From 00f3aeb0087a1850fa73bd9fa11ae43843548ff7 Mon Sep 17 00:00:00 2001
From: Ferdinand <ferdinand@MacBook-Pro.local>
Date: Tue, 7 Apr 2026 13:43:02 +0200
Subject: [PATCH] feat: Claude Vision AI analysis integration

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 analyzer.py | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/analyzer.py b/analyzer.py
index 9654746..73ab5b3 100644
--- a/analyzer.py
+++ b/analyzer.py
@@ -3,6 +3,7 @@ import numpy as np
 from PIL import Image
 import imagehash
 import os
+import base64
 from typing import List, Optional
 
 
@@ -69,6 +70,58 @@ def find_duplicates(paths: List[str], threshold: int = 8) -> List[List[str]]:
 SUPPORTED_EXTENSIONS = {".jpg", ".jpeg", ".png"}
 
 
+def _analyze_with_ai(paths: List[str], api_key: str) -> dict:
+    """
+    Sendet Bilder an Claude Vision API zur Qualitaetsanalyse.
+    Gibt {path: [reasons]} zurueck. Bei Fehler wird der Pfad uebersprungen.
+    """
+    import anthropic
+
+    client = anthropic.Anthropic(api_key=api_key)
+    ai_results: dict = {path: [] for path in paths}
+
+    PROMPT = (
+        "Analysiere dieses Foto auf Qualitaetsprobleme fuer einen professionellen Fotografen. "
+        "Antworte NUR mit einer kommagetrennten Liste von Problemen aus diesen Kategorien: "
+        "unscharf, ueberbelichtet, unterbelichtet, schlechter Bildausschnitt, stoerende Elemente, "
+        "schlechter Weissabgleich. Wenn das Bild in Ordnung ist, antworte mit 'ok'."
+    )
+
+    for path in paths:
+        try:
+            with open(path, "rb") as f:
+                img_data = base64.standard_b64encode(f.read()).decode("utf-8")
+            ext = os.path.splitext(path)[1].lower().lstrip(".")
+            media_type = "image/jpeg" if ext in ("jpg", "jpeg") else "image/png"
+
+            response = client.messages.create(
+                model="claude-opus-4-6",
+                max_tokens=100,
+                messages=[{
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": media_type,
+                                "data": img_data,
+                            },
+                        },
+                        {"type": "text", "text": PROMPT},
+                    ],
+                }],
+            )
+            answer = response.content[0].text.strip().lower()
+            if answer != "ok":
+                reasons = [r.strip() for r in answer.split(",") if r.strip()]
+                ai_results[path].extend(reasons)
+        except Exception:
+            continue
+
+    return ai_results
+
+
 def analyze_folder(
     folder: str,
     blur_threshold: float = 100.0,