#码力全开·技术π对#如何结合Google Vision API与OpenAI CLIP模型实现跨模态（图像+文本）检索？

import clip
from google.cloud import vision

# 加载CLIP模型
model, preprocess = clip.load("ViT-B/32")
client = vision.ImageAnnotatorClient()

def get_image_description(image_path):
    with open(image_path, "rb") as image_file:
        content = image_file.read()
    image = vision.Image(content=content)
    response = client.label_detection(image=image)
    labels = [label.description for label in response.label_annotations]
    return labels

def encode_with_clip(texts):
    return clip.tokenize(texts)  # 文本编码

image_descriptions = get_image_description("your_image.jpg")
text_inputs = clip.tokenize(image_descriptions)
with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text_inputs)
similarity = (image_features @ text_features.T).softmax(dim=-1)