Issue class: Enhancement
Severity: Medium
Category: observability
Repository: Mukku27/Inventory-Management-Using-GenAI
Affected files or impacted area: runtime diagnostics, inventory mutation auditability, operations
Summary
Introduce structured logging, health diagnostics, and an audit trail for inventory-changing actions and model-assisted operations.
Why it matters
Once the app modifies inventory data or calls external models, maintainers need a way to trace failures, understand user actions, and prove what changed. That’s essential for both production readiness and portfolio quality.
Technical evidence or justification
- The repository contains no logging configuration, no health/readiness checks, and no audit persistence for add/modify/remove workflows.
- Current code paths call external services and mutate the database without any structured diagnostic output.
Expected behavior or target state
Operators should be able to inspect startup/config health, trace user actions, and review a durable audit log of database mutations and model decisions.
Actual behavior or current gap
Failures and data changes are effectively invisible outside ad hoc Streamlit UI messages.
Recommended fix
Add structured application logs, startup health checks, correlation IDs per user action, and an audit table or append-only log for inventory mutations and AI-assisted decisions.
Suggested GitHub labels
enhancement, severity:medium, category:observability
Issue class: Enhancement
Severity: Medium
Category: observability
Repository: Mukku27/Inventory-Management-Using-GenAI
Affected files or impacted area: runtime diagnostics, inventory mutation auditability, operations
Summary
Introduce structured logging, health diagnostics, and an audit trail for inventory-changing actions and model-assisted operations.
Why it matters
Once the app modifies inventory data or calls external models, maintainers need a way to trace failures, understand user actions, and prove what changed. That’s essential for both production readiness and portfolio quality.
Technical evidence or justification
Expected behavior or target state
Operators should be able to inspect startup/config health, trace user actions, and review a durable audit log of database mutations and model decisions.
Actual behavior or current gap
Failures and data changes are effectively invisible outside ad hoc Streamlit UI messages.
Recommended fix
Add structured application logs, startup health checks, correlation IDs per user action, and an audit table or append-only log for inventory mutations and AI-assisted decisions.
Suggested GitHub labels
enhancement, severity:medium, category:observability