chore(generative_ai): update model references to stable gemini models (#14117)

[aditya-cyberverse]

Description

Fixes #14117

Updated deprecated Gemini model references across generative AI code samples to stable releases (gemini-1.5-flash and gemini-2.5-flash).

Checklist

Testing

• I have tested this change on a live environment and verified it works as intended.

Compliance & Style

• I have followed the Sample Guidelines.
• The README is updated with all relevant information (if applicable).
• If this PR adds a new sample directory: I updated the CODEOWNERS file.

---

Post-Approval Actions

• Please merge this PR for me once it is approved

[google-cla]

Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).

View this failed invocation of the CLA check for more information.

For the most up to date status, view the checks section at the bottom of the pull request.

[gemini-code-assist]

Code Review

This pull request updates multiple generative AI code samples to use 'gemini-1.5-flash' instead of 'gemini-2.0-flash-001' (and 'gemini-2.5-flash' instead of 'gemini-2.5-pro' in one case). The feedback correctly points out that in the pairwise evaluation sample, comparing 'gemini-1.5-flash' against itself defeats the purpose of a pairwise comparison, and suggests using 'gemini-1.5-pro' as the candidate model instead.

[gemini-code-assist]

In a pairwise evaluation, comparing the exact same model (gemini-1.5-flash) against itself defeats the purpose of demonstrating a pairwise comparison between two different models (e.g., a lighter/faster model vs. a larger/more capable model). To make this sample more meaningful and illustrative, consider using gemini-1.5-pro as the candidate model while keeping gemini-1.5-flash as the baseline.

Suggested change

	baseline_model = GenerativeModel("gemini-1.5-flash")
	# Candidate model for pairwise comparison
	candidate_model = GenerativeModel(
	"gemini-2.0-flash-001", generation_config={"temperature": 0.4}
	"gemini-1.5-flash", generation_config={"temperature": 0.4}
	)
	baseline_model = GenerativeModel("gemini-1.5-flash")
	# Candidate model for pairwise comparison
	candidate_model = GenerativeModel(
	"gemini-1.5-pro", generation_config={"temperature": 0.4}
	)