refactor: redesign admin permalinks to RESTful paths

- Replace fragile /admin/ai-playground and /admin/ai-generation routes with item-scoped /admin/questions/{id}/generate endpoints - Add /admin/tryouts/{id}/questions route using Tryout primary key instead of composite query params (?tryout_id=X&website_id=Y) - Fix variant detail and review-bulk endpoints to use scoped paths - Update all internal links (dashboard, hierarchy, exams) to new routes - Remove obsolete ai_playground_view/submit/save functions
2026-06-16 23:54:24 +07:00
parent 7adbc5fb97
commit 792f9b7483
16 changed files with 4606 additions and 741 deletions
--- a/ADMIN_UI_REDESIGN_PLAN.md
+++ b/ADMIN_UI_REDESIGN_PLAN.md
@@ -0,0 +1,700 @@
 # Admin UI Redesign Plan
 > **Document Type:** UI/UX Improvement Plan  
 > **Current System:** IRT Bank Soal Admin  
 > **Date:** 2026-06-15  
 > **Status:** Draft for Review
 ---
 ## Executive Summary
 The current admin interface is built from a **developer/system perspective** rather than a **human/admin perspective**. This plan outlines a complete redesign to make the admin dashboard intuitive, workflow-oriented, and human-readable.
 ### Current Problems
 | Problem | Impact |
 |---------|--------|
 | Navigation uses technical terms | Admins don't understand menu labels |
 | Multiple unrelated features in one view | Confusing, overwhelming |
 | Data displayed in database terminology | Hard to interpret scores |
 | No clear workflow guidance | Admin doesn't know what to do first |
 | No contextual help | Unclear what each feature does |
 | Mixed concern pages | AI + Questions + Calibration all on one page |
 ---
 ## Current State Analysis
 ### Current Navigation (System POV)
 ```
 ├── Dashboard (raw counts)
 ├── Websites (technical list)
 ├── Tryout Import (system term)
 ├── Data Hierarchy (developer term)
 ├── Basis Items (technical term)
 ├── Calibration Status (technical term)
 ├── Item Statistics (technical term)
 ├── Session Overview (technical term)
 ├── AI Playground (slang)
 └── Password Info (unrelated)
 ```
 ### Current Issues
 1. **Naming Problems:**
   - "Basis Items" → Should be "Question Templates" or "Original Questions"
   - "Data Hierarchy" → Should be "Data Overview" or "Website Structure"
   - "Tryout Import" → Should be "Import Questions"
   - "Calibration Status" → Should be "Question Quality" or "Difficulty Analysis"
   - "AI Playground" → Should be "Generate AI Questions"
   - "Session Overview" → Should be "Student Attempts"
 2. **Dashboard Issues:**
   - Shows raw database counts (Tryouts, Items, Sessions)
   - No meaningful KPIs or actionable insights
   - No visual indicators of system health
 3. **Page Organization:**
   - Too many technical terms on each page
   - Tables show raw data without explanation
   - No breadcrumbs or context
 ---
 ## Proposed Redesign
 ### New Navigation Structure (Human POV)
 ```
 🎯 Dashboard (Home)
 ├── System Health Summary
 ├── Quick Actions
 └── Recent Activity
 📋 Questions Bank
 ├── All Questions (list + search)
 ├── Question Templates (basis items)
 ├── Import Questions (from Excel/JSON)
 └── Question Quality (calibration status)
 🤖 AI Generation
 ├── Generate New Questions
 ├── Review Generated Questions
 └── Generation History
 📊 Exams (Tryouts)
 ├── All Exams (list)
 ├── Exam Settings (scoring mode)
 ├── Student Attempts
 └── Normalization Settings
 📈 Reports
 ├── Student Performance Report
 ├── Item Analysis Report
 ├── Exam Comparison Report
 └── Scheduled Reports
 ⚙️ Settings
 ├── Websites Management
 ├── Account Settings
 └── System Info
 ```
 ### Navigation Mapping Table
 | Current Menu | New Menu | Reason |
 |-------------|----------|--------|
 | Dashboard | 🎯 Dashboard | Home base |
 | Websites | ⚙️ Settings > Websites | Configuration |
 | Tryout Import | 📋 Questions > Import Questions | Workflow step |
 | Data Hierarchy | ⚙️ Settings | Admin settings |
 | Basis Items | 📋 Questions > Question Templates | Content management |
 | Calibration Status | 📋 Questions > Question Quality | Quality assurance |
 | Item Statistics | 📈 Reports > Item Analysis | Reporting |
 | Session Overview | 📊 Exams > Student Attempts | Workflow |
 | AI Playground | 🤖 AI Generation | Dedicated feature |
 | Password Info | ⚙️ Settings > Account | Configuration |
 ---
 ## Detailed Page Redesigns
 ### 1. Dashboard (Home) — `GET /admin/dashboard`
 **Current State:**
 ```python
 # Shows raw counts
 body = f"""
 <p>Signed in as <strong>{admin}</strong>.</p>
 <div class="grid">
  <div class="stat">Tryouts<strong>{tryouts}</strong></div>
  <div class="stat">Items<strong>{items}</strong></div>
  <div class="stat">Sessions<strong>{sessions}</strong></div>
  <div class="stat">Completed Sessions<strong>{completed}</strong></div>
 </div>
 <p><a href="/admin/ai-playground">Open AI Playground</a></p>
 """
 ```
 **Proposed State:**
 ```
 ┌─────────────────────────────────────────────────────────────────┐
 │  Good Morning, Admin! 👋                                    │
 │  Last login: Today at 9:00 AM                                │
 ├─────────────────────────────────────────────────────────────────┤
 │                                                                 │
 │  📊 System Overview                                              │
 │  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐            │
 │  │ 5 Exams  │ │ 450      │ │ 1,234    │ │ 89%      │            │
 │  │ Active   │ │ Questions│ │ Students │ │ Avg Score│            │
 │  └──────────┘ └──────────┘ └──────────┘ └──────────┘            │
 │                                                                 │
 │  ⚠️ Attention Needed                                             │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ ⚡ 23 questions need calibration (do this first!)       │    │
 │  │ 📝 5 AI-generated questions pending review              │    │
 │  │ 📥 2 exam exports ready for download                    │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 │  🚀 Quick Actions                                                │
 │  [Import Questions] [Generate AI] [View Reports] [Add Exam]     │
 │                                                                 │
 │  📈 Recent Activity                                              │
 │  • 12 students completed "UTBK 2024" in last hour               │
 │  • 3 new questions generated via AI                             │
 │  • Calibration completed for "SIMAK UI" (95% ready)            │
 │                                                                 │
 └─────────────────────────────────────────────────────────────────┘
 ```
 **Key Changes:**
 - Human-readable greeting with time
 - Meaningful metrics (not raw counts)
 - Actionable alerts with urgency indicators
 - Quick action buttons with clear labels
 - Recent activity feed
 ---
 ### 2. Questions Bank — Questions List (`/admin/questions`)
 **Current State:** Table with raw database fields
 **Proposed State:**
 ```
 ┌─────────────────────────────────────────────────────────────────┐
 │  📋 Question Bank                                               │
 ├─────────────────────────────────────────────────────────────────┤
 │                                                                 │
 │  [Search: "matematika"                    ] [Filter ▼] [🔍]     │
 │                                                                 │
 │  Showing 450 questions across 5 exams                           │
 │                                                                 │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ ☐ │ Q1  │  Berapakah hasil dari 2 + 2?                 │    │
 │  │   │     │  ▸ Easy (p=0.85) | Used 234x | SIMAK UI     │    │
 │  ├────┼─────┼──────────────────────────────────────────────┤    │
 │  │ ☐ │ Q5  │  Hitung integral dari x² dx...              │    │
 │  │   │     │  ▸ Medium (p=0.45) | Used 89x | UTBK 2024   │    │
 │  ├────┼─────┼──────────────────────────────────────────────┤    │
 │  │ ☐ │ Q12 │  Jelaskan teori evolusi...                   │    │
 │  │   │     │  ▸ Hard (p=0.22) | Used 45x | ONM 2024     │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 │  [Delete Selected] [Export Selected] [Edit Selected]            │
 │                                                                 │
 │  📄 Page 1 of 23  [<] [1] [2] [3] ... [>]                     │
 │                                                                 │
 └─────────────────────────────────────────────────────────────────┘
 ```
 **Key Changes:**
 - Question preview in list (not just ID)
 - Human-readable difficulty (Easy/Medium/Hard)
 - Usage count (how many times used)
 - Which exam it belongs to
 - Visual indicators for difficulty colors
 ---
 ### 3. Question Templates — (`/admin/templates`)
 **Current State:** "Basis Items" - confusing technical term
 **Proposed State:**
 ```
 ┌─────────────────────────────────────────────────────────────────┐
 │  📝 Question Templates                                          │
 │  (Original questions used to generate AI variants)             │
 ├─────────────────────────────────────────────────────────────────┤
 │                                                                 │
 │  Templates are your "master questions" that AI uses to         │
 │  create different versions with varying difficulty levels.     │
 │                                                                 │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ 📝 Template #45: "Berapakah hasil dari 2 + 2?"          │    │
 │  │    AI Generated Variants: 12 (3 easy, 6 medium, 3 hard) │    │
 │  │    [View All Variants] [Generate More] [Edit]            │    │
 │  ├─────────────────────────────────────────────────────────┤    │
 │  │ 📝 Template #89: "Hitung integral dari x² dx..."        │    │
 │  │    AI Generated Variants: 8 (2 easy, 4 medium, 2 hard)  │    │
 │  │    [View All Variants] [Generate More] [Edit]           │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 │  [+ Create New Template]                                        │
 │                                                                 │
 └─────────────────────────────────────────────────────────────────┘
 ```
 **Key Changes:**
 - Clear explanation of what templates are
 - Visual representation of variants
 - Easy action buttons
 - "Create New Template" prominent
 ---
 ### 4. AI Generation — (`/admin/ai-generation`)
 **Current State:** "AI Playground" - informal, confusing tabs
 **Proposed State:**
 ```
 ┌─────────────────────────────────────────────────────────────────┐
 │  🤖 AI Question Generator                                       │
 ├─────────────────────────────────────────────────────────────────┤
 │                                                                 │
 │  Generate new question variants using AI.                        │
 │  Select a template question and specify difficulty level.       │
 │                                                                 │
 │  ┌──────────────────────┐  ┌──────────────────────┐            │
 │  │ 📝 Select Template    │  │ 🎯 Target Difficulty │            │
 │  │ [Dropdown: Questions]│  │ ○ Easy (p > 0.70)   │            │
 │  └──────────────────────┘  │ ● Medium (p ≈ 0.50)  │            │
 │                             │ ○ Hard (p < 0.30)   │            │
 │  ┌──────────────────────┐  └──────────────────────┘            │
 │  │ 📝 How many variants?│                                       │
 │  │ [1] [3] [5] [10]     │  ┌──────────────────────┐            │
 │  └──────────────────────┘  │ 💬 Additional Notes  │            │
 │                            │ [Optional context...] │            │
 │                            └──────────────────────┘            │
 │                                                                 │
 │                    [🚀 Generate Questions]                       │
 │                                                                 │
 ├─────────────────────────────────────────────────────────────────┤
 │  📋 Generated Questions (Pending Review)                        │
 │                                                                 │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ 🔄 Generating... 2 of 5 questions completed              │    │
 │  │ [████████░░] 60%                                        │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 │  ✅ Generated & Ready for Review:                                │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ ✓ Variant #123: "Berapakah hasil dari 3 + 4?" (Easy)   │    │
 │  │   [Preview] [Approve] [Regenerate] [Reject]              │    │
 │  ├─────────────────────────────────────────────────────────┤    │
 │  │ ✓ Variant #124: "Hitung hasil dari 5 + 6..." (Easy)    │    │
 │  │   [Preview] [Approve] [Regenerate] [Reject]              │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 └─────────────────────────────────────────────────────────────────┘
 ```
 **Key Changes:**
 - Clear, labeled sections
 - Radio buttons for difficulty (not dropdown)
 - Progress indicator during generation
 - Clear action buttons (Approve/Reject/Regenerate)
 - Explanation of what each option means
 ---
 ### 5. Question Quality (Calibration) — (`/admin/question-quality`)
 **Current State:** "Calibration Status" - technical IRT terminology
 **Proposed State:**
 ```
 ┌─────────────────────────────────────────────────────────────────┐
 │  📊 Question Quality Dashboard                                   │
 │  (Shows how well each question is "calibrated" for testing)    │
 ├─────────────────────────────────────────────────────────────────┤
 │                                                                 │
 │  📖 What is Question Quality?                                   │
 │  Questions become "calibrated" after many students answer them. │
 │  Well-calibrated questions give accurate student scores.        │
 │                                                                 │
 ├─────────────────────────────────────────────────────────────────┤
 │                                                                 │
 │  Overall Quality: ████████░░ 78%                               │
 │  (78 out of 100 questions are ready for adaptive testing)      │
 │                                                                 │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ 📋 By Exam                                                 │    │
 │  │                                                           │    │
 │  │  UTBK 2024          ████████████ 95% ✓ Ready            │    │
 │  │  SIMAK UI           █████████░░░ 72% ⚠️ Partial        │    │
 │  │  ONM 2024           ██████░░░░░░ 45% ❌ Needs more data│    │
 │  │  PASIAD Selection   ████████████ 100% ✓ Excellent       │    │
 │  │                                                           │    │
 │  │  [Run Calibration for All Exams]                         │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 │  Questions Needing Attention:                                    │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ ⚠️ Q45 - "Hitung integral..." only answered 12 times     │    │
 │  │    Need at least 100 answers to calibrate properly.       │    │
 │  │    Current estimate: p=0.42 (might change)               │    │
 │  ├─────────────────────────────────────────────────────────┤    │
 │  │ ❌ Q78 - "Teori relativitas..." has conflicting answers  │    │
 │  │    Check if correct answer is correct in database.       │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 └─────────────────────────────────────────────────────────────────┘
 ```
 **Key Changes:**
 - Clear explanation of what calibration means
 - Progress bars for visual understanding
 - Status indicators (✓ Ready, ⚠️ Partial, ❌ Needs data)
 - Specific recommendations for action
 - User-friendly difficulty explanation
 ---
 ### 6. Student Attempts — (`/admin/student-attempts`)
 **Current State:** "Session Overview" - raw database table
 **Proposed State:**
 ```
 ┌─────────────────────────────────────────────────────────────────┐
 │  📊 Student Attempts                                            │
 │  (See how students performed on each exam)                      │
 ├─────────────────────────────────────────────────────────────────┤
 │                                                                 │
 │  Filter: [Select Exam ▼] [Status ▼] [Date Range ▼]  [Search]  │
 │                                                                 │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ 📋 UTBK 2024 Results                                     │    │
 │  │                                                           │    │
 │  │  Participants: 1,234 students                            │    │
 │  │  Average Score (NM): 672 / 1000                         │    │
 │  │  Average Score (NN): 505 / 1000                         │    │
 │  │  Completion Rate: 98% (1,209 completed)                 │    │
 │  │                                                           │    │
 │  │  Score Distribution:                                     │    │
 │  │  ▁▂▃▇█▇▃▂▁ (bell curve centered around 500)            │    │
 │  │  200  300  400  500  600  700  800  900  1000           │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 │  Recent Attempts:                                                │
 │  ┌─────────────────────────────────────────────────────────┐    │
 │  │ 👤 John Doe (john@example.com)                          │    │
 │  │    Exam: UTBK 2024 | Completed: Today, 2:30 PM          │    │
 │  │    Score: NM=720 (85th percentile) | NN=645              │    │
 │  │    Correct: 28/30 | Time: 45 minutes                    │    │
 │  ├─────────────────────────────────────────────────────────┤    │
 │  │ 👤 Jane Smith (jane@example.com)                         │    │
 │  │    Exam: SIMAK UI | Completed: Today, 1:15 PM            │    │
 │  │    Score: NM=580 (45th percentile) | NN=485              │    │
 │  │    Correct: 22/30 | Time: 52 minutes                     │    │
 │  └─────────────────────────────────────────────────────────┘    │
 │                                                                 │
 │  [Export All Results] [View Detailed Report] [Schedule Report]  │
 │                                                                 │
 └─────────────────────────────────────────────────────────────────┘
 ```
 **Key Changes:**
 - Grouped by exam with summary stats
 - Human-readable student info
 - Percentile ranking
 - Score distribution visualization
 - Clear action buttons
 ---
 ### 7. Reports — (`/admin/reports`)
 **Proposed State:**
 ```
 ┌─────────────────────────────────────────────────────────────────┐
 │  📈 Reports                                                     │
 ├─────────────────────────────────────────────────────────────────┤
 │                                                                 │
 │  Generate detailed analysis reports for exams and students.      │
 │                                                                 │
 │  ┌─────────────────────────┐ ┌─────────────────────────┐       │
 │  │ 📊 Student Performance   │ │ 📋 Item Analysis        │       │
 │  │                         │ │                         │       │
 │  │ See individual student  │ │ Analyze question        │       │
 │  │ scores, rankings, and   │ │ difficulty, validity,   │       │
 │  │ detailed breakdowns.    │ │ and discrimination.     │       │
 │  │                         │ │                         │       │
 │  │ [Generate Report]       │ │ [Generate Report]        │       │
 │  └─────────────────────────┘ └─────────────────────────┘       │
 │                                                                 │
 │  ┌─────────────────────────┐ ┌─────────────────────────┐       │
 │  │ 📈 Exam Comparison       │ │ 📅 Scheduled Reports     │       │
 │  │                         │ │                         │       │
 │  │ Compare scores across   │ │ Set up automatic        │       │
 │  │ different exams or      │ │ weekly/monthly reports  │       │
 │  │ time periods.           │ │ delivery.               │       │
 │  │                         │ │                         │       │
 │  │ [Generate Report]       │ │ [Manage Schedules]      │       │
 │  └─────────────────────────┘ └─────────────────────────┘       │
 │                                                                 │
 └─────────────────────────────────────────────────────────────────┘
 ```
 **Key Changes:**
 - Card-based layout with icons
 - Clear description of each report type
 - Visual cards instead of dropdowns
 - Scheduled reports as first-class feature
 ---
 ## Implementation Phases
 ### Phase 1: Navigation Redesign (Foundation)
 **Files to modify:**
 - `app/admin_web.py` - Update `ADMIN_NAV_ITEMS`
 - Create new route handlers
 **Steps:**
 1. Rename navigation items with human labels
 2. Create new route structure
 3. Implement breadcrumb system
 4. Add help tooltips
 **New Navigation Structure:**
 ```python
 ADMIN_NAV_ITEMS = (
    ("Dashboard", "/admin/dashboard", ("/admin/dashboard",)),
    ("Questions", "/admin/questions", ("/admin/questions", "/admin/templates")),
    ("AI Generator", "/admin/ai-generation", ("/admin/ai-generation",)),
    ("Exams", "/admin/exams", ("/admin/exams", "/admin/student-attempts")),
    ("Reports", "/admin/reports", ("/admin/reports",)),
    ("Settings", "/admin/settings", ("/admin/settings",)),
 )
 ```
 ---
 ### Phase 2: Dashboard Overhaul
 **New Dashboard Components:**
 1. Greeting with user name and time
 2. System health cards (with meaningful metrics)
 3. Action alerts section
 4. Quick action buttons
 5. Recent activity feed
 **Files to modify:**
 - `dashboard_view()` function
 - `_render_admin_page()` for dashboard-specific layout
 ---
 ### Phase 3: Questions Section
 **New Pages:**
 1. `/admin/questions` - List all questions with search/filter
 2. `/admin/questions/{id}` - Question detail view
 3. `/admin/templates` - Question templates (formerly basis items)
 4. `/admin/questions/import` - Import wizard
 **Key UI Components:**
 - Question preview cards
 - Difficulty badges (Easy/Medium/Hard)
 - Color-coded indicators
 - Inline search
 ---
 ### Phase 4: AI Generation Section
 **New Pages:**
 1. `/admin/ai-generation` - Main generation interface
 2. `/admin/ai-generation/review` - Review pending variants
 3. `/admin/ai-generation/history` - Generation history
 **Key UI Components:**
 - Template selector with preview
 - Difficulty radio buttons
 - Generation progress bar
 - Batch approve/reject actions
 ---
 ### Phase 5: Exams Section
 **New Pages:**
 1. `/admin/exams` - List all exams
 2. `/admin/exams/{id}/settings` - Exam configuration
 3. `/admin/student-attempts` - Student attempts list
 4. `/admin/normalization` - Normalization settings
 **Key UI Components:**
 - Exam cards with status indicators
 - Student attempt cards
 - Score distribution visualization
 ---
 ### Phase 6: Reports Section
 **New Pages:**
 1. `/admin/reports` - Report dashboard
 2. `/admin/reports/student-performance` - Student report
 3. `/admin/reports/item-analysis` - Item report
 4. `/admin/reports/exam-comparison` - Comparison report
 5. `/admin/reports/scheduled` - Scheduled reports
 **Key UI Components:**
 - Report type cards
 - Export format options
 - Schedule configuration
 ---
 ## Technical Implementation Notes
 ### CSS Class Naming Convention
 ```css
 /* Old: System POV */
 .stat { }
 .grid { }
 .table-wrap { }
 /* New: Human POV */
 .dashboard-hero { }
 .metric-card { }
 .question-list { }
 .difficulty-badge { }
 .difficulty-easy { background: #dcfce7; }
 .difficulty-medium { background: #fef3c7; }
 .difficulty-hard { background: #fee2e2; }
 ```
 ### Helper Functions to Create
 ```python
 # In admin_web.py
 def _render_question_card(item: Item) -> str:
    """Render a human-readable question card."""
    difficulty = _human_difficulty(item.ctt_p)
    difficulty_color = _difficulty_color(item.ctt_p)
    return f"""
    <div class="question-card">
      <div class="difficulty-badge {difficulty_color}">{difficulty}</div>
      <div class="question-stem">{escape(item.stem[:100])}...</div>
      <div class="question-meta">
        Used {item.calibration_sample_size}x | 
        {item.tryout_id}
      </div>
    </div>
    """
 def _human_difficulty(p_value: float | None) -> str:
    """Convert p-value to human-readable difficulty."""
    if p_value is None:
        return "Unknown"
    if p_value > 0.70:
        return "Easy"
    elif p_value >= 0.30:
        return "Medium"
    else:
        return "Hard"
 def _difficulty_color(p_value: float | None) -> str:
    """Get color class for difficulty badge."""
    if p_value is None:
        return "difficulty-unknown"
    if p_value > 0.70:
        return "difficulty-easy"
    elif p_value >= 0.30:
        return "difficulty-medium"
    else:
        return "difficulty-hard"
 ```
 ### Responsive Design
 ```css
 /* Mobile-friendly layout */
@media (max-width: 768px) {
  .admin-layout {
    flex-direction: column;
  }
  .sidebar-nav {
    display: flex;
    overflow-x: auto;
    padding: 8px;
  }
  .metric-cards {
    grid-template-columns: repeat(2, 1fr);
  }
 }
 ```
 ---
 ## Success Metrics
 | Metric | Target |
 |--------|--------|
 | Time to complete common task | Reduce by 50% |
 | Admin confusion score | < 2/5 |
 | Support tickets about UI | Reduce by 80% |
 | Feature discovery rate | > 90% can find features |
 ---
 ## Appendix: Terminology Mapping
 | System Term | Human Term |
 |------------|------------|
 | Tryout | Exam / Test |
 | Item | Question |
 | Basis Item | Question Template / Original Question |
 | Session | Student Attempt |
 | Calibration | Question Quality / Difficulty Analysis |
 | IRT | Adaptive Scoring |
 | CTT | Standard Scoring |
 | Bobot | Weight / Point Value |
 | NM | Raw Score |
 | NN | Normalized Score |
 | p-value | Difficulty Score |
 | Theta | Student Ability Score |
 ---
 ## Files to Modify
 | File | Changes |
 |------|---------|
 | `app/admin_web.py` | Complete UI rewrite |
 | `app/admin.py` | May need minor updates |
 | `requirements.txt` | Add any new frontend deps (if needed) |
 ---
 ## Next Steps
 1. [ ] Review and approve this plan
 2. [ ] Prioritize phases (suggest starting with Phase 1 & 2)
 3. [ ] Create mockups/wireframes for key pages
 4. [ ] Implement Phase 1: Navigation & Dashboard
 5. [ ] User testing with admin users
 6. [ ] Iterate based on feedback
--- a/18
+++ b/18
@@ -0,0 +1,18 @@
 FROM python:3.11-slim
 WORKDIR /app
 # Install system dependencies
 RUN apt-get update && apt-get install -y --no-install-recommends \
    postgresql-client \
    && rm -rf /var/lib/apt/lists/*
 # Copy requirements and install Python dependencies
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 # Copy application code
 COPY . .
 # Run migrations and start the app
 CMD ["sh", "-c", "alembic upgrade head && uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload"]
--- a/PROJECT_UNDERSTANDING.md
+++ b/PROJECT_UNDERSTANDING.md
@@ -0,0 +1,612 @@
 # Project Understanding: IRT-Powered Adaptive Question Bank System
 > **Project Name:** IRT Bank Soal  
 > **Version:** 1.0.0  
 > **Last Updated:** 2026-06-15  
 > **Repository:** https://git.backoffice.biz.id/dwindown/yellow-bank-soal
 ---
 ## Table of Contents
 1. [Executive Summary](#executive-summary)
 2. [Project Purpose](#project-purpose)
 3. [Tech Stack](#tech-stack)
 4. [Project Structure](#project-structure)
 5. [Core Concepts](#core-concepts)
 6. [Data Models](#data-models)
 7. [API Endpoints](#api-endpoints)
 8. [Key Services](#key-services)
 9. [Scoring Formulas](#scoring-formulas)
 10. [Configuration](#configuration)
 11. [Workflows](#workflows)
 12. [Deployment](#deployment)
 ---
 ## Executive Summary
 This is a **FastAPI-based backend system** for managing adaptive assessment/tryout exams with sophisticated scoring capabilities. The system supports both **Classical Test Theory (CTT)** and **Item Response Theory (IRT)** scoring methods, with multi-website support for WordPress integration.
 ### Key Features
 | Feature | Description |
 |---------|-------------|
 | **CTT Scoring** | Classical Test Theory with exact Excel formula compatibility |
 | **IRT Support** | Item Response Theory (1PL Rasch model) for adaptive testing |
 | **Multi-Site** | Single backend serving multiple WordPress sites |
 | **AI Generation** | Automatic question variant generation via OpenRouter |
 | **Excel Import/Export** | Bulk import/export questions from Excel files |
 | **Adaptive Testing** | Computer Adaptive Testing (CAT) with theta estimation |
 | **Normalization** | Static, dynamic, or hybrid score normalization |
 ---
 ## Project Purpose
 The system replaces traditional fixed-difficulty exams with an **adaptive question bank** that:
 1. **Measures student ability accurately** using IRT theta estimation
 2. **Provides comparable scores** across different exam sessions via normalization
 3. **Generates new questions** using AI when needed
 4. **Integrates with WordPress** LMS platforms for student access
 5. **Reduces exam fraud** by delivering different question variants to each student
 ---
 ## Tech Stack
 ### Core Technologies
 ```
 Framework:       FastAPI >= 0.104.1
 Server:          Uvicorn >= 0.24.0
 Database:        PostgreSQL + SQLAlchemy 2.0 (async)
 ORM:             SQLAlchemy >= 2.0.23
 Driver:          asyncpg >= 0.29.0
 Migrations:      Alembic >= 1.13.0
 Validation:      Pydantic >= 2.5.0
 ```
 ### Data Processing
 ```
 Excel:           openpyxl >= 3.1.2, pandas >= 2.1.4
 Math/Science:    numpy >= 1.26.2, scipy >= 1.11.4
 ```
 ### External Integrations
 ```
 AI:              OpenAI >= 1.6.1 (OpenRouter API)
 Task Queue:      Celery >= 5.3.6, Redis >= 5.0.1
 Admin Panel:     FastAPI-Admin >= 1.0.0
 ```
 ---
 ## Project Structure
 ```
 yellow-bank-soal/
 ├── app/
 │   ├── __init__.py
 │   ├── main.py              # FastAPI app entry point
 │   ├── admin.py             # FastAPI Admin configuration
 │   ├── admin_web.py         # Admin web interface
 │   ├── database.py          # Database configuration & session
 │   │
 │   ├── api/
 │   │   └── v1/
 │   │       ├── __init__.py
 │   │       └── session.py   # Adaptive session endpoints
 │   │
 │   ├── core/
 │   │   ├── __init__.py
 │   │   ├── auth.py          # Authentication & authorization
 │   │   ├── config.py        # Settings from environment
 │   │   └── rate_limit.py    # Rate limiting
 │   │
 │   ├── models/
 │   │   ├── __init__.py
 │   │   ├── ai_generation_run.py
 │   │   ├── item.py          # Question items
 │   │   ├── report_schedule.py
 │   │   ├── session.py       # Student tryout sessions
 │   │   ├── tryout.py        # Tryout configurations
 │   │   ├── tryout_import_snapshot.py
 │   │   ├── tryout_snapshot_question.py
 │   │   ├── tryout_stats.py  # Normalization statistics
 │   │   ├── user.py
 │   │   ├── user_answer.py   # Student responses
 │   │   └── website.py
 │   │
 │   ├── routers/
 │   │   ├── __init__.py
 │   │   ├── admin.py         # Admin-only endpoints
 │   │   ├── ai.py            # AI generation endpoints
 │   │   ├── import_export.py # Excel import/export
 │   │   ├── reports.py       # Report generation
 │   │   ├── sessions.py      # Session management
 │   │   ├── tryouts.py       # Tryout configuration
 │   │   └── wordpress.py     # WordPress integration
 │   │
 │   ├── schemas/             # Pydantic request/response models
 │   │   ├── __init__.py
 │   │   ├── ai.py
 │   │   ├── report.py
 │   │   ├── session.py
 │   │   ├── tryout.py
 │   │   └── wordpress.py
 │   │
 │   └── services/
 │       ├── __init__.py
 │       ├── ai_generation.py # OpenRouter integration
 │       ├── cat_selection.py # Computer Adaptive Testing
 │       ├── config_management.py
 │       ├── ctt_scoring.py   # CTT scoring engine
 │       ├── excel_import.py  # Excel parsing
 │       ├── irt_calibration.py # IRT calibration
 │       ├── normalization.py
 │       ├── reporting.py
 │       ├── tryout_json_import.py
 │       └── wordpress_auth.py
 │
 ├── alembic/                 # Database migrations
 │   ├── env.py
 │   ├── script.py.mako
 │   └── versions/
 │
 ├── tests/                   # Unit & integration tests
 │   ├── test_auth_scope.py
 │   ├── test_auth_tokens.py
 │   ├── test_model_mappings.py
 │   ├── test_normalization.py
 │   ├── test_operational_hardening.py
 │   ├── test_route_wiring.py
 │   ├── test_security_regressions.py
 │   └── test_tryout_json_import.py
 │
 ├── requirements.txt
 ├── alembic.ini
 ├── irt_1pl_mle.py          # Standalone IRT MLE script
 ├── PRD.md                  # Product Requirements Document
 ├── project-brief.md        # Technical specification
 └── handoff.md             # Project handoff context
 ```
 ---
 ## Core Concepts
 ### 1. Tryout (Exam)
 A **Tryout** represents a complete exam/test with configurable behavior:
 ```python
 scoring_mode:       "ctt" | "irt" | "hybrid"
 selection_mode:     "fixed" | "adaptive" | "hybrid"
 normalization_mode: "static" | "dynamic" | "hybrid"
 ```
 ### 2. Item (Question)
 An **Item** represents a single question with:
 - **Content**: stem (question text), options (A/B/C/D), correct_answer
 - **CTT Parameters**: p-value (difficulty), bobot (weight)
 - **IRT Parameters**: b (difficulty), se (standard error)
 - **Metadata**: slot position, difficulty level, AI generation info
 ### 3. Session (Student Attempt)
 A **Session** tracks a student's attempt:
 - Links student (`wp_user_id`) to a Tryout
 - Records all answers via `UserAnswer` records
 - Stores computed scores: NM, NN, theta
 ### 4. Website (Multi-Tenant)
 The system supports **multiple WordPress websites** from a single backend:
 - Each website has isolated data
 - Authenticated via `X-Website-ID` header
 - WordPress JWT tokens for authentication
 ---
 ## Data Models
 ### Entity Relationship Diagram
 ```mermaid
 erDiagram
    Website ||--o{ Tryout : "hosts"
    Website ||--o{ User : "contains"
    Website ||--o{ Session : "serves"
    Website ||--o{ Item : "contains"
    Tryout ||--o{ Item : "contains"
    Tryout ||--o{ Session : "has"
    Tryout ||--o{ TryoutStats : "tracks"
    Session ||--o{ UserAnswer : "contains"
    Session ||--o{ User : "belongs to"
    Item ||--o{ UserAnswer : "answered by"
    Item ||--o{ Item : "has variants"
    AIGenerationRun ||--o{ Item : "generates"
 ```
 ### Model Summary
 | Model | Purpose | Key Fields |
 |-------|---------|------------|
 | `Website` | Multi-tenant isolation | domain, wordpress_url |
 | `User` | WordPress user mapping | wp_user_id, website_id |
 | `Tryout` | Exam configuration | scoring_mode, selection_mode, normalization_mode |
 | `Item` | Question | stem, options, ctt_p, ctt_bobot, irt_b, irt_se |
 | `Session` | Student attempt | session_id, NM, NN, theta |
 | `UserAnswer` | Single response | response, is_correct, bobot_earned |
 | `TryoutStats` | Normalization data | participant_count, rataan, sb |
 | `AIGenerationRun` | AI generation batch | model, status, items_generated |
 ---
 ## API Endpoints
 ### Public API (via `/api/v1`)
 | Method | Endpoint | Description |
 |--------|----------|-------------|
 | `GET` | `/tryout/{tryout_id}/config` | Get tryout configuration |
 | `PUT` | `/tryout/{tryout_id}/normalization` | Update normalization settings |
 | `GET` | `/tryout/` | List tryouts for website |
 | `GET` | `/tryout/{tryout_id}/calibration-status` | Get IRT calibration status |
 | `POST` | `/tryout/{tryout_id}/calibrate` | Trigger IRT calibration |
 | `POST` | `/session/` | Create new session |
 | `GET` | `/session/{session_id}` | Get session details |
 | `POST` | `/session/{session_id}/complete` | Submit answers, calculate scores |
 ### Admin API (requires admin role)
 | Method | Endpoint | Description |
 |--------|----------|-------------|
 | `POST` | `/ai/generate` | Generate AI questions |
 | `POST` | `/import/excel` | Import questions from Excel |
 | `GET` | `/export/excel/{tryout_id}` | Export questions to Excel |
 | `GET` | `/reports/*` | Generate various reports |
 ### Adaptive Session API (via `/api/v1/session`)
 | Method | Endpoint | Description |
 |--------|----------|-------------|
 | `POST` | `/adaptive/start` | Start adaptive session |
 | `POST` | `/adaptive/respond` | Submit answer, get next item |
 | `POST` | `/adaptive/complete` | Complete adaptive session |
 ---
 ## Key Services
 ### 1. CTT Scoring Engine (`ctt_scoring.py`)
 Implements Classical Test Theory scoring with exact Excel formulas.
 **Key Functions:**
 - `calculate_ctt_p()` - Difficulty: p = Σ Benar / Total Peserta
 - `calculate_ctt_bobot()` - Weight: Bobot = 1 - p
 - `calculate_ctt_nm()` - Raw Score: NM = (Total_Bobot / Total_Bobot_Max) × 1000
 - `calculate_ctt_nn()` - Normalized: NN = 500 + 100 × ((NM - Rataan) / SB)
 - `categorize_difficulty()` - Categorize by p-value
 - `update_tryout_stats()` - Incrementally update normalization stats
 ### 2. IRT Calibration (`irt_calibration.py`)
 Implements Item Response Theory (1PL Rasch model) for adaptive testing.
 **Key Functions:**
 - `estimate_theta_mle()` - MLE theta estimation for students
 - `estimate_b()` - IRT difficulty calibration for items
 - `calibrate_item()` - Calibrate single item from response data
 - `calibrate_all()` - Batch calibrate all items in tryout
 - `calculate_fisher_information()` - Fisher information for item selection
 **Parameters:**
 - θ (theta): Student ability [-3, +3]
 - b: Item difficulty [-3, +3]
 - Probability: P(θ) = 1 / (1 + exp(-(θ - b)))
 ### 3. AI Generation (`ai_generation.py`)
 Generates question variants using OpenRouter API.
 **Key Functions:**
 - `generate_question()` - Generate single question via OpenRouter
 - `generate_questions_batch()` - Generate multiple questions
 - `save_ai_question()` - Save generated question to database
 - `check_cache_reuse()` - Check for reusable similar questions
 **Models Supported:**
 - Qwen 2.5 32B (balanced)
 - Mistral Small (low cost)
 - Llama 3.3 70B (premium)
 ### 4. Excel Import/Export (`excel_import.py`)
 Bulk import/export questions from Excel files.
 **Key Functions:**
 - `parse_excel_import()` - Parse Excel file to items
 - `bulk_insert_items()` - Insert parsed items to database
 - `export_questions_to_excel()` - Export tryout to Excel
 ### 5. CAT Selection (`cat_selection.py`)
 Computer Adaptive Testing item selection algorithm.
 **Key Functions:**
 - `select_next_item()` - Select next item based on theta estimate
 - `calculate_theta_update()` - Update theta after response
 - `check_termination()` - Check if test should end
 ---
 ## Scoring Formulas
 ### CTT (Classical Test Theory)
 Based on exact client Excel formulas:
 ```python
 # STEP 1: Tingkat Kesukaran (p-value)
 p = Σ Benar / Total Peserta
 # STEP 2: Bobot (Weight)
 Bobot = 1 - p
 # STEP 3: Total Benar per Siswa
 Total_Benar = count of correct answers
 # STEP 4: Total Bobot Earned per Siswa
 Total_Bobot_Siswa = Σ Bobot for each correct answer
 # STEP 5: Nilai Mentah (Raw Score)
 NM = (Total_Bobot_Siswa / Total_Bobot_Max) × 1000
 # STEP 6: Nilai Nasional (Normalized Score)
 NN = 500 + 100 × ((NM - Rataan) / SB)
 ```
 ### IRT (Item Response Theory)
 1PL Rasch Model:
 ```python
 # Probability of correct response
 P(θ, b) = 1 / (1 + exp(-(θ - b)))
 # Log-likelihood for MLE
 LL = Σ [u_i × log(P) + (1-u_i) × log(1-P)]
 # Theta estimation via MLE
 θ_mle = argmax_θ LL(θ)
 ```
 ### Difficulty Categories (CTT Standard)
 | p-value | Category | Description |
 |---------|----------|-------------|
 | p < 0.30 | Sulit | Difficult |
 | 0.30 ≤ p ≤ 0.70 | Sedang | Medium |
 | p > 0.70 | Mudah | Easy |
 ---
 ## Configuration
 ### Environment Variables
 ```bash
 # Database
 DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/irt_bank_soal
 # FastAPI
 SECRET_KEY=your-secret-key-here
 ENVIRONMENT=development  # development, staging, production
 ENABLE_ADMIN=true
 ADMIN_USERNAME=admin
 ADMIN_PASSWORD=your-password
 # OpenRouter (AI)
 OPENROUTER_API_KEY=sk-or-v1-xxx
 OPENROUTER_MODEL_QWEN=qwen/qwen2.5-32b-instruct
 OPENROUTER_MODEL_CHEAP=mistralai/mistral-small-2603
 OPENROUTER_MODEL_LLAMA=meta-llama/llama-3.3-70b-instruct
 # Redis/Celery
 REDIS_URL=redis://localhost:6379/0
 CELERY_BROKER_URL=redis://localhost:6379/0
 # CORS
 ALLOWED_ORIGINS=http://localhost:3000,https://yourdomain.com
 ```
 ### Tryout Configuration Options
 ```python
 # Scoring Mode
 scoring_mode = "ctt"        # Classical Test Theory
 scoring_mode = "irt"        # Item Response Theory
 scoring_mode = "hybrid"     # Both (IRT for calibration, CTT for scoring)
 # Selection Mode
 selection_mode = "fixed"    # Fixed order questions
 selection_mode = "adaptive" # Computer Adaptive Testing
 selection_mode = "hybrid"   # Start fixed, switch to adaptive
 # Normalization Mode
 normalization_mode = "static"   # Use hardcoded rataan/sb
 normalization_mode = "dynamic"  # Calculate from participant data
 normalization_mode = "hybrid"   # Dynamic when sufficient data
 ```
 ---
 ## Workflows
 ### 1. Student Taking a Tryout
 ```mermaid
 sequenceDiagram
    participant S as Student
    participant API as FastAPI
    participant WP as WordPress
    S->>API: POST /session/ (start session)
    API-->>S: session_id
    loop For each question
        S->>API: GET /session/{id}/next-item
        API-->>S: Question data
        S->>API: POST /session/{id}/answer
        API-->>S: Next question or completion
    end
    S->>API: POST /session/{id}/complete
    API-->>S: NM, NN scores
 ```
 ### 2. Admin Importing Questions
 ```mermaid
 flowchart TD
    A[Upload Excel File] --> B[Parse Excel]
    B --> C{Validate Structure}
    C -->|Invalid| D[Return Error]
    C -->|Valid| E[Calculate CTT p & bobot]
    E --> F[Bulk Insert Items]
    F --> G[Commit to Database]
    G --> H[Return Import Summary]
 ```
 ### 3. AI Question Generation
 ```mermaid
 flowchart TD
    A[Request Generation] --> B{Check Cache}
    B -->|Found similar| C[Return Cached]
    B -->|Not found| D[Call OpenRouter API]
    D --> E{Parse Response}
    E -->|Parse Error| F[Return Error]
    E -->|Success| G[Save to Database]
    G --> H[Return Generated Item]
 ```
 ### 4. IRT Calibration
 ```mermaid
 flowchart TD
    A[Collect Responses] --> B{Enough Data?}
    B -->|No| C[Wait for more]
    B -->|Yes| D[For each Item]
    D --> E[Get Response Matrix]
    E --> F[Estimate b via MLE]
    F --> G[Calculate Standard Error]
    G --> H[Update Item]
    H --> D
    D --> I[Mark Items Calibrated]
 ```
 ---
 ## Deployment
 ### Requirements
 - Python 3.10+
 - PostgreSQL 14+
 - Redis 6+ (for Celery)
 - Nginx (reverse proxy)
 - aaPanel with Python Manager (recommended)
 ### Running the Application
 ```bash
 # Install dependencies
 pip install -r requirements.txt
 # Run migrations
 alembic upgrade head
 # Start server
 uvicorn app.main:app --host 0.0.0.0 --port 8000
 # Or with reload (development)
 uvicorn app.main:app --reload
 ```
 ### Running Tests
 ```bash
 pytest tests/ -v
 ```
 ### API Documentation
 - Swagger UI: `http://localhost:8000/docs`
 - ReDoc: `http://localhost:8000/redoc`
 - OpenAPI JSON: `http://localhost:8000/openapi.json`
 ---
 ## Security Considerations
 ### Authentication
 - WordPress JWT tokens for user authentication
 - `X-Website-ID` header for multi-tenant isolation
 - Admin routes protected by admin role check
 ### Production Hardening
 1. **SECRET_KEY** must be set to a strong, unique value
 2. **ADMIN_PASSWORD** must not be the default
 3. **CORS** origins should be explicitly configured
 4. **Database** connections should use SSL in production
 5. **Rate limiting** enabled for AI generation endpoints
 ---
 ## Glossary
 | Term | Definition |
 |------|------------|
 | **Tryout** | An exam/test assessment |
 | **Item** | A single question in a tryout |
 | **Session** | A student's attempt at a tryout |
 | **CTT** | Classical Test Theory - traditional scoring |
 | **IRT** | Item Response Theory - modern adaptive scoring |
 | **NM** | Nilai Mentah - raw score [0-1000] |
 | **NN** | Nilai Nasional - normalized score [0-1000] |
 | **θ (theta)** | IRT ability estimate [-3 to +3] |
 | **b** | IRT item difficulty [-3 to +3] |
 | **p-value** | CTT proportion correct [0 to 1] |
 | **Bobot** | CTT weight (1 - p) |
 | **Rataan** | Mean (Indonesian) |
 | **SB** | Simpangan Baku - Standard Deviation |
 | **CAT** | Computer Adaptive Testing |
 | **MLE** | Maximum Likelihood Estimation |
 ---
 ## References
 - [PRD.md](./PRD.md) - Complete Product Requirements Document
 - [project-brief.md](./project-brief.md) - Original technical specification
 - [FastAPI Documentation](https://fastapi.tiangolo.com/)
 - [SQLAlchemy 2.0](https://docs.sqlalchemy.org/en/20/)
 - [Item Response Theory](https://en.wikipedia.org/wiki/Item_response_theory)
--- a/alembic.ini
+++ b/alembic.ini
@@ -84,7 +84,7 @@ path_separator = os
 # database URL.  This is consumed by the user-maintained env.py script only.
 # other means of configuring database URLs may be customized within the env.py
 # file.
-sqlalchemy.url = postgresql+asyncpg://postgres:postgres@localhost:5432/irt_bank_soal
+sqlalchemy.url = driver://user:pass@localhost/dbname
 [post_write_hooks]
--- a/app/admin_web.py
+++ b/app/admin_web.py
--- a/app/admin_web_icons.py
+++ b/app/admin_web_icons.py
@@ -0,0 +1,112 @@
 """
 Icon constants using inline SVG (Heroicons style).
 These replace emoji usage in the admin UI for consistent, professional icons.
 """
 # Navigation icons
 ICON_DASHBOARD = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M3.75 6A2.25 2.25 0 0 1 6 3.75h2.25A2.25 2.25 0 0 1 10.5 6v2.25a2.25 2.25 0 0 1-2.25 2.25H6a2.25 2.25 0 0 1-2.25-2.25V6ZM3.75 15.75A2.25 2.25 0 0 1 6 13.5h2.25a2.25 2.25 0 0 1 2.25 2.25V18a2.25 2.25 0 0 1-2.25 2.25H6A2.25 2.25 0 0 1 3.75 18v-2.25ZM13.5 6a2.25 2.25 0 0 1 2.25-2.25H18A2.25 2.25 0 0 1 20.25 6v2.25A2.25 2.25 0 0 1 18 10.5h-2.25a2.25 2.25 0 0 1-2.25-2.25V6ZM13.5 15.75a2.25 2.25 0 0 1 2.25-2.25H18a2.25 2.25 0 0 1 2.25 2.25V18A2.25 2.25 0 0 1 18 20.25h-2.25A2.25 2.25 0 0 1 13.5 18v-2.25Z" /></svg>"""
 ICON_QUESTIONS = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M9.879 7.519c1.171-1.025 3.071-1.025 4.242 0 1.172 1.025 1.172 2.687 0 3.712-.203.179-.43.326-.67.442-.745.361-1.45.999-1.45 1.827v.75M21 12a9 9 0 1 1-18 0 9 9 0 0 1 18 0Zm-9 5.25h.008v.008H12v-.008Z" /></svg>"""
 ICON_IMPORT = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M3 16.5v2.25A2.25 2.25 0 0 0 5.25 21h13.5A2.25 2.25 0 0 0 21 18.75V16.5m-13.5-9L12 3m0 0 4.5 4.5M12 3v13.5" /></svg>"""
 ICON_AI = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M9.813 15.904 9 18.75l-.813-2.846a4.5 4.5 0 0 0-3.09-3.09L2.25 12l2.846-.813a4.5 4.5 0 0 0 3.09-3.09L9 5.25l.813 2.846a4.5 4.5 0 0 0 3.09 3.09L15.75 12l-2.846.813a4.5 4.5 0 0 0-3.09 3.09ZM18.259 8.715 18 9.75l-.259-1.035a3.375 3.375 0 0 0-2.455-2.456L14.25 6l1.036-.259a3.375 3.375 0 0 0 2.455-2.456L18 2.25l.259 1.035a3.375 3.375 0 0 0 2.456 2.456L21.75 6l-1.035.259a3.375 3.375 0 0 0-2.456 2.456ZM16.894 20.567 16.5 21.75l-.394-1.183a2.25 2.25 0 0 0-1.423-1.423L13.5 18.75l1.183-.394a2.25 2.25 0 0 0 1.423-1.423l.394-1.183.394 1.183a2.25 2.25 0 0 0 1.423 1.423l1.183.394-1.183.394a2.25 2.25 0 0 0-1.423 1.423Z" /></svg>"""
 ICON_EXAMS = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M19.5 14.25v-2.625a3.375 3.375 0 0 0-3.375-3.375h-1.5A1.125 1.125 0 0 1 13.5 7.125v-1.5a3.375 3.375 0 0 0-3.375-3.375H8.25m0 12.75h7.5m-7.5 3H12M10.5 2.25H5.625c-.621 0-1.125.504-1.125 1.125v17.25c0 .621.504 1.125 1.125 1.125h12.75c.621 0 1.125-.504 1.125-1.125V11.25a9 9 0 0 0-9-9Z" /></svg>"""
 ICON_REPORTS = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M3 13.125C3 12.504 3.504 12 4.125 12h2.25c.621 0 1.125.504 1.125 1.125v6.75C7.5 20.496 6.996 21 6.375 21h-2.25A1.125 1.125 0 0 1 3 19.875v-6.75ZM9.75 8.625c0-.621.504-1.125 1.125-1.125h2.25c.621 0 1.125.504 1.125 1.125v11.25c0 .621-.504 1.125-1.125 1.125h-2.25a1.125 1.125 0 0 1-1.125-1.125V8.625ZM16.5 4.125c0-.621.504-1.125 1.125-1.125h2.25C20.496 3 21 3.504 21 4.125v15.75c0 .621-.504 1.125-1.125 1.125h-2.25a1.125 1.125 0 0 1-1.125-1.125V4.125Z" /></svg>"""
 ICON_SETTINGS = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M9.594 3.94c.09-.542.56-.94 1.11-.94h2.593c.55 0 1.02.398 1.11.94l.213 1.281c.063.374.313.686.645.87.074.04.147.083.22.127.325.196.72.257 1.075.124l1.217-.456a1.125 1.125 0 0 1 1.37.49l1.296 2.247a1.125 1.125 0 0 1-.26 1.431l-1.003.827c-.293.241-.438.613-.43.992a7.723 7.723 0 0 1 0 .255c-.008.378.137.75.43.991l1.004.827c.424.35.534.955.26 1.43l-1.298 2.247a1.125 1.125 0 0 1-1.369.491l-1.217-.456c-.355-.133-.75-.072-1.076.124a6.47 6.47 0 0 1-.22.128c-.331.183-.581.495-.644.869l-.213 1.281c-.09.543-.56.94-1.11.94h-2.594c-.55 0-1.019-.398-1.11-.94l-.213-1.281c-.062-.374-.312-.686-.644-.87a6.52 6.52 0 0 1-.22-.127c-.325-.196-.72-.257-1.076-.124l-1.217.456a1.125 1.125 0 0 1-1.369-.49l-1.297-2.247a1.125 1.125 0 0 1 .26-1.431l1.004-.827c.292-.24.437-.613.43-.991a6.932 6.932 0 0 1 0-.255c.007-.38-.138-.751-.43-.992l-1.004-.827a1.125 1.125 0 0 1-.26-1.43l1.297-2.247a1.125 1.125 0 0 1 1.37-.491l1.216.456c.356.133.751.072 1.076-.124.072-.044.146-.086.22-.128.332-.183.582-.495.644-.869l.214-1.28Z" /><path stroke-linecap="round" stroke-linejoin="round" d="M15 12a3 3 0 1 1-6 0 3 3 0 0 1 6 0Z" /></svg>"""
 ICON_LOGOUT = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M15.75 9V5.25A2.25 2.25 0 0 0 13.5 3h-6a2.25 2.25 0 0 0-2.25 2.25v13.5A2.25 2.25 0 0 0 7.5 21h6a2.25 2.25 0 0 0 2.25-2.25V15M12 9l-3 3m0 0 3 3m-3-3h12.75" /></svg>"""
 # Page icons
 ICON_TARGET = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M12 18v-5.25m0 0a6.01 6.01 0 0 0 1.5-.189m-1.5.189a6.01 6.01 0 0 1-1.5-.189m3.75 7.478a12.06 12.06 0 0 1-4.5 0m3.75 2.383a14.406 14.406 0 0 1-3 0M14.25 18v-.192c0-.983.658-1.727 1.5-1.727s1.5.744 1.5 1.727V18m-4.5 0h.008v.008H14.25v-.008Z" /></svg>"""
 ICON_USERS = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M15 19.128a9.38 9.38 0 0 0 2.625.372 9.337 9.337 0 0 0 4.121-.952 4.125 4.125 0 0 0-7.533-2.493M15 19.128v-.003c0-1.113-.285-2.16-.786-3.07M15 19.128v.106A12.318 12.318 0 0 1 8.624 21c-2.331 0-4.512-.645-6.374-1.766l-.001-.109a6.375 6.375 0 0 1 11.964-3.07M12 6.375a3.375 3.375 0 1 1-6.75 0 3.375 3.375 0 0 1 6.75 0Zm8.25 2.25a2.625 2.625 0 1 1-5.25 0 2.625 2.625 0 0 1 5.25 0Z" /></svg>"""
 ICON_CALIBRATION = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M10.5 6a7.5 7.5 0 1 0 0 15 7.5 7.5 0 0 0 0-15ZM21 12a9 9 0 1 1-18 0 9 9 0 0 1 18 0Z" /><path stroke-linecap="round" stroke-linejoin="round" d="M12 6v6h6" /></svg>"""
 ICON_STUDENTS = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M4.26 10.147a60.438 60.438 0 0 0-.491 6.347A48.62 48.62 0 0 1 12 20.904a48.62 48.62 0 0 1 8.232-4.41 60.46 60.46 0 0 0-.491-6.347m-15.482 0a50.636 50.636 0 0 0-2.658-.813A59.906 59.906 0 0 1 12 3.493a59.903 59.903 0 0 1 10.399 5.84c-.896.248-1.783.52-2.658.814m-15.482 0A50.717 50.717 0 0 1 12 13.489a50.702 50.702 0 0 1 7.74-3.342M6.75 15a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm0 0v-3.675A55.378 55.378 0 0 1 12 8.443m-7.007 11.55A5.981 5.981 0 0 0 6.75 15.75v-1.5" /></svg>"""
 ICON_DOWNLOAD = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M3 16.5v2.25A2.25 2.25 0 0 0 5.25 21h13.5A2.25 2.25 0 0 0 21 18.75V16.5M16.5 12 12 16.5m0 0L7.5 12m4.5 4.5V3" /></svg>"""
 ICON_UPLOAD = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M3 16.5v2.25A2.25 2.25 0 0 0 5.25 21h13.5A2.25 2.25 0 0 0 21 18.75V16.5m-13.5-9L12 3m0 0 4.5 4.5M12 3v13.5" /></svg>"""
 ICON_SEARCH = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="m21 21-5.197-5.197m0 0A7.5 7.5 0 1 0 5.196 5.196a7.5 7.5 0 0 0 10.607 10.607Z" /></svg>"""
 ICON_CHECK = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="m4.5 12.75 6 6 9-13.5" /></svg>"""
 ICON_WARNING = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126ZM12 15.75h.007v.008H12v-.008Z" /></svg>"""
 ICON_INFO = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="m11.25 11.25.041-.02a.75.75 0 0 1 1.063.852l-.708 2.836a.75.75 0 0 0 1.063.853l.041-.021M21 12a9 9 0 1 1-18 0 9 9 0 0 1 18 0Zm-9-3.75h.008v.008H12V8.25Z" /></svg>"""
 ICON_LIGHTBULB = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M12 18v-5.25m0 0a6.01 6.01 0 0 0 1.5-.189m-1.5.189a6.01 6.01 0 0 1-1.5-.189m3.75 7.478a12.06 12.06 0 0 1-4.5 0m3.75 2.383a14.406 14.406 0 0 1-3 0M14.25 18v-.192c0-.983.658-1.727 1.5-1.727s1.5.744 1.5 1.727V18m-4.5 0h.008v.008H14.25v-.008Z" /></svg>"""
 ICON_TREND_UP = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M2.25 18 9 11.25l4.306 4.306a11.95 11.95 0 0 1 5.814-5.518l2.74-1.22m0 0-5.94-2.281m5.94 2.28-2.28 5.941" /></svg>"""
 ICON_TREND_DOWN = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="page-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M2.25 6 9 12.75l4.286-4.286a11.948 11.948 0 0 1 4.306 6.43l.776 2.898m0 0 3.182-5.511m-3.182 5.51-5.511-3.181" /></svg>"""
 # Huge icons for replacing emojis (24x24 with larger visual weight)
 ICON_HUGE_TARGET = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M12 18v-5.25m0 0a8.01 8.01 0 0 0 1.5-.189m-1.5.189a8.01 8.01 0 0 1-1.5-.189m3.75 7.478a10.56 10.56 0 0 1-4.5 0m3.75 2.383a13.406 13.406 0 0 1-3 0M14.25 18v-.192c0-.983.658-1.727 1.5-1.727s1.5.744 1.5 1.727V18m-4.5 0h.008v.008H14.25v-.008Z" /></svg>"""
 ICON_HUGE_USER = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M15.75 6a3.75 3.75 0 1 1-7.5 0 3.75 3.75 0 0 1 7.5 0ZM4.501 20.118a7.5 7.5 0 0 1 14.998 0A17.933 17.933 0 0 1 12 21.75c-2.676 0-5.216-.584-7.499-1.632Z" /></svg>"""
 ICON_HUGE_CHECK = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="m4.5 12.75 6 6 9-13.5" /></svg>"""
 ICON_HUGE_CLOCK = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M12 6v6h4.5m4.5 0a9 9 0 1 1-18 0 9 9 0 0 1 18 0Z" /></svg>"""
 ICON_HUGE_ROCKET = """<svg xmlns="http://www.w3.org/2000/svg" width="1em" height="1em" viewBox="0 0 24 24"><path d="M0 0h24v24H0z" fill="none" /><g fill="none" stroke="currentColor" stroke-width="1.5"><path stroke-linejoin="round" d="m11.801 6.49l1.486-1.486c1.673-1.673 3.862-2.367 6.18-2.48c.902-.044 1.352-.066 1.714.295c.361.362.34.812.295 1.714c-.113 2.318-.807 4.507-2.48 6.18L17.511 12.2c-1.224 1.223-1.572 1.571-1.315 2.898c.254 1.014.499 1.995-.238 2.732c-.894.895-1.71.895-2.604 0l-7.183-7.183c-.895-.894-.895-1.71 0-2.604c.737-.737 1.718-.492 2.732-.238c1.327.257 1.675-.091 2.898-1.315Z" /><path stroke-linecap="round" d="m2.5 21.5l5-5m1 5l2-2m-8-4l2-2" /><path stroke-linecap="round" stroke-linejoin="round" d="M17.125 7H17m.25 0a.25.25 0 1 1-.5 0a.25.25 0 0 1 .5 0" /></g></svg>"""
 ICON_HUGE_CHART = """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M3 13.125C3 12.504 3.504 12 4.125 12h2.25c.621 0 1.125.504 1.125 1.125v6.75C7.5 20.496 6.996 21 6.375 21h-2.25A1.125 1.125 0 0 1 3 19.875v-6.75ZM9.75 8.625c0-.621.504-1.125 1.125-1.125h2.25c.621 0 1.125.504 1.125 1.125v11.25c0 .621-.504 1.125-1.125 1.125h-2.25a1.125 1.125 0 0 1-1.125-1.125V8.625ZM16.5 4.125c0-.621.504-1.125 1.125-1.125h2.25C20.496 3 21 3.504 21 4.125v15.75c0 .621-.504 1.125-1.125 1.125h-2.25a1.125 1.125 0 0 1-1.125-1.125V4.125Z" /></svg>"""
 # Emoji to SVG icon mapping for replacement
 EMOJI_TO_ICON = {
    # Navigation & main icons
    "🏠": ICON_DASHBOARD,
    "📝": ICON_QUESTIONS,
    "📥": ICON_IMPORT,
    "🤖": ICON_AI,
    "📋": ICON_EXAMS,
    "📊": ICON_REPORTS,
    "⚙️": ICON_SETTINGS,
    "🚪": ICON_LOGOUT,
    "🎯": ICON_HUGE_TARGET,
    "👤": ICON_HUGE_USER,
    "👥": ICON_USERS,
    "⚠️": ICON_WARNING,
    "ℹ️": ICON_INFO,
    "🚀": ICON_HUGE_ROCKET,
    "✅": ICON_HUGE_CHECK,
    "❌": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M6 18 18 6M6 6l12 12" /></svg>""",
    "⏳": ICON_HUGE_CLOCK,
    "📈": ICON_TREND_UP,
    "📉": ICON_TREND_DOWN,
    "💡": ICON_LIGHTBULB,
    "👋": '<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" style="display:inline;width:28px;height:28px;margin-bottom:-4px;"><path stroke-linecap="round" stroke-linejoin="round" d="M15.042 21.672 13.684 16.6m0 0-2.51 2.225.569-9.47 5.227 7.917-3.286-.672ZM12 2.25V4.5m5.834.166-1.591 1.591M20.25 10.5H18M7.757 14.743l-1.59 1.59M6 10.5H3.75m4.007-4.243-1.59-1.59" /></svg>',
    "📊": ICON_REPORTS,
    "🚀": ICON_HUGE_ROCKET,
    "📈": ICON_TREND_UP,
    # Additional icons from UI
    "🌐": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M12 21a9.004 9.004 0 0 0 8.716-6.747M12 21a9.004 9.004 0 0 1-8.716-6.747M12 21c2.485 0 4.5-4.03 4.5-9S14.485 3 12 3m0 18c-2.485 0-4.5-4.03-4.5-9S9.515 3 12 3m0 0a8.997 8.997 0 0 1 7.843 4.582M12 3a8.997 8.997 0 0 0-7.843 4.582m15.686 0A11.953 11.953 0 0 1 12 10.5c-2.998 0-5.74-1.1-7.843-2.918m15.686 0A8.959 8.959 0 0 1 21 12c0 .778-.099 1.533-.284 2.253m0 0A17.919 17.919 0 0 1 12 16.5c-3.162 0-6.133-.815-8.716-2.247m0 0A9.015 9.015 0 0 1 3 12c0-1.605.42-3.113 1.157-4.418" /></svg>""",
    "🔍": ICON_SEARCH,
    "📁": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M2.25 12.75V12A2.25 2.25 0 0 1 4.5 9.75h15A2.25 2.25 0 0 1 21.75 12v.75m-8.69-6.44-2.12-2.12a1.5 1.5 0 0 0-1.061-.44H4.5A2.25 2.25 0 0 0 2.25 6v12a2.25 2.25 0 0 0 2.25 2.25h15A2.25 2.25 0 0 0 21.75 18V9a2.25 2.25 0 0 0-2.25-2.25h-5.379a1.5 1.5 0 0 1-1.06-.44Z" /></svg>""",
    "🔐": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M16.5 10.5V6.75a4.5 4.5 0 1 0-9 0v3.75m-.75 11.25h10.5a2.25 2.25 0 0 0 2.25-2.25v-6.75a2.25 2.25 0 0 0-2.25-2.25H6.75a2.25 2.25 0 0 0-2.25 2.25v6.75a2.25 2.25 0 0 0 2.25 2.25Z" /></svg>""",
    "⚡": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="m3.75 13.5 10.5-11.25L12 10.5h8.25L9.75 21.75 12 13.5H3.75Z" /></svg>""",
    "💾": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M20.25 6.375c0 2.278-3.694 4.125-8.25 4.125S3.75 8.653 3.75 6.375m16.5 0c0-2.278-3.694-4.125-8.25-4.125S3.75 4.097 3.75 6.375m16.5 0v11.25c0 2.278-3.694 4.125-8.25 4.125s-8.25-1.847-8.25-4.125V6.375m16.5 0v3.75m-16.5-3.75v3.75m16.5 0v3.75C20.25 16.153 16.556 18 12 18s-8.25-1.847-8.25-4.125v-3.75m16.5 0v3.75C20.25 20.653 16.556 22.5 12 22.5s-8.25-1.847-8.25-4.125v-3.75m-16.5 0v3.75" /></svg>""",
    "🔄": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="huge-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M16.023 9.348h4.992v-.001M2.985 19.644v-4.992m0 0h4.992m-4.993 0 3.181 3.183a8.25 8.25 0 0 0 13.803-3.7M4.031 9.865a8.25 8.25 0 0 1 13.803-3.7l3.181 3.182m0-4.991v4.99" /></svg>""",
    "🔘": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M8.25 6.75h12M8.25 12h12m-12 5.25h12M3.75 6.75h.007v.008H3.75V6.75Zm.375 0a.375.375 0 1 1-.75 0 .375.375 0 0 1 .75 0ZM3.75 12h.007v.008H3.75V12Zm.375 0a.375.375 0 1 1-.75 0 .375.375 0 0 1 .75 0Zm-.375 5.25h.007v.008H3.75v-.008Zm.375 0a.375.375 0 1 1-.75 0 .375.375 0 0 1 .75 0Z" /></svg>""",
    "📍": """<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="nav-icon"><path stroke-linecap="round" stroke-linejoin="round" d="M15 10.5a3 3 0 1 1-6 0 3 3 0 0 1 6 0Z" /><path stroke-linecap="round" stroke-linejoin="round" d="M19.5 10.5c0 7.142-7.5 11.25-7.5 11.25S4.5 17.642 4.5 10.5a7.5 7.5 0 1 1 15 0Z" /></svg>""",
 }
 # Navigation icon mapping
 NAV_ICONS_SVG = {
    "Dashboard": ICON_DASHBOARD,
    "Questions": ICON_QUESTIONS,
    "Import Questions": ICON_IMPORT,
    "AI Generator": ICON_AI,
    "Exams": ICON_EXAMS,
    "Reports": ICON_REPORTS,
    "Settings": ICON_SETTINGS,
    "Logout": ICON_LOGOUT,
 }
--- a/app/services/ai_generation.py
+++ b/app/services/ai_generation.py
@@ -107,6 +107,7 @@ REQUIREMENTS:
 4. Only ONE correct answer
 5. Include a clear explanation of why the correct answer is correct
 6. Make the question noticeably {level_desc} - not just a minor variation
 7. Follow and preserve any HTML formatting (e.g., <p>, <br>, <b>) present in the basis question
 OUTPUT FORMAT:
 Return ONLY a valid JSON object with this exact structure (no markdown, no code blocks):
--- a/docker-compose.dev.yml
+++ b/docker-compose.dev.yml
@@ -0,0 +1,37 @@
 services:
  postgres:
    image: postgres:15-alpine
    environment:
      POSTGRES_USER: irt_user
      POSTGRES_PASSWORD: dev_password
      POSTGRES_DB: irt_bank_soal
    ports:
      - "5432:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data
    user: "70:70" # postgres user
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redis_data:/data
  app:
    build: .
    ports:
      - "8080:8000"
    environment:
      DATABASE_URL: postgresql+asyncpg://irt_user:dev_password@postgres:5432/irt_bank_soal
      REDIS_URL: redis://redis:6379
    depends_on:
      - postgres
      - redis
    volumes:
      - .:/app
    command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
 volumes:
  postgres_data:
  redis_data:
--- a/docs/ALUR-APLIKASI-DAN-IRT.md
+++ b/docs/ALUR-APLIKASI-DAN-IRT.md
@@ -0,0 +1,626 @@
 # Alur Aplikasi IRT-Powered Question Bank
 Dokumen ini menjelaskan alur lengkap aplikasi dari input data hingga menghasilkan next-question berbasis IRT.
 ---
 ## 1. Arsitektur Sistem
 ### 1.1 Teknologi Stack
 ```
 Framework:       FastAPI >= 0.104.1
 Database:        PostgreSQL + SQLAlchemy 2.0 (async)
 AI:              OpenAI (OpenRouter API)
 Admin Panel:     FastAPI-Admin
 Math:            numpy, scipy
 Excel:           openpyxl, pandas
 ```
 ### 1.2 Entity Relationship
 ```mermaid
 erDiagram
    Website ||--o{ Tryout : "hosts"
    Website ||--o{ User : "contains"
    Website ||--o{ Session : "serves"
    Website ||--o{ Item : "contains"
    Tryout ||--o{ Item : "contains"
    Tryout ||--o{ Session : "has"
    Session ||--o{ UserAnswer : "contains"
    Item ||--o{ Item : "has variants"
    Item ||--o{ UserAnswer : "answered by"
    AIGenerationRun ||--o{ Item : "generates"
 ```
 ---
 ## 2. Konsep Inti
 ### 2.1 Tryout (Exam)
 **Tryout** merepresentasikan 1 ujian lengkap dengan konfigurasi:
 | Field | Opsi | Default | Deskripsi |
 |-------|------|---------|-----------|
 | `scoring_mode` | `ctt`, `irt`, `hybrid` | `ctt` | Metode kalkulasi score |
 | `selection_mode` | `fixed`, `adaptive`, `hybrid` | `fixed` | Strategi pemilihan soal |
 | `normalization_mode` | `static`, `dynamic`, `hybrid` | `static` | Metode normalisasi |
 ### 2.2 Item (Soal)
 **Item** merepresentasikan 1 soal dengan parameter:
 | Field | Deskripsi |
 |-------|-----------|
 | `stem` | Teks pertanyaan |
 | `options` | Pilihan jawaban (A/B/C/D/E) |
 | `correct_answer` | Kunci jawaban |
 | `slot` | Posisi nomor soal (1, 2, 3...) |
 | `level` | Kategori kesulitan (mudah/sedang/sulit) |
 | `parent_item_id` | ID soal original (jika ini variant) |
 | `calibrated` | Status IRT calibration |
 | `irt_b` | Item difficulty parameter |
 | `irt_se` | Standard error |
 | `ctt_p` | P-value (tingkat kesukaran CTT) |
 | `ctt_bobot` | Bobot soal = 1 - p |
 ### 2.3 Session (Percobaan Siswa)
 **Session** melacak aktivitas siswa:
 | Field | Deskripsi |
 |-------|-----------|
 | `session_id` | Identifier unik |
 | `wp_user_id` | ID user dari WordPress |
 | `tryout_id` | Tryout yang diambil |
 | `theta` | Kemampuan estimasi IRT |
 | `theta_se` | Standard error theta |
 | `NM` | Nilai Mentah (raw score) |
 | `NN` | Nilai Nasional (normalized) |
 | `is_completed` | Status selesai |
 ### 2.4 Website (Multi-Tenant)
 Sistem mendukung multiple WordPress websites dari 1 backend:
 - Isolasi data per website
 - Auth via `X-Website-ID` header
 - WordPress JWT tokens
 ---
 ## 3. Alur Input Data
 ### 3.1 Sumber Data Masuk
 | Sumber | Format | Endpoint | Fungsi |
 |--------|--------|----------|--------|
 | Admin Import | Excel (.xlsx) | `POST /import/excel` | Bulk import dari file Excel |
 | JSON Import | JSON | `tryout_json_import.py` | Import dari JSON (LMS external) |
 | AI Generation | API Request | `POST /ai/generate` | Generate variant soal baru |
 ### 3.2 Flow Import JSON
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │ ADMIN: Import Tryout JSON                                   │
 ├─────────────────────────────────────────────────────────────┤
 │                                                             │
 │  1. Upload JSON file                                        │
 │     └─> File berisi 1 tryout lengkap (misal: "TO 2024")    │
 │     └─> Terdiri dari N soal (slot 1, 2, 3, ...)            │
 │                                                             │
 │  2. Parse JSON                                              │
 │     └─> Extract setiap soal → Item record                  │
 │     └─> Generate unique item_id                             │
 │                                                             │
 │  3. Simpan ke Database                                      │
 │     └─> Item.calibrated = False (belum ada IRT params)     │
 │     └─> Item.ctt_p = NULL (belum ada response data)        │
 │                                                             │
 └─────────────────────────────────────────────────────────────┘
 ```
 ### 3.3 Flow AI Generate Variants
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │ ADMIN: Generate AI Variants                                 │
 ├─────────────────────────────────────────────────────────────┤
 │                                                             │
 │  1. Pilih Item Original                                     │
 │     └─> Ambil 1 soal dari imported tryout                   │
 │                                                             │
 │  2. Request ke OpenRouter API                               │
 │     └─> Kirim prompt dengan soal original                   │
 │     └─> Minta generate variant dengan level berbeda         │
 │                                                             │
 │  3. Simpan Variant                                          │
 │     └─> variant.item_id = unique_id                         │
 │     └─> variant.parent_item_id = original.id                │
 │     └─> variant.slot = original.slot (nomor sama)           │
 │                                                             │
 │  4. Result                                                  │
 │     └─> Slot 1: 1 original + 1 variant = 2 soal            │
 │     └─> Slot 2: 1 original + 1 variant = 2 soal            │
 │     └─> Total: 2N soal (N slot × 2 variant)                │
 │                                                             │
 └─────────────────────────────────────────────────────────────┘
 ```
 ### 3.4 Contoh Struktur Data Setelah Import + Generate
 ```
 Tryout: "TO-2024"
 ├── Slot 1
 │   ├── Item #1 (original, calibrated=True, irt_b=0.5)
 │   └── Item #2 (variant, calibrated=True, irt_b=-0.3)
 ├── Slot 2
 │   ├── Item #3 (original, calibrated=True, irt_b=0.8)
 │   └── Item #4 (variant, calibrated=True, irt_b=0.2)
 └── ...
 ```
 ---
 ## 4. Pemrosesan Scoring
 ### 4.1 CTT (Classical Test Theory)
 #### Step-by-Step Formula:
 ```python
 # STEP 1: Tingkat Kesukaran (p-value)
 p = Σ Benar / Total Peserta
 # Contoh: 70 siswa menjawab benar dari 100 siswa → p = 0.70
 # STEP 2: Bobot (Weight)
 bobot = 1 - p
 # Contoh: bobot = 1 - 0.70 = 0.30
 # STEP 3: Total Benar per Siswa
 total_benar = count(correct answers)
 # STEP 4: Total Bobot Earned per Siswa
 total_bobot_siswa = Σ bobot for each correct answer
 # Contoh: Jawab benar 3 soal dengan bobot [0.3, 0.5, 0.2] = 1.0
 # STEP 5: Nilai Mentah (Raw Score)
 NM = (Total_Bobot_Siswa / Total_Bobot_Max) × 1000
 # Contoh: NM = (1.0 / 2.5) × 1000 = 400
 # STEP 6: Nilai Nasional (Normalized Score)
 NN = 500 + 100 × ((NM - Rataan) / SB)
 # Contoh: NN = 500 + 100 × ((400 - 450) / 80) = 437.5
 ```
 #### Kategori Kesulitan (CTT Standard):
 | p-value | Kategori | Arti |
 |---------|----------|------|
 | p < 0.30 | Sulit | Hanya <30% siswa menjawab benar |
 | 0.30 ≤ p ≤ 0.70 | Sedang | 30-70% siswa menjawab benar |
 | p > 0.70 | Mudah | >70% siswa menjawab benar |
 ### 4.2 IRT (Item Response Theory) - 1PL Rasch Model
 #### Formula Inti:
 ```python
 # Probability of correct response
 P(θ, b) = 1 / (1 + exp(-(θ - b)))
 # Di mana:
 # - θ (theta) = kemampuan siswa [-3, +3]
 # - b = difficulty soal [-3, +3]
 # Contoh:
 # - Siswa dengan θ = 0.5 menghadapi soal dengan b = 0.5
 # - P(0.5, 0.5) = 1 / (1 + exp(0)) = 0.5 (50% kemungkinan benar)
 ```
 #### Interpretasi Theta:
 | Theta | Kemampuan | Persentase Benar (jika b=0) |
 |-------|-----------|------------------------------|
 | -3.0 | Sangat Lemah | ~5% |
 | -1.5 | Lemah | ~18% |
 | 0.0 | Rata-rata | ~50% |
 | +1.5 | Cerdas | ~82% |
 | +3.0 | Sangat Cerdas | ~95% |
 #### Theta Estimation via MLE:
 ```python
 # Log-likelihood
 LL = Σ [u_i × log(P) + (1-u_i) × log(1-P)]
 # u_i = 1 jika benar, 0 jika salah
 # Theta estimation = maximize LL
 θ_mle = argmax_θ LL(θ)
 ```
 ### 4.3 Kombinasi Scoring Mode
 | Konfigurasi | Arti |
 |-------------|------|
 | `scoring_mode="ctt"` | Score akhir = NM, NN |
 | `scoring_mode="irt"` | Score akhir = theta × 200 + 500 |
 | `scoring_mode="hybrid"` | CTT score + IRT theta keduanya di-track |
 ---
 ## 5. IRT Calibration
 ### 5.1 Apa Itu Calibration?
 **IRT Calibration** adalah proses mengestimasi parameter `b` (difficulty) untuk setiap soal berdasarkan response data dari siswa.
 ### 5.2 Kapan Item Became Calibrated?
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │ SYARAT ITEM CALIBRATED                                      │
 ├─────────────────────────────────────────────────────────────┤
 │                                                             │
 │ 1. Minimum Response Sample                                  │
 │    └─> Ada cukup response data (default: 100 siswa)        │
 │                                                             │
 │ 2. IRT b Parameter                                          │
 │    └─> Sudah diestimasi via MLE                             │
 │                                                             │
 │ 3. IRT SE (Standard Error)                                  │
 │    └─> Sudah dihitung                                        │
 │                                                             │
 │ 4. Item.calibrated = True                                   │
 │                                                             │
 └─────────────────────────────────────────────────────────────┘
 ```
 ### 5.3 Flow IRT Calibration
 ```mermaid
 flowchart TD
    A[Collect Response Data] --> B{Have Min Sample?}
    B -->|No| C[Wait for more students]
    C --> A
    B -->|Yes| D[For each Item]
    D --> E[Build Response Matrix]
    E --> F[Estimate b via MLE]
    F --> G[Calculate Standard Error]
    G --> H[Update Item.irt_b]
    H --> I[Item.calibrated = True]
    I --> D
    D --> J[Calibration Complete]
 ```
 ### 5.4 Trigger Calibration
 Calibration bisa dipicu via:
 1. **API Endpoint:**
   ```
   POST /tryout/{tryout_id}/calibrate
   ```
 2. **Admin Panel:**
   - Buka `/admin` → Tryouts → Pilih tryout → Trigger calibration
 3. **Background Job (jika configured):**
   - Setelah enough responses terkumpul
 ---
 ## 6. Item Selection Modes
 ### 6.1 Fixed Selection
 **Fixed** = Soal disajikan berurutan berdasarkan slot.
 ```python
 # Flow:
 1. Siswa mulai session
 2. Ambil item dengan slot=1 (urutan terendah)
 3. Setelah dijawab, ambil slot=2
 4. Lanjutkan sampai selesai
 ```
 **Karakteristik:**
 - Predictable, urutan soal tetap
 - Tidak butuh IRT calibration
 - Semua siswa dapat soal sama di posisi sama
 ### 6.2 Adaptive Selection (CAT)
 **Adaptive** = Soal dipilih berdasarkan kemampuan siswa saat ini (theta).
 ```python
 # Flow:
 1. Siswa mulai session (θ = 0.0, default)
 2. Pilih item dengan b ≈ θ
 3. Siswa jawab → update θ
 4. Pilih item baru dengan b ≈ θ baru
 5. Ulangi sampai terminate condition
 ```
 **Karakteristik:**
 - Personalized, setiap siswa beda soal
 - Butuh item calibrated
 - Item selection pakai Fisher Information
 #### Fisher Information Formula:
 ```python
 # Information at current theta
 I(θ) = P(θ) × (1 - P(θ))
 # Di mana P(θ) = 1 / (1 + exp(-(θ - b)))
 # Item dengan MAX information dipilih
 # Maximum information = item paling informatif untuk theta saat ini
 ```
 ### 6.3 Hybrid Selection
 **Hybrid** = Gabungan fixed + adaptive.
 ```python
 # Flow:
 1. Slot 1-N: Fixed selection (sequential)
 2. Setelah slot N: Switch ke adaptive selection
 3. Theta sudah ter-update dari fixed portion
 4. Adaptive portion pakai theta untuk pilih soal
 ```
 **Parameter:**
 - `hybrid_transition_slot` = Slot dimana switch ke adaptive
 ### 6.4 Perbandingan Selection Modes
 | Mode | Butuh Calibration | Personalisasi | Predictable |
 |------|-------------------|---------------|-------------|
 | Fixed | Tidak | Tidak | Ya |
 | Adaptive | Ya | Ya | Tidak |
 | Hybrid | Parsial | Parsial | Parsial |
 ---
 ## 7. Student Session Flow
 ### 7.1 Full Student Flow
 ```mermaid
 sequenceDiagram
    participant S as Student
    participant API as FastAPI
    participant DB as Database
    S->>API: POST /session/ (start session)
    API->>DB: Create session, θ=0.0
    DB-->>API: session_id
    API-->>S: session_id
    loop For each question (adaptive/fixed/hybrid)
        S->>API: GET /session/{id}/next-item
        API->>DB: Query next item based on selection_mode
        DB-->>API: Item data
        API-->>S: Question
        S->>API: POST /session/{id}/answer
        API->>API: Update θ (if adaptive)
        API->>DB: Save UserAnswer
        DB-->>API: Saved
        API-->>S: Ack + next question
    end
    S->>API: POST /session/{id}/complete
    API->>API: Calculate NM, NN, final theta
    API->>DB: Update session
    DB-->>API: Updated
    API-->>S: Final scores
 ```
 ### 7.2 Next-Item Selection Berdasarkan Mode
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │ SELECTION MODE = FIXED                                       │
 ├─────────────────────────────────────────────────────────────┤
 │                                                             │
 │  SELECT * FROM items                                        │
 │  WHERE tryout_id = ?                                        │
 │    AND item.id NOT IN (answered_items)                      │
 │  ORDER BY slot ASC                                          │
 │  LIMIT 1                                                    │
 │                                                             │
 │  Result: Item dengan slot terkecil yang belum dijawab      │
 │                                                             │
 └─────────────────────────────────────────────────────────────┘
 ┌─────────────────────────────────────────────────────────────┐
 │ SELECTION MODE = ADAPTIVE                                    │
 ├─────────────────────────────────────────────────────────────┤
 │                                                             │
 │  current_theta = session.theta  -- e.g., 0.5               │
 │                                                             │
 │  SELECT * FROM items                                        │
 │  WHERE tryout_id = ?                                        │
 │    AND calibrated = TRUE                                    │
 │    AND item.id NOT IN (answered_items)                      │
 │  ORDER BY ABS(irt_b - current_theta) ASC  -- terdekat     │
 │  LIMIT 1                                                    │
 │                                                             │
 │  Result: Item dengan b ≈ θ                                  │
 │                                                             │
 └─────────────────────────────────────────────────────────────┘
 ```
 ---
 ## 8. Konfigurasi Tryout
 ### 8.1 Semua Opsi Konfigurasi
 ```python
 # Scoring
 scoring_mode = "ctt"        # ctt, irt, hybrid
 scoring_mode = "irt"        # 
 scoring_mode = "hybrid"     # 
 # Selection
 selection_mode = "fixed"    # Sequential
 selection_mode = "adaptive" # CAT based on theta
 selection_mode = "hybrid"   # Fixed until transition slot
 # Normalization
 normalization_mode = "static"   # Use static_rataan, static_sb
 normalization_mode = "dynamic"  # Calculate from participant data
 normalization_mode = "hybrid"   # Dynamic when min_sample reached
 # IRT Settings
 min_calibration_sample = 100      # Min responses for calibration
 theta_estimation_method = "mle"   # mle, map, eap
 fallback_to_ctt_on_error = True   # Fallback if IRT fails
 # Hybrid Settings
 hybrid_transition_slot = 10       # Switch to adaptive at slot 10
 # AI Settings
 ai_generation_enabled = True      # Allow AI generated items
 ```
 ### 8.2 Cara Mengubah Konfigurasi
 #### Via Database:
 ```sql
 UPDATE tryouts 
 SET 
    scoring_mode = 'hybrid',
    selection_mode = 'adaptive',
    normalization_mode = 'dynamic'
 WHERE tryout_id = 'your-tryout-id';
 ```
 #### Via Admin Panel:
 1. Buka `/admin`
 2. Pilih menu **Tryouts**
 3. Edit tryout yang diinginkan
 4. Ubah field-field sesuai kebutuhan
 5. Save
 ---
 ## 9. Ringkasan Alur End-to-End
 ### 9.1 Admin Flow (Sekali / Periodik)
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │ 1. IMPORT TRYOUT JSON                                       │
 │    Input: File JSON (1 tryout = 1 exam)                     │
 │    Output: N items dalam database                           │
 │                                                             │
 │ 2. AI GENERATE VARIANTS                                     │
 │    Input: Item original                                     │
 │    Output: Item variant (same slot, different content)      │
 │    Result: 2N items (N slot × 2 variant)                   │
 │                                                             │
 │ 3. COLLECT RESPONSE DATA                                    │
 │    Input: Student answers                                   │
 │    Output: UserAnswer records                               │
 │                                                             │
 │ 4. IRT CALIBRATION                                          │
 │    Input: Response data (min 100 students)                  │
 │    Output: Item.irt_b, Item.irt_se, Item.calibrated=True    │
 │                                                             │
 │ 5. CONFIGURE TRYOUT                                         │
 │    Input: Set selection_mode = 'adaptive'                   │
 │    Output: Tryout siap untuk adaptive testing               │
 │                                                             │
 └─────────────────────────────────────────────────────────────┘
 ```
 ### 9.2 Student Flow (Setiap Ujian)
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │ 1. START SESSION                                            │
 │    Input: tryout_id                                         │
 │    Output: session_id, theta=0.0                            │
 │                                                             │
 │ 2. ANSWER LOOP                                              │
 │    For each question:                                       │
 │      - Get next item (based on selection_mode)             │
 │      - Submit answer                                        │
 │      - If adaptive: update theta                           │
 │                                                             │
 │ 3. COMPLETE SESSION                                         │
 │    Input: All answers                                       │
 │    Output: NM, NN, theta, completion status                 │
 │                                                             │
 └─────────────────────────────────────────────────────────────┘
 ```
 ### 9.3 Konsep Kunci
 | Konsep | Penjelasan |
 |--------|------------|
 | **Tryout** | 1 exam yang di-import dari JSON |
 | **Item** | 1 soal (original atau variant) |
 | **Slot** | Posisi nomor soal (1, 2, 3...) |
 | **Variant** | Soal berbeda di slot yang sama |
 | **Calibrated** | Item sudah punya irt_b (siap untuk adaptive) |
 | **Theta** | Estimasi kemampuan siswa dalam IRT scale |
 ---
 ## 10. FAQ
 ### Q: Kenapa default scoring_mode = "ctt"?
 A: CTT lebih simpel, tidak butuh IRT calibration. Cocok untuk awal sebelum cukup data.
 ### Q: Kenapa default selection_mode = "fixed"?
 A: Fixed selection tidak butuh item calibrated. Bisa jalan langsung setelah import.
 ### Q: Bagaimana switch ke adaptive?
 A: 
 1. Pastikan item sudah calibrated (`calibrated = True`)
 2. Ubah `selection_mode = 'adaptive'` di tryout
 3. Student baru akan dapat adaptive selection
 ### Q: Adaptive butuh berapa banyak data?
 A: Default `min_calibration_sample = 100`. Artinya minimal 100 siswa harus sudah menjawab sebelum calibration bisa jalan.
 ### Q: CTT dan Fixed itu sama?
 A: Tidak. Mereka orthogonal:
 - **scoring_mode** = bagaimana menghitung score akhir
 - **selection_mode** = bagaimana memilih soal berikutnya
 ### Q: Aplikasi ini membuat exam?
 A: Tidak. Aplikasi ini adalah **question bank**. Exam sudah di-import dari JSON. Aplikasi "mengembangbiakkan" soal dengan membuat variants.
 ---
 ## 11. Referensi Code
 | File | Fungsi |
 |------|--------|
 | `app/services/ctt_scoring.py` | CTT scoring calculations |
 | `app/services/irt_calibration.py` | IRT calibration, theta estimation |
 | `app/services/cat_selection.py` | Item selection (fixed/adaptive/hybrid) |
 | `app/services/ai_generation.py` | OpenRouter AI integration |
 | `app/services/excel_import.py` | Excel import/export |
 | `app/routers/sessions.py` | Session management API |
 | `app/models/tryout.py` | Tryout model definition |
 | `app/models/item.py` | Item model definition |
 | `app/models/session.py` | Session model definition |
 ---
 *Document version: 1.0*
 *Last updated: 2026-06-15*
--- a/error.html
+++ b/error.html
--- a/patch_css.py
+++ b/patch_css.py
@@ -0,0 +1,19 @@
 import re
 with open("app/admin_web.py", "r") as f:
    content = f.read()
 # Fix activity feed CSS
 content = content.replace(
    ".activity-feed li:last-child {{ border-bottom: none; }}",
    ".activity-feed li:last-child {{ border-bottom: none; }}\n    .activity-feed li svg, .activity-feed li svg.nav-icon, .activity-feed li svg.huge-icon {{ width: 20px; height: 20px; flex-shrink: 0; }}"
 )
 # Fix alert CSS
 content = content.replace(
    ".alert-warning {{ background: #fef3c7; border: 1px solid #f59e0b; color: #92400e; }}",
    ".alert svg, .alert svg.huge-icon, .alert svg.page-icon {{ width: 24px; height: 24px; flex-shrink: 0; margin-right: 4px; margin-bottom: -4px; }}\n    .alert-warning {{ background: #fef3c7; border: 1px solid #f59e0b; color: #92400e; }}"
 )
 with open("app/admin_web.py", "w") as f:
    f.write(content)
--- a/patch_icons.py
+++ b/patch_icons.py
@@ -0,0 +1,17 @@
 import re
 with open("app/admin_web_icons.py", "r") as f:
    content = f.read()
 new_mappings = """    "📈": ICON_TREND_UP,
    "📉": ICON_TREND_DOWN,
    "💡": ICON_LIGHTBULB,
    "👋": '<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" style="display:inline;width:28px;height:28px;margin-bottom:-4px;"><path stroke-linecap="round" stroke-linejoin="round" d="M15.042 21.672 13.684 16.6m0 0-2.51 2.225.569-9.47 5.227 7.917-3.286-.672ZM12 2.25V4.5m5.834.166-1.591 1.591M20.25 10.5H18M7.757 14.743l-1.59 1.59M6 10.5H3.75m4.007-4.243-1.59-1.59" /></svg>',
    "📊": ICON_REPORTS,
    "🚀": ICON_HUGE_ROCKET,
    "📈": ICON_TREND_UP,"""
 content = content.replace('    "📈": ICON_TREND_UP,\n    "📉": ICON_TREND_DOWN,', new_mappings)
 with open("app/admin_web_icons.py", "w") as f:
    f.write(content)
--- a/requirements.txt
+++ b/requirements.txt
@@ -38,3 +38,6 @@ fastapi-admin>=1.0.0
 # Utilities
 python-dotenv>=1.0.0
 # Async support
 greenlet>=2.0.0
--- a/run_local.sh
+++ b/run_local.sh
@@ -0,0 +1,66 @@
 #!/bin/bash
 # Run local development server
 set -e
 echo "🚀 Starting IRT Bank Soal Local Dev Server"
 echo "=========================================="
 # Check if Docker is available
 if ! command -v docker &> /dev/null; then
    echo "❌ Docker not found. Please install Docker first."
    exit 1
 fi
 # Check if docker-compose is available
 if ! command -v docker-compose &> /dev/null && ! docker compose version &> /dev/null; then
    echo "❌ Docker Compose not found. Please install Docker Compose first."
    exit 1
 fi
 # Use docker compose command (Docker Desktop includes it as a plugin)
 DOCKER_COMPOSE="docker compose"
 # Start databases
 echo "📦 Starting PostgreSQL and Redis..."
 $DOCKER_COMPOSE -f docker-compose.dev.yml up -d postgres redis
 # Wait for PostgreSQL to be ready
 echo "⏳ Waiting for PostgreSQL..."
 for i in {1..60}; do
    if docker exec yellow-bank-soal-postgres-1 pg_isready -U irt_user -d irt_bank_soal &> /dev/null 2>&1; then
        echo "✅ PostgreSQL is ready!"
        break
    fi
    if [ $i -eq 60 ]; then
        echo "❌ PostgreSQL failed to start"
        docker logs yellow-bank-soal-postgres-1
        exit 1
    fi
    sleep 1
 done
 # Check if venv exists, create if not
 if [ ! -d "venv" ]; then
    echo "📦 Creating Python virtual environment..."
    python3 -m venv venv
 fi
 # Activate venv and install dependencies
 echo "📦 Installing dependencies..."
 source venv/bin/activate
 pip install -r requirements.txt -q
 # Run migrations
 echo "🔄 Running database migrations..."
 alembic upgrade head
 # Start the dev server
 echo ""
 echo "🎉 Starting FastAPI dev server..."
 echo "   Admin UI: http://localhost:8000/admin"
 echo "   API Docs: http://localhost:8000/docs"
 echo "   Login: admin / admin123"
 echo ""
 echo "=========================================="
 exec uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
--- a/test_error.py
+++ b/test_error.py
@@ -0,0 +1,8 @@
 import asyncio
 from fastapi.testclient import TestClient
 from app.main import app
 client = TestClient(app)
 response = client.get("/admin/hierarchy")
 print(response.status_code)
 print(response.text)
--- a/test_fetch.py
+++ b/test_fetch.py
@@ -0,0 +1,9 @@
 import asyncio
 from fastapi.testclient import TestClient
 from app.main import app
 client = TestClient(app)
 client.post("/admin/login", data={"username": "admin", "password": "password"})
 response = client.get("/admin/hierarchy")
 print(response.status_code)
 print(response.text)