Fix dashboard hydration and archive theming
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## ADDED Requirements
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### Requirement: System suggests categories from merchant names
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The system SHALL support merchant-name-based category suggestion for expense entry while keeping all suggestion logic fully offline.
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#### Scenario: Known merchant resolves from deterministic rules
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- **WHEN** the user enters a merchant or shop name that matches a known merchant rule
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- **THEN** the system assigns the mapped category without needing model inference
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#### Scenario: Unknown merchant falls back to local model
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- **WHEN** the user enters a merchant or shop name that does not match a known merchant rule
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- **THEN** the system asks the local AI service for a category suggestion and returns the suggested category
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### Requirement: Ambiguous suggestions remain user-controlled
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The system SHALL keep the final saved category under user control for ambiguous or model-generated suggestions.
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#### Scenario: User confirms model suggestion before save
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- **WHEN** the category suggestion comes from model inference instead of a deterministic rule
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- **THEN** the user can review and confirm or change the category before the expense is saved
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#### Scenario: No cloud fallback is used
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- **WHEN** the local suggestion service is unavailable
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- **THEN** the system continues to allow manual category selection and does not send merchant data to a hosted provider
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## ADDED Requirements
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### Requirement: User can record categorized expenses
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The system SHALL allow the user to create an expense with a title, amount, category, and local calendar date using fixed starter categories.
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#### Scenario: Valid expense is created
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- **WHEN** the user submits a title, positive amount, valid category, and valid local date
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- **THEN** the system stores the expense and returns the created record
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#### Scenario: Invalid expense is rejected
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- **WHEN** the user submits a missing title, invalid amount, invalid category, or invalid date
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- **THEN** the system rejects the request with a validation error and does not store the expense
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### Requirement: User can review and delete expenses
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The system SHALL allow the user to list recorded expenses and delete a specific expense by identifier.
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#### Scenario: Expenses are listed
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- **WHEN** the user requests expenses for the app
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- **THEN** the system returns stored expenses in a stable order with their recorded fields
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### Requirement: User can browse expense history by month
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The system SHALL allow the user to select a `YYYY-MM` month when reviewing expense history and SHALL return the expenses recorded for that month.
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#### Scenario: Prior month entries are visible
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- **WHEN** the user selects February 2026 in the add-expense history view
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- **THEN** the system shows the expenses recorded in February 2026 and exposes delete actions for deletable entries in that month
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#### Scenario: Expense is deleted
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- **WHEN** the user deletes an existing expense
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- **THEN** the system removes that expense and it no longer appears in future listings or aggregates
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## ADDED Requirements
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### Requirement: Dashboard shows month-specific financial totals
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The system SHALL return month-specific dashboard data for a requested `YYYY-MM` month using the local machine timezone for month boundaries.
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#### Scenario: Dashboard totals are calculated for a populated month
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- **WHEN** the user requests the dashboard for a month with expenses and paychecks
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- **THEN** the system returns total expenses, total paychecks, net cash flow, and a category breakdown for that month
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#### Scenario: Dashboard supports partial current-month data
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- **WHEN** the user requests the dashboard for the current month before the month is complete
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- **THEN** the system returns meaningful month-to-date totals and comparisons using the transactions recorded so far
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### Requirement: Dashboard includes derived spending comparisons
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The system SHALL provide derived comparisons for the selected month, including highest category, largest expense, average daily spend, and paycheck coverage information.
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#### Scenario: Derived comparisons are available
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- **WHEN** the selected month contains enough data for comparisons
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- **THEN** the system returns the highest category, largest single expense, average daily spend, and spend-versus-paycheck coverage values
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#### Scenario: Derived comparisons degrade safely for sparse data
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- **WHEN** the selected month has no expenses or otherwise insufficient data for a comparison
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- **THEN** the system returns null or empty-safe comparison fields instead of failing
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## ADDED Requirements
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### Requirement: User can generate monthly AI insights on demand
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The system SHALL allow the user to manually generate monthly AI insights for any month with existing or sparse data by sending structured monthly context to a fully offline local inference runtime.
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#### Scenario: Insights are generated for a month with data
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- **WHEN** the user requests insight generation for a month with recorded activity
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- **THEN** the system sends monthly aggregates plus transaction samples to the local AI service and returns a rendered narrative summary with structured supporting totals
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#### Scenario: Prior month insights can be generated
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- **WHEN** the user requests insight generation for a previous month that has recorded data
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- **THEN** the system generates and stores insight output for that requested month
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### Requirement: Insight generation is read-only and safe for sparse months
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The system SHALL keep AI insight generation read-only and return a safe fallback summary when a month does not have enough data for meaningful guidance.
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#### Scenario: Sparse month returns fallback insight
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- **WHEN** the user requests insight generation for a month with empty or near-empty data
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- **THEN** the system returns a fallback message instead of low-confidence advice
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#### Scenario: AI does not mutate financial records
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- **WHEN** the system generates or stores monthly insights
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- **THEN** no expense or paycheck records are created, updated, or deleted as part of that request
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### Requirement: Insight generation remains private and resilient offline
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The system SHALL keep monthly insight generation fully offline and provide a clear fallback response when the local model runtime or selected model is unavailable.
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#### Scenario: Local runtime is unavailable
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- **WHEN** the user requests monthly insights while the local AI runtime is not running or the configured model is unavailable
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- **THEN** the system returns a clear setup or availability message instead of attempting a cloud fallback
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## ADDED Requirements
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### Requirement: User can record paychecks by pay date
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The system SHALL allow the user to create a paycheck with a positive amount and a local pay date.
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#### Scenario: Valid paycheck is created
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- **WHEN** the user submits a positive amount and valid local pay date
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- **THEN** the system stores the paycheck and returns the created record
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#### Scenario: Invalid paycheck is rejected
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- **WHEN** the user submits a missing or invalid amount or date
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- **THEN** the system rejects the request with a validation error and does not store the paycheck
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### Requirement: User can review and delete paychecks
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The system SHALL allow the user to list recorded paychecks and delete a specific paycheck by identifier.
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#### Scenario: Paychecks are listed
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- **WHEN** the user requests paychecks for the app
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- **THEN** the system returns stored paychecks in a stable order with their recorded fields
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#### Scenario: Paycheck is deleted
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- **WHEN** the user deletes an existing paycheck
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- **THEN** the system removes that paycheck and it no longer appears in future dashboard totals or insight inputs
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