Refactor and improve reliability of core model validation logic

[Dadiya-Harsh]

Background

The initial implementation of model_validation.py provides only basic functionality and is not robust enough for real-world usage. Failures occur frequently due to poor error handling, minimal abstraction, and lack of modularity.

Specific Changes Suggested

• Modularize the code: Split logic into smaller, well-defined functions (e.g., for API requests, parsing responses, error handling).
• Improve error handling: Catch and log exceptions for API timeouts, invalid responses, and network issues. Return informative error messages.
• Add retries and fallback: On transient errors (e.g., 429, 5xx), implement retries with exponential backoff. Add fallback logic for temporary API unavailability.
• Validate API responses: Check for required fields and types in API responses. Handle edge cases (missing models, unexpected data shape).
• Centralize configuration: Use a config object or file for API keys, endpoints, and model selection parameters.
• Test coverage: Add unit and integration tests for all functions, including edge cases and error paths.
• Extensibility: Design interfaces or abstract classes to allow plugging in new providers (OpenAI, Groq, Gemini, etc.) easily.
• Improve logging: Add debug and error logs for each step of model fetching and validation.
• Documentation: Document each function, its expected input/output, and error conditions.

Expected Outcome

• Reliable, maintainable, and extensible model validation logic.
• Fewer failures and more informative errors in production.
• Easier onboarding for contributors and future improvements.

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This issue tracks a major refactor and reliability improvement for the core model validation logic in model_validation.py. See issue #3 for previous context.