Setting Up Your Jupyter Notebook for AI-Driven Image Matching
In this section, we'll walk through the setup of a Jupyter Notebook designed to leverage AWS Bedrock for AI-driven image matching.
This setup will prepare us to create and manage our product catalog effectively.
Import Python libraries
First, import the Python libraries will use:
boto3: AWS SDK for Python, needed for AWS Bedrock
botocore.exceptions: For handling exceptions related to Boto3
base64: For encoding images in Base64 format, a requirement for AWS Bedrock
json: To handle JSON data, which is often used in APIs
os: To interact with the operating system, useful for file paths
pandas: For data manipulation and analysis. We will use it to store and manipulate the image and embedding data
sklearn.metrics.pairwise: Specifically, cosine_similarity from this module helps in comparing embeddings
uuid: To generate unique identifiers for each image in our catalog
Add all the libraries at the top of the Jupyter Notebook:
import boto3
from botocore.exceptions import NoCredentialsError
import base64
from IPython.core.display import HTML
import json
import os
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import uuid
In the next section, we'll initialize AWS Bedrock.