'use client'; import React from 'react'; import { motion } from 'framer-motion'; import { Typography, Row, Col, Card, Progress, Rate, Badge, Statistic } from 'antd'; import { BrainCircuitIcon, ThumbsUpIcon, ThumbsDownIcon, TrendingUpIcon, TargetIcon, FlaskConicalIcon, BarChart3Icon, Users2Icon, ClockIcon, SparklesIcon, AwardIcon, RefreshCwIcon } from 'lucide-react'; const { Title, Paragraph, Text } = Typography; // Animation variants const fadeInUp = { hidden: { opacity: 0, y: 30 }, visible: { opacity: 1, y: 0, transition: { duration: 0.6, ease: 'easeOut' } } }; const stagger = { visible: { transition: { staggerChildren: 0.15 } } }; const scaleOnHover = { hover: { scale: 1.02, transition: { duration: 0.2 } } }; // Mock feedback data const feedbackSamples = [ { id: 1, agent: 'AI-Agent-Alpha', context: 'API Documentation Update', rating: 5, feedback: 'Highly relevant and timely', timestamp: '2m ago' }, { id: 2, agent: 'AI-Agent-Beta', context: 'Code Review Context', rating: 4, feedback: 'Good context but could be more specific', timestamp: '5m ago' }, { id: 3, agent: 'Human-Operator', context: 'Performance Metrics', rating: 3, feedback: 'Adequate but needs improvement', timestamp: '12m ago' }, { id: 4, agent: 'AI-Agent-Gamma', context: 'User Feedback Analysis', rating: 5, feedback: 'Excellent contextual relevance', timestamp: '18m ago' }, ]; // Mock learning metrics const learningMetrics = [ { metric: 'Context Accuracy', current: 87.5, trend: '+2.3%', color: '#30d158' }, { metric: 'Response Relevance', current: 92.1, trend: '+1.8%', color: '#007aff' }, { metric: 'Agent Satisfaction', current: 4.2, trend: '+0.3', color: '#eab308', max: 5 }, { metric: 'Learning Rate', current: 78.9, trend: '+5.1%', color: '#f97316' }, ]; export default function COOEEShowcase() { return (
{/* Header */}
COOEE Feedback & Learning (RL Context SLURP) Reinforcement learning for context relevance tuning with agent feedback collection, role-based filtering, and continuous improvement through real-world performance data.
{/* Main Features Grid */}
Reinforcement Learning Engine Advanced RL algorithms for context relevance optimization with multi-agent feedback integration and adaptive learning rates.
{learningMetrics.map((metric, index) => (
{metric.metric}
Current Performance {metric.current}{metric.max ? `/${metric.max}` : '%'}
))}
Agent Feedback Collection Real-time feedback collection with upvote/downvote systems, detailed comments, and sentiment analysis for continuous improvement.
{feedbackSamples.map((sample) => (
{sample.timestamp}
{sample.context} "{sample.feedback}"
))}
{/* Learning Statistics */} Total Feedback} value={127834} valueStyle={{ color: '#a855f7', fontSize: '2rem', fontWeight: 'bold' }} prefix={} /> Learning Cycles} value={5847} valueStyle={{ color: '#30d158', fontSize: '2rem', fontWeight: 'bold' }} prefix={} /> Accuracy Gain} value={23.7} precision={1} suffix="%" valueStyle={{ color: '#eab308', fontSize: '2rem', fontWeight: 'bold' }} prefix={} /> Active Agents} value={47} valueStyle={{ color: '#f97316', fontSize: '2rem', fontWeight: 'bold' }} prefix={} /> {/* Continuous Learning Features */} Continuous Improvement Through Real-World Data
Adaptive Learning Dynamic algorithm adjustment based on real-world performance metrics with personalized learning paths for different agent types.
A/B Testing Framework Automated experimentation platform for testing context relevance improvements with statistical significance validation.
Performance Rewards Incentive-based learning system with performance-based rewards and penalty mechanisms for optimal context quality.
{/* Role-Based Learning */} Role-Based Context Filtering & Access Control
Multi-Agent Learning Personalized learning models for different agent roles and capabilities
Performance Analytics Detailed metrics tracking and analysis for continuous optimization
Real-Time Adaptation Immediate learning integration with live performance monitoring
Predictive Modeling Future context relevance prediction based on historical patterns
); }