feat: Add AI usage monitoring dashboard

- Add AIUsageLog, AIUsageDaily, AIRateLimit models to database.py
- Update gemini_service.py to log to new AIUsageLog table
- Create /admin/ai-usage dashboard with stats and charts
- Show daily/weekly/monthly requests, tokens, costs
- Track usage by type (chat, news_evaluation, etc.)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

app.py

@@ -5719,6 +5719,130 @@ def chat_analytics():
         db.close()
 
 
+@app.route('/admin/ai-usage')
+@login_required
+def admin_ai_usage():
+    """Admin dashboard for AI (Gemini) API usage monitoring"""
+    if not current_user.is_admin:
+        flash('Brak uprawnień do tej strony.', 'error')
+        return redirect(url_for('dashboard'))
+
+    from database import AIUsageLog, AIUsageDaily
+    from sqlalchemy import func, desc
+    from datetime import timedelta
+
+    db = SessionLocal()
+    try:
+        now = datetime.now()
+        today = now.date()
+        week_ago = today - timedelta(days=7)
+        month_ago = today - timedelta(days=30)
+        day_ago = now - timedelta(hours=24)
+
+        # Today's stats
+        today_stats = db.query(
+            func.count(AIUsageLog.id).label('requests'),
+            func.coalesce(func.sum(AIUsageLog.tokens_input), 0).label('tokens_input'),
+            func.coalesce(func.sum(AIUsageLog.tokens_output), 0).label('tokens_output'),
+            func.coalesce(func.sum(AIUsageLog.cost_cents), 0).label('cost_cents')
+        ).filter(
+            func.date(AIUsageLog.created_at) == today
+        ).first()
+
+        # Week stats
+        week_requests = db.query(func.count(AIUsageLog.id)).filter(
+            func.date(AIUsageLog.created_at) >= week_ago
+        ).scalar() or 0
+
+        # Month stats
+        month_stats = db.query(
+            func.count(AIUsageLog.id).label('requests'),
+            func.coalesce(func.sum(AIUsageLog.cost_cents), 0).label('cost_cents')
+        ).filter(
+            func.date(AIUsageLog.created_at) >= month_ago
+        ).first()
+
+        # Error rate (last 24h)
+        last_24h_total = db.query(func.count(AIUsageLog.id)).filter(
+            AIUsageLog.created_at >= day_ago
+        ).scalar() or 0
+
+        last_24h_errors = db.query(func.count(AIUsageLog.id)).filter(
+            AIUsageLog.created_at >= day_ago,
+            AIUsageLog.success == False
+        ).scalar() or 0
+
+        error_rate = (last_24h_errors / last_24h_total * 100) if last_24h_total > 0 else 0
+
+        # Average response time (last 24h)
+        avg_response_time = db.query(func.avg(AIUsageLog.response_time_ms)).filter(
+            AIUsageLog.created_at >= day_ago,
+            AIUsageLog.success == True
+        ).scalar() or 0
+
+        # Usage by type (last 30 days)
+        type_stats = db.query(
+            AIUsageLog.request_type,
+            func.count(AIUsageLog.id).label('count')
+        ).filter(
+            func.date(AIUsageLog.created_at) >= month_ago
+        ).group_by(AIUsageLog.request_type).order_by(desc('count')).all()
+
+        # Calculate percentages for type breakdown
+        total_type_count = sum(t.count for t in type_stats) if type_stats else 0
+        type_labels = {
+            'chat': ('Chat AI', 'chat'),
+            'news_evaluation': ('Ocena newsów', 'news'),
+            'user_creation': ('Tworzenie user', 'user'),
+            'image_analysis': ('Analiza obrazu', 'image'),
+            'general': ('Ogólne', 'other')
+        }
+        usage_by_type = []
+        for t in type_stats:
+            label, css_class = type_labels.get(t.request_type, (t.request_type, 'other'))
+            percentage = (t.count / total_type_count * 100) if total_type_count > 0 else 0
+            usage_by_type.append({
+                'type': t.request_type,
+                'type_label': label,
+                'type_class': css_class,
+                'count': t.count,
+                'percentage': round(percentage, 1)
+            })
+
+        # Recent logs
+        recent_logs = db.query(AIUsageLog).order_by(desc(AIUsageLog.created_at)).limit(20).all()
+        for log in recent_logs:
+            label, _ = type_labels.get(log.request_type, (log.request_type, 'other'))
+            log.type_label = label
+
+        # Daily history (last 14 days)
+        daily_history = db.query(AIUsageDaily).filter(
+            AIUsageDaily.date >= today - timedelta(days=14)
+        ).order_by(desc(AIUsageDaily.date)).all()
+
+        stats = {
+            'today_requests': today_stats.requests or 0,
+            'today_tokens_input': int(today_stats.tokens_input) or 0,
+            'today_tokens_output': int(today_stats.tokens_output) or 0,
+            'today_cost': float(today_stats.cost_cents or 0) / 100,
+            'week_requests': week_requests,
+            'month_requests': month_stats.requests or 0,
+            'month_cost': float(month_stats.cost_cents or 0) / 100,
+            'error_rate': error_rate,
+            'avg_response_time': int(avg_response_time)
+        }
+
+        return render_template(
+            'admin/ai_usage_dashboard.html',
+            stats=stats,
+            usage_by_type=usage_by_type,
+            recent_logs=recent_logs,
+            daily_history=daily_history
+        )
+    finally:
+        db.close()
+
+
 @app.route('/api/admin/chat-stats')
 @login_required
 def api_chat_stats():

database.py

@@ -16,10 +16,11 @@ Models:
 - GBPAudit: Google Business Profile audit results
 - ITAudit: IT infrastructure audit results
 - ITCollaborationMatch: IT collaboration matches between companies
+- AIUsageLog, AIUsageDaily, AIRateLimit: AI API usage monitoring
 
 Author: Norda Biznes Development Team
 Created: 2025-11-23
-Updated: 2026-01-09 (IT Audit Tool, IT Collaboration Matching)
+Updated: 2026-01-11 (AI Usage Tracking)
 """
 
 import os
@@ -1941,6 +1942,118 @@ class ZOPKNewsFetchJob(Base):
     user = relationship('User', foreign_keys=[triggered_by_user])
 
 
+# ============================================================
+# AI USAGE TRACKING MODELS
+# ============================================================
+
+class AIUsageLog(Base):
+    """
+    Individual AI API call logs.
+    Tracks tokens, costs, and performance for each Gemini API request.
+    """
+    __tablename__ = 'ai_usage_logs'
+
+    id = Column(Integer, primary_key=True)
+
+    # Request info
+    request_type = Column(String(50), nullable=False)  # chat, news_evaluation, user_creation, image_analysis
+    model = Column(String(100), nullable=False)  # gemini-2.0-flash, gemini-1.5-pro, etc.
+
+    # Token counts
+    tokens_input = Column(Integer, default=0)
+    tokens_output = Column(Integer, default=0)
+    # Note: tokens_total is a generated column in PostgreSQL
+
+    # Cost (in USD cents for precision)
+    cost_cents = Column(Numeric(10, 4), default=0)
+
+    # Context
+    user_id = Column(Integer, ForeignKey('users.id'))
+    company_id = Column(Integer, ForeignKey('companies.id'))
+    related_entity_type = Column(String(50))  # zopk_news, chat_message, company, etc.
+    related_entity_id = Column(Integer)
+
+    # Request details
+    prompt_length = Column(Integer)
+    response_length = Column(Integer)
+    response_time_ms = Column(Integer)  # How long the API call took
+
+    # Status
+    success = Column(Boolean, default=True)
+    error_message = Column(Text)
+
+    # Timestamps
+    created_at = Column(DateTime, default=datetime.now)
+
+    # Relationships
+    user = relationship('User', foreign_keys=[user_id])
+    company = relationship('Company', foreign_keys=[company_id])
+
+
+class AIUsageDaily(Base):
+    """
+    Pre-aggregated daily AI usage statistics.
+    Auto-updated by PostgreSQL trigger on ai_usage_logs insert.
+    """
+    __tablename__ = 'ai_usage_daily'
+
+    id = Column(Integer, primary_key=True)
+    date = Column(Date, unique=True, nullable=False)
+
+    # Request counts by type
+    chat_requests = Column(Integer, default=0)
+    news_evaluation_requests = Column(Integer, default=0)
+    user_creation_requests = Column(Integer, default=0)
+    image_analysis_requests = Column(Integer, default=0)
+    other_requests = Column(Integer, default=0)
+    total_requests = Column(Integer, default=0)
+
+    # Token totals
+    total_tokens_input = Column(Integer, default=0)
+    total_tokens_output = Column(Integer, default=0)
+    total_tokens = Column(Integer, default=0)
+
+    # Cost (in USD cents)
+    total_cost_cents = Column(Numeric(10, 4), default=0)
+
+    # Performance
+    avg_response_time_ms = Column(Integer)
+    error_count = Column(Integer, default=0)
+
+    # Timestamps
+    created_at = Column(DateTime, default=datetime.now)
+    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
+
+
+class AIRateLimit(Base):
+    """
+    Rate limit tracking for AI API quota management.
+    """
+    __tablename__ = 'ai_rate_limits'
+
+    id = Column(Integer, primary_key=True)
+
+    # Limit type
+    limit_type = Column(String(50), nullable=False)  # daily, hourly, per_minute
+    limit_scope = Column(String(50), nullable=False)  # global, user, ip
+    scope_identifier = Column(String(255))  # user_id, ip address, or NULL for global
+
+    # Limits
+    max_requests = Column(Integer, nullable=False)
+    current_requests = Column(Integer, default=0)
+
+    # Reset
+    reset_at = Column(DateTime, nullable=False)
+
+    # Timestamps
+    created_at = Column(DateTime, default=datetime.now)
+    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
+
+    __table_args__ = (
+        UniqueConstraint('limit_type', 'limit_scope', 'scope_identifier', name='uq_rate_limit'),
+    )
+
+
 # ============================================================
 # DATABASE INITIALIZATION
 # ============================================================

gemini_service.py

@@ -28,7 +28,7 @@ logger = logging.getLogger(__name__)
 
 # Database imports for cost tracking
 try:
-    from database import SessionLocal, AIAPICostLog
+    from database import SessionLocal, AIAPICostLog, AIUsageLog
     DB_AVAILABLE = True
 except ImportError:
     logger.warning("Database not available - cost tracking disabled")
@@ -101,7 +101,10 @@ class GeminiService:
         max_tokens: Optional[int] = None,
         stream: bool = False,
         feature: str = 'general',
-        user_id: Optional[int] = None
+        user_id: Optional[int] = None,
+        company_id: Optional[int] = None,
+        related_entity_type: Optional[str] = None,
+        related_entity_id: Optional[int] = None
     ) -> str:
         """
         Generate text using Gemini API with automatic cost tracking.
@@ -111,10 +114,11 @@ class GeminiService:
             temperature: Sampling temperature (0.0-1.0). Higher = more creative
             max_tokens: Maximum tokens to generate (None = model default)
             stream: Whether to stream the response
-            feature: Feature name for cost tracking ('ai_coach', 'ai_nutrition', etc.)
+            feature: Feature name for cost tracking ('chat', 'news_evaluation', etc.)
             user_id: Optional user ID for cost tracking
-            user_id: Optional rider ID for cost tracking
+            company_id: Optional company ID for context
-            event_id: Optional event ID for cost tracking
+            related_entity_type: Entity type ('zopk_news', 'chat_message', etc.)
+            related_entity_id: Entity ID for reference
 
         Returns:
             Generated text response
@@ -191,7 +195,10 @@ class GeminiService:
                 latency_ms=latency_ms,
                 success=True,
                 feature=feature,
-                user_id=user_id
+                user_id=user_id,
+                company_id=company_id,
+                related_entity_type=related_entity_type,
+                related_entity_id=related_entity_id
             )
 
             return response_text
@@ -209,7 +216,10 @@ class GeminiService:
                 success=False,
                 error_message=str(e),
                 feature=feature,
-                user_id=user_id
+                user_id=user_id,
+                company_id=company_id,
+                related_entity_type=related_entity_type,
+                related_entity_id=related_entity_id
             )
 
             logger.error(f"Gemini API error: {str(e)}")
@@ -302,7 +312,10 @@ class GeminiService:
         success: bool = True,
         error_message: Optional[str] = None,
         feature: str = 'general',
-        user_id: Optional[int] = None
+        user_id: Optional[int] = None,
+        company_id: Optional[int] = None,
+        related_entity_type: Optional[str] = None,
+        related_entity_id: Optional[int] = None
     ):
         """
         Log API call costs to database for monitoring
@@ -315,10 +328,11 @@ class GeminiService:
             latency_ms: Response time in milliseconds
             success: Whether API call succeeded
             error_message: Error details if failed
-            feature: Feature name ('ai_coach', 'ai_nutrition', 'test', etc.)
+            feature: Feature name ('chat', 'news_evaluation', 'user_creation', etc.)
             user_id: Optional user ID
-            user_id: Optional rider ID
+            company_id: Optional company ID for context
-            event_id: Optional event ID
+            related_entity_type: Entity type ('zopk_news', 'chat_message', etc.)
+            related_entity_id: Entity ID for reference
         """
         if not DB_AVAILABLE:
             return
@@ -330,13 +344,17 @@ class GeminiService:
             output_cost = (output_tokens / 1_000_000) * pricing['output']
             total_cost = input_cost + output_cost
 
+            # Cost in cents for AIUsageLog (more precise)
+            cost_cents = total_cost * 100
+
             # Create prompt hash (for debugging, not storing full prompt for privacy)
             prompt_hash = hashlib.sha256(prompt.encode()).hexdigest()
 
             # Save to database
             db = SessionLocal()
             try:
-                log_entry = AIAPICostLog(
+                # Log to legacy AIAPICostLog table
+                legacy_log = AIAPICostLog(
                     timestamp=datetime.now(),
                     api_provider='gemini',
                     model_name=self.model_name,
@@ -353,7 +371,27 @@ class GeminiService:
                     latency_ms=latency_ms,
                     prompt_hash=prompt_hash
                 )
-                db.add(log_entry)
+                db.add(legacy_log)
+
+                # Log to new AIUsageLog table (with automatic daily aggregation via trigger)
+                usage_log = AIUsageLog(
+                    request_type=feature,
+                    model=self.model_name,
+                    tokens_input=input_tokens,
+                    tokens_output=output_tokens,
+                    cost_cents=cost_cents,
+                    user_id=user_id,
+                    company_id=company_id,
+                    related_entity_type=related_entity_type,
+                    related_entity_id=related_entity_id,
+                    prompt_length=len(prompt),
+                    response_length=len(response_text),
+                    response_time_ms=latency_ms,
+                    success=success,
+                    error_message=error_message
+                )
+                db.add(usage_log)
+
                 db.commit()
 
                 logger.info(

templates/admin/ai_usage_dashboard.html

@@ -0,0 +1,432 @@
+{% extends "base.html" %}
+
+{% block title %}Monitoring AI - Panel Admina{% endblock %}
+
+{% block extra_css %}
+<style>
+    .page-header {
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+        margin-bottom: var(--spacing-xl);
+    }
+
+    .page-header h1 {
+        font-size: var(--font-size-2xl);
+    }
+
+    .stats-grid {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(180px, 1fr));
+        gap: var(--spacing-lg);
+        margin-bottom: var(--spacing-2xl);
+    }
+
+    .stat-card {
+        background: var(--surface);
+        padding: var(--spacing-lg);
+        border-radius: var(--radius-lg);
+        box-shadow: var(--shadow);
+        text-align: center;
+    }
+
+    .stat-card.highlight {
+        border-left: 4px solid var(--primary);
+    }
+
+    .stat-card.warning {
+        border-left: 4px solid var(--warning);
+    }
+
+    .stat-card.success {
+        border-left: 4px solid var(--success);
+    }
+
+    .stat-card.error {
+        border-left: 4px solid var(--error);
+    }
+
+    .stat-value {
+        font-size: var(--font-size-3xl);
+        font-weight: 700;
+        color: var(--text-primary);
+    }
+
+    .stat-value.primary { color: var(--primary); }
+    .stat-value.success { color: var(--success); }
+    .stat-value.warning { color: var(--warning); }
+    .stat-value.error { color: var(--error); }
+
+    .stat-label {
+        color: var(--text-secondary);
+        font-size: var(--font-size-sm);
+        margin-top: var(--spacing-xs);
+    }
+
+    .stat-sublabel {
+        color: var(--text-muted);
+        font-size: var(--font-size-xs);
+        margin-top: 2px;
+    }
+
+    .section {
+        background: var(--surface);
+        padding: var(--spacing-xl);
+        border-radius: var(--radius-lg);
+        box-shadow: var(--shadow);
+        margin-bottom: var(--spacing-xl);
+    }
+
+    .section h2 {
+        font-size: var(--font-size-xl);
+        margin-bottom: var(--spacing-lg);
+        color: var(--text-primary);
+        border-bottom: 2px solid var(--border);
+        padding-bottom: var(--spacing-sm);
+        display: flex;
+        align-items: center;
+        gap: var(--spacing-sm);
+    }
+
+    .section h2 .icon {
+        font-size: var(--font-size-2xl);
+    }
+
+    /* Usage breakdown bars */
+    .usage-breakdown {
+        display: flex;
+        flex-direction: column;
+        gap: var(--spacing-md);
+    }
+
+    .usage-row {
+        display: flex;
+        align-items: center;
+        gap: var(--spacing-md);
+    }
+
+    .usage-label {
+        width: 150px;
+        font-size: var(--font-size-sm);
+        color: var(--text-secondary);
+    }
+
+    .usage-bar-container {
+        flex: 1;
+        height: 24px;
+        background: var(--background);
+        border-radius: var(--radius);
+        overflow: hidden;
+    }
+
+    .usage-bar {
+        height: 100%;
+        border-radius: var(--radius);
+        display: flex;
+        align-items: center;
+        justify-content: flex-end;
+        padding-right: var(--spacing-sm);
+        font-size: var(--font-size-xs);
+        font-weight: 600;
+        color: white;
+        min-width: 40px;
+        transition: width 0.3s ease;
+    }
+
+    .usage-bar.chat { background: #3b82f6; }
+    .usage-bar.news { background: #10b981; }
+    .usage-bar.user { background: #f59e0b; }
+    .usage-bar.image { background: #8b5cf6; }
+    .usage-bar.other { background: #6b7280; }
+
+    .usage-count {
+        width: 80px;
+        text-align: right;
+        font-weight: 600;
+        font-size: var(--font-size-sm);
+    }
+
+    /* Daily history table */
+    .daily-table {
+        width: 100%;
+        border-collapse: collapse;
+    }
+
+    .daily-table th,
+    .daily-table td {
+        padding: var(--spacing-md);
+        text-align: left;
+        border-bottom: 1px solid var(--border);
+    }
+
+    .daily-table th {
+        background: var(--background);
+        font-weight: 600;
+        font-size: var(--font-size-sm);
+        color: var(--text-secondary);
+    }
+
+    .daily-table tr:hover {
+        background: var(--background);
+    }
+
+    .cost-badge {
+        display: inline-block;
+        padding: 2px 8px;
+        border-radius: var(--radius-sm);
+        font-size: var(--font-size-xs);
+        font-weight: 500;
+        background: #dcfce7;
+        color: #166534;
+    }
+
+    .cost-badge.medium {
+        background: #fef3c7;
+        color: #92400e;
+    }
+
+    .cost-badge.high {
+        background: #fee2e2;
+        color: #991b1b;
+    }
+
+    /* Recent logs */
+    .log-list {
+        list-style: none;
+    }
+
+    .log-item {
+        padding: var(--spacing-md);
+        border-bottom: 1px solid var(--border);
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+    }
+
+    .log-item:last-child {
+        border-bottom: none;
+    }
+
+    .log-type {
+        display: inline-block;
+        padding: 2px 8px;
+        border-radius: var(--radius-sm);
+        font-size: var(--font-size-xs);
+        font-weight: 500;
+    }
+
+    .log-type.chat { background: #dbeafe; color: #1d4ed8; }
+    .log-type.news_evaluation { background: #d1fae5; color: #065f46; }
+    .log-type.user_creation { background: #fef3c7; color: #92400e; }
+    .log-type.image_analysis { background: #ede9fe; color: #5b21b6; }
+    .log-type.other { background: #f3f4f6; color: #374151; }
+
+    .log-meta {
+        display: flex;
+        gap: var(--spacing-lg);
+        color: var(--text-secondary);
+        font-size: var(--font-size-sm);
+    }
+
+    .log-tokens {
+        font-family: monospace;
+    }
+
+    .log-time {
+        color: var(--text-muted);
+    }
+
+    .log-error {
+        color: var(--error);
+        font-size: var(--font-size-xs);
+        margin-top: 4px;
+    }
+
+    /* No data state */
+    .empty-state {
+        text-align: center;
+        padding: var(--spacing-2xl);
+        color: var(--text-secondary);
+    }
+
+    .empty-state .icon {
+        font-size: 48px;
+        margin-bottom: var(--spacing-md);
+    }
+
+    /* Two column layout for sections */
+    .sections-grid {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(400px, 1fr));
+        gap: var(--spacing-xl);
+    }
+
+    @media (max-width: 768px) {
+        .sections-grid {
+            grid-template-columns: 1fr;
+        }
+        .usage-label {
+            width: 100px;
+        }
+    }
+</style>
+{% endblock %}
+
+{% block content %}
+<div class="page-header">
+    <div>
+        <h1>Monitoring AI</h1>
+        <p class="text-muted">Statystyki wykorzystania Gemini API</p>
+    </div>
+    <a href="{{ url_for('admin_dashboard') }}" class="btn btn-secondary">Powrót</a>
+</div>
+
+<!-- Main Stats -->
+<div class="stats-grid">
+    <div class="stat-card highlight">
+        <div class="stat-value primary">{{ stats.today_requests }}</div>
+        <div class="stat-label">Dzisiaj</div>
+        <div class="stat-sublabel">zapytań do AI</div>
+    </div>
+    <div class="stat-card">
+        <div class="stat-value">{{ stats.week_requests }}</div>
+        <div class="stat-label">Ten tydzień</div>
+        <div class="stat-sublabel">zapytań</div>
+    </div>
+    <div class="stat-card">
+        <div class="stat-value">{{ stats.month_requests }}</div>
+        <div class="stat-label">Ten miesiąc</div>
+        <div class="stat-sublabel">zapytań</div>
+    </div>
+    <div class="stat-card success">
+        <div class="stat-value success">${{ "%.4f"|format(stats.today_cost) }}</div>
+        <div class="stat-label">Koszt dziś</div>
+        <div class="stat-sublabel">USD</div>
+    </div>
+    <div class="stat-card warning">
+        <div class="stat-value warning">${{ "%.4f"|format(stats.month_cost) }}</div>
+        <div class="stat-label">Koszt miesiąc</div>
+        <div class="stat-sublabel">USD</div>
+    </div>
+    <div class="stat-card {% if stats.error_rate > 10 %}error{% elif stats.error_rate > 5 %}warning{% else %}success{% endif %}">
+        <div class="stat-value {% if stats.error_rate > 10 %}error{% elif stats.error_rate > 5 %}warning{% else %}success{% endif %}">{{ "%.1f"|format(stats.error_rate) }}%</div>
+        <div class="stat-label">Błędy</div>
+        <div class="stat-sublabel">ostatnie 24h</div>
+    </div>
+</div>
+
+<!-- Token Stats -->
+<div class="stats-grid">
+    <div class="stat-card">
+        <div class="stat-value">{{ "{:,}".format(stats.today_tokens_input) }}</div>
+        <div class="stat-label">Tokeny input</div>
+        <div class="stat-sublabel">dzisiaj</div>
+    </div>
+    <div class="stat-card">
+        <div class="stat-value">{{ "{:,}".format(stats.today_tokens_output) }}</div>
+        <div class="stat-label">Tokeny output</div>
+        <div class="stat-sublabel">dzisiaj</div>
+    </div>
+    <div class="stat-card">
+        <div class="stat-value">{{ stats.avg_response_time }}ms</div>
+        <div class="stat-label">Avg czas odpowiedzi</div>
+        <div class="stat-sublabel">ostatnie 24h</div>
+    </div>
+</div>
+
+<div class="sections-grid">
+    <!-- Usage by Type -->
+    <div class="section">
+        <h2><span class="icon">📊</span> Wykorzystanie wg typu</h2>
+        {% if usage_by_type %}
+        <div class="usage-breakdown">
+            {% for item in usage_by_type %}
+            <div class="usage-row">
+                <div class="usage-label">{{ item.type_label }}</div>
+                <div class="usage-bar-container">
+                    <div class="usage-bar {{ item.type_class }}" style="width: {{ item.percentage }}%">
+                        {{ item.percentage }}%
+                    </div>
+                </div>
+                <div class="usage-count">{{ item.count }}</div>
+            </div>
+            {% endfor %}
+        </div>
+        {% else %}
+        <div class="empty-state">
+            <div class="icon">📭</div>
+            <p>Brak danych</p>
+        </div>
+        {% endif %}
+    </div>
+
+    <!-- Recent Logs -->
+    <div class="section">
+        <h2><span class="icon">📜</span> Ostatnie zapytania</h2>
+        {% if recent_logs %}
+        <ul class="log-list">
+            {% for log in recent_logs %}
+            <li class="log-item">
+                <div>
+                    <span class="log-type {{ log.request_type }}">{{ log.type_label }}</span>
+                    {% if not log.success %}
+                    <div class="log-error">{{ log.error_message[:50] }}...</div>
+                    {% endif %}
+                </div>
+                <div class="log-meta">
+                    <span class="log-tokens">{{ log.tokens_input }}+{{ log.tokens_output }}</span>
+                    <span class="log-time">{{ log.created_at.strftime('%H:%M:%S') }}</span>
+                </div>
+            </li>
+            {% endfor %}
+        </ul>
+        {% else %}
+        <div class="empty-state">
+            <p>Brak ostatnich zapytań</p>
+        </div>
+        {% endif %}
+    </div>
+</div>
+
+<!-- Daily History -->
+<div class="section">
+    <h2><span class="icon">📅</span> Historia dzienna (ostatnie 14 dni)</h2>
+    {% if daily_history %}
+    <table class="daily-table">
+        <thead>
+            <tr>
+                <th>Data</th>
+                <th>Zapytania</th>
+                <th>Chat</th>
+                <th>News</th>
+                <th>Tokeny</th>
+                <th>Koszt</th>
+                <th>Błędy</th>
+            </tr>
+        </thead>
+        <tbody>
+            {% for day in daily_history %}
+            <tr>
+                <td>{{ day.date.strftime('%d.%m.%Y') }}</td>
+                <td><strong>{{ day.total_requests }}</strong></td>
+                <td>{{ day.chat_requests }}</td>
+                <td>{{ day.news_evaluation_requests }}</td>
+                <td>{{ "{:,}".format(day.total_tokens) }}</td>
+                <td>
+                    <span class="cost-badge {% if day.total_cost_cents > 10 %}high{% elif day.total_cost_cents > 1 %}medium{% endif %}">
+                        ${{ "%.4f"|format(day.total_cost_cents / 100) }}
+                    </span>
+                </td>
+                <td>{% if day.error_count > 0 %}<span style="color: var(--error)">{{ day.error_count }}</span>{% else %}-{% endif %}</td>
+            </tr>
+            {% endfor %}
+        </tbody>
+    </table>
+    {% else %}
+    <div class="empty-state">
+        <div class="icon">📭</div>
+        <p>Brak historii - dane pojawią się po pierwszym użyciu AI</p>
+    </div>
+    {% endif %}
+</div>
+{% endblock %}