app.py

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)}