chore: fix detect logos snippets

[Alan5142]

Description

Fix the detection logos snippets to use annotate_image instead of logo_detection

Checklist

• I have followed Sample Guidelines from AUTHORING_GUIDE.MD
• README is updated to include all relevant information
• Tests pass: nox -s py-3.9 (see Test Environment Setup)
• Lint pass: nox -s lint (see Test Environment Setup)
• These samples need a new API enabled in testing projects to pass (let us know which ones)
• These samples need a new/updated env vars in testing projects set to pass (let us know which ones)
• This sample adds a new sample directory, and I updated the CODEOWNERS file with the codeowners for this sample
• This sample adds a new Product API, and I updated the Blunderbuss issue/PR auto-assigner with the codeowners for this sample
• Please merge this PR for me once it is approved

[gemini-code-assist]

Summary of Changes

Hello @Alan5142, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refactors the logo detection snippets within the Google Cloud Vision API samples. It transitions from using the specific logo_detection client method to the more versatile annotate_image method, explicitly defining LOGO_DETECTION as a feature in the request. This change aligns the samples with current API best practices, ensuring robustness and future compatibility.

Highlights

• API Method Update: The detect_logos function has been updated to use client.annotate_image with a LOGO_DETECTION feature, replacing the direct call to client.logo_detection.
• URI-based Logo Detection: Similarly, the detect_logos_uri function now utilizes client.annotate_image with the LOGO_DETECTION feature for URI-based image processing.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request updates the logo detection snippets to use the annotate_image method, which is a good improvement. However, the new implementation constructs the request using a dictionary with a potentially incorrect key (type instead of type_), which could lead to bugs. My review includes suggestions to fix this by using a more idiomatic and robust approach, passing keyword arguments directly to the annotate_image method and explicitly creating vision.Feature objects. This will improve code clarity and correctness.