Create app.py

app.py

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+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from PIL import Image
+import requests
+from transformers import AutoProcessor, SiglipModel
+
+# 1. 初始化 FastAPI 
+app = FastAPI(title="SigLIP 2 Embedding API")
+
+# 2. 自動載入您複製的 SigLIP 2 模型 (只會在啟動時載入一次)
+model_id = "google/siglip2-base-patch16-224"
+print("正在載入 SigLIP 2 模型...")
+processor = AutoProcessor.from_pretrained(model_id)
+model = SiglipModel.from_pretrained(model_id)
+print("模型載入完成！")
+
+# 定義資料格式：API 接收一個包含圖片網址的 JSON
+class ImageInput(BaseModel):
+    url: str
+
+# 3. 建立網頁 API 接口 /embed
+@app.post("/embed")
+def get_embedding(data: ImageInput):
+    try:
+        # 下載 n8n 傳過來的圖片網址
+        image = Image.open(requests.get(data.url, stream=True).raw)
+        
+        # 使用模型提取特徵
+        inputs = processor(images=image, return_tensors="pt")
+        with torch.no_grad():
+            # 提取 768 維度圖片向量
+            image_features = model.get_image_features(**inputs)
+            # 進行歸一化 (L2 Normalization)，這對向量搜尋非常重要
+            image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)
+        
+        # 將 Tensor 轉換為 Python 的標準陣列 (List)
+        embedding_list = image_features.squeeze().tolist()
+        
+        return {
+            "status": "success",
+            "dimension": len(embedding_list),
+            "embedding": embedding_list
+        }
+    except Exception as e:
+        return {"status": "error", "message": str(e)}