pv" | "wind" | "fiber" | "power" | "battery" | "unknown",
+  "environment": "freies_feld" | "urban" | "wald" | "indoor" | "unknown",
+  "quality_score": number, // 1-10, wie gut eignet sich das Bild als Hero/Titelbild? (Schärfe, Ästhetik, Belichtung)
+  "description": "string" // Kurze deutsche Beschreibung, max 10 Wörter
+}
+
+Erklärung der Kategorien:
+- "pv": Photovoltaik-Anlagen, Solarparks, Solarmodule
+- "wind": Windräder, Windparks
+- "fiber": Kabeltiefbau, Spülbohrtechnik, Bagger bei Kabelverlegung, Glasfaser
+- "power": Umspannwerke, Hochspannungstrassen, Strommasten
+- "battery": Batterie-Speichersysteme (BESS), Container-Speicher
+
+Erklärung der Umgebung:
+- "freies_feld": Offenes Feld, Wiese, Natur (vom Kunden stark bevorzugt für PV und Wind!)
+- "urban": Stadt, Straße, Siedlung
+- "wald": Im Wald, viele Bäume nah dran
+- "indoor": Innenräume
+
+WICHTIG: Gib NUR JSON zurück. Keinen Markdown-Code-Block. Nichts anderes.
+`;
+
+interface ImageMeta {
+  file: string;
+  category: 'pv' | 'wind' | 'fiber' | 'power' | 'battery' | 'unknown';
+  environment: 'freies_feld' | 'urban' | 'wald' | 'indoor' | 'unknown';
+  quality_score: number;
+  description: string;
+}
+
+async function classifyImage(filePath: string): Promise<ImageMeta | null> {
+  try {
+    const ext = path.extname(filePath).toLowerCase();
+    if (!['.jpg', '.jpeg', '.png', '.webp', '.avif'].includes(ext)) {
+      return null;
+    }
+
+    const imageBuffer = await fs.readFile(filePath);
+    const base64Image = imageBuffer.toString('base64');
+
+    console.log(`Analyzing ${path.basename(filePath)}...`);
+
+    const response = await fetch(OLLAMA_API, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({
+        model: MODEL,
+        prompt: "Bitte klassifiziere dieses Bild gemäß den Anweisungen und antworte nur mit JSON.",
+        system: SYSTEM_PROMPT,
+        images: [base64Image],
+        stream: false,
+        format: "json",
+        options: {
+          temperature: 0.1,
+          num_predict: 200,
+        }
+      })
+    });
+
+    if (!response.ok) {
+      console.error(`Ollama error for ${filePath}: ${response.status} ${response.statusText}`);
+      return null;
+    }
+
+    const result = await response.json();
+    let responseText = result.response.trim();
+    
+    // Fallback if model wraps in code blocks
+    if (responseText.startsWith('\`\`\`json')) {
+      responseText = responseText.replace(/^\`\`\`json/m, '').replace(/\`\`\`$/m, '').trim();
+    }
+
+    const parsed = JSON.parse(responseText);
+    
+    return {
+      file: '/assets/photos/' + path.basename(filePath),
+      category: parsed.category || 'unknown',
+      environment: parsed.environment || 'unknown',
+      quality_score: parsed.quality_score || 5,
+      description: parsed.description || ''
+    };
+
+  } catch (error) {
+    console.error(`Error processing ${filePath}:`, error);
+    return null;
+  }
+}
+
+async function main() {
+  const photosDir = path.join(process.cwd(), 'public', 'assets', 'photos');
+  const files = await fs.readdir(photosDir);
+  const metadataMap: Record<string, ImageMeta> = {};
+  
+  const metadataPath = path.join(process.cwd(), 'public', 'assets', 'image-metadata.json');
+  
+  // Load existing to skip if needed, or to resume
+  let existing: Record<string, ImageMeta> = {};
+  try {
+    const existingData = await fs.readFile(metadataPath, 'utf-8');
+    existing = JSON.parse(existingData);
+  } catch (e) {
+    // Ignore if not exists
+  }
+
+  for (const file of files) {
+    const filePath = path.join(photosDir, file);
+    const relativePath = '/assets/photos/' + file;
+    
+    if (existing[relativePath] && existing[relativePath].category !== 'unknown') {
+      console.log(`Skipping ${file}, already analyzed.`);
+      metadataMap[relativePath] = existing[relativePath];
+      continue;
+    }
+
+    const meta = await classifyImage(filePath);
+    if (meta) {
+      metadataMap[relativePath] = meta;
+      // Save incrementally so we don't lose progress if it crashes
+      await fs.writeFile(metadataPath, JSON.stringify(metadataMap, null, 2));
+    }
+  }
+
+  console.log('Classification complete!');
+}
+
+main().catch(console.error);